Diagnomatic

diagnomatic

Startups of The Year 2023 Winners: Europe

In radiology, traditional medical imaging techniques are failing patients and providers — the rise of false positives leads to unnecessary treatments and malpractice lawsuits. Radiologists need an innovative alternative to turn things around. Is artificial intelligence the solution they’ve been waiting for? Traditional Imaging Techniques Must Improve Radiologists use X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRIs) to see what’s inside someone’s body. Once they get their hands on the images, they review them for potential signs of injuries, anomalies, or diseases. For the most part, radiologists rely on their expertise to identify suspicious results. With all of their training, you’d expect them to nail it every time. In reality, the on average. Almost three in 10 people will receive a false positive or negative. diagnostic error rate is up to 26% People who receive false positives will undergo unnecessary treatments. Usually, they’ll be overexposed to radiation and contrast agents during the imaging process as professionals try to find out what’s wrong. As a result, patient outcomes worsen. Even when radiologists get it right, their techniques still take time. After all, they manually. You can probably understand why the process can take so long. have to review thousands of medical images As you might expect, medical imaging in radiology needs a fast, accurate solution to its problems. AI is one of the few modern technologies capable of addressing each one of its needs while improving interpretation and diagnostic accuracy. What is AI’s Role in Radiological Image Interpretation? AI’s first application involves image review. It uses extensive training and leverages large datasets to rapidly evaluate X-rays, CT, and MRI scans. It than manual search methods and simultaneously compare thousands of previous cases. can locate images faster Another application involves quantitative results. When you study medical images, you look at the size, shape, density, and texture of bodily structures, which takes time and may be inaccurate. AI rapidly analyzes them, enabling it to provide accurate measurements. You’re probably already aware of how AI excels at identifying trends. It does a fantastic job of interpretation and diagnosis because abnormality identification revolves around pattern recognition. It can pick up on hidden details you aren’t able to notice. Radiologists often consult with other medical professionals when they find themselves stuck on a case. A generative algorithm might not be the same as a doctor, but it can still provide tips and suggestions. AI-driven collaboration is slowly becoming more accepted. How Does AI Help With Medical Imaging? AI improves medical imaging interpretation and diagnostic accuracy in numerous ways. 1. It prevents human error. As you know, AI is immune to human error. Where you might make a cognitive, search, or recognition mistake, an algorithm remains accurate because it relies on data and logical processes. It won’t overlook a slight anomaly or work off a misguided hunch. 2. It constantly improves its approach. Deep learning and machine learning algorithms constantly improve. They use real-world data to become more accurate over time. The longer you use AI for medical imaging, the more sensitive and precise it will become. 3. It makes data-driven suggestions. AI can improve interpretation and diagnostic accuracy by giving you suggestions. For example, it can recommend re-reviewing a suspicious-looking area you initially overlooked. Its data-driven insights help prevent you from providing false positives or negatives. It reviews large databases immediately. If you’ve ever used AI, you know it’s fast. You can use it to reference thousands of images in a massive database almost immediately, saving hours of searching and reviewing. Your diagnosis becomes more accurate since the algorithm knows exactly which references to pull. Benefits of Using AI in Medical Imaging There are numerous benefits of using AI in medical imaging. For one, patients receive faster intervention. Since algorithms speed up virtually every step of the process, they get their diagnostic results earlier and can move forward with treatment sooner. AI is also more accurate than humans are. In one study, researchers used a neural network to review images of brain tumors. Their on average. It correctly identified tumors and distinguished between different types almost every time. model achieved 98.5% accuracy Patient monitoring is another intriguing benefit of using AI in medical imaging. Since algorithms can detect minuscule changes in size, shape, density, and texture, they can tell you exactly how and where your disease progresses. All these minor benefits add up to the most significant one — better patient outcomes. When radiologists use AI, the rate of false positives decreases, and your survival rate increases. Also, since early detection becomes possible, you get a better chance of getting help in time. AI improves patient outcomes and shortens review times. Radiologists probably feel like a weight is lifted off their shoulders. Since they get more done with less effort, they experience burnout less often and can prioritize especially tough cases. Is AI Better at Radiology Than Humans? Technically, AI can identify and diagnose diseases way better than humans. However, radiology is more than that — it also involves intervention. While an algorithm can suggest a treatment plan, it obviously can’t step in and provide one. Even though AI improves virtually every aspect of medical imaging in radiology, humans are still an essential part of the process. You’re capable of abstract reasoning, contextualizing, and out-of-the-box thinking — all things an algorithm can only dream of. As it stands, AI is an essential part of the process. This fact is especially true given radiologists in the United States than new professionals can replace them. Radiology professionals should leverage algorithms in support roles. are retiring way faster Is AI Transforming Radiology? Algorithms are overhauling how radiologists’ work flows. They’re changing how people organize, evaluate, and search for medical images. In 2021, were already on the market. This technology may become a staple in this field and permanently transform it. over 150 AI-powered radiology tools AI Might be the Future of Medical Imaging in Radiology Considering dozens of AI-powered radiological tools already exist, there’s no telling how radiology will evolve. Still, all indicators point to algorithms becoming a permanent part of radiologists’ workflows. In time, you may see this field transform into a modern wonder. Sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545608/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571277/ https://www.uperform.com/blog/ai-training-in-healthcare/ https://www.nature.com/articles/s41598-022-19465-1#Sec9 https://radiologybusiness.com/topics/healthcare-management/healthcare-staffing/mismatch-between-radiologist-shortages-rising-exam-volumes-growing-concern-medical-imaging https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537124/

How Does AI Improve Medical Imaging Interpretation and Diagnostic Accuracy in Radiology?

Using Diagnostic Observers to Decouple Logging and Observability Code

As Eventbrite neared the release of their rewritten platform in React.js, they knew observability on the frontend was a problem they needed to solve. If they were going to release a dynamic, JavaScript application, they would need visibility into their product. Their homegrown solution sent errors to Splunk, but there was no clarity around which errors were problematic. CHALLENGE: Eventbrite has now implemented Bugsnag in their development and release process which has given them peace of mind. They know any bugs will be detected before they impact their entire user base. Eventbrite has also become more agile since they can release features to a subset of users, monitor incoming errors, and decide when to rollout platform wide, knowing they will be alerted swiftly of issues. RESULTS: Growth Goals and an Innovating Product Highlighted the Need for Visibility into Errors Eventbrite is an online events platform that helps organizers build, manage, and grow their events. Their software provides end-to-end online event logistics, and enables organizers to get up and running smoothly and quickly, whether they are hosting intimate gatherings or complex, large scale events. As Eventbrite solidified its platform for event organizers, they sought to grow their user base of attendees to become the destination for finding things to do. They also decided to move away from being a monolithic platform and break up their product functionality. This would allow them to offer certain services to event organizers who may not need their entire platform. These growth goals would require thinking about quality in a new way, but Eventbrite’s Frontend Platform team knew their previous system would not support their growth. “Before Bugsnag, all of our JavaScript errors were put into Splunk, and it was just a pile of errors. It was very difficult to sort through or even get any visibility into. There was no grouping or ability to see what was actually impactful, and it was mixed in with all other error messages and logs as well,” says Senna Bala who manages the Eventbrite QA team. “I would spend time trying to force people to look at errors because no one would do it proactively on their own when it’s just so difficult to do.” Since they are responsible for helping engineering produce high quality software through testing and release support, this caused a lot of problems for her team. At the time, the most common way to discover bugs was when they were reported by customers experiencing difficulty with their platform. It’s important to remember that for each user who complains about a bug, there are countless others who also experienced it and abandoned a product. Ben Ilegbodu, Principal Frontend Engineer, explains that their team was even suspicious of errors negatively impacting some of their business metrics, like conversion, but there was no way to know with certainty because of the lack of visibility into errors. Gaining Peace of Mind Enables More Agile Development at Eventbrite In the end, Eventbrite chose Bugsnag to monitor all frontend applications because of its superior user experience, like the ease with which they are able to find specific errors using custom filters. Senna and Ben agree the best thing they have gained from using Bugsnag is the tremendous peace of mind it brings their teams. “Now I can be confident that everything is going into Bugsnag, and we can actually see the number of users that are hitting a bug, the number of different events. We get such granular data that I know I can go in there and find errors that are impactful.” - Senna Bala, QA Enginerring Manager The ability to know about errors before they negatively impact customers has helped them speed up their release process. “In a sense it’s kind of like a safety net. If there’s a problem, we know we’re going to catch it. We are trying to move in a more agile way, where we can just decide to put something out to 5% of users and see how it goes. We can slowly release it out and check Bugsnag to see if there are any issues users are experiencing. This helps us move quicker since we don’t need to make sure everything is perfect before showing it to anyone, and we can get feedback early in terms of user experience and errors,” says Ben. When issues do arise, they are fixing them much faster. According to Ben, having Bugsnag “is the difference between fixing bugs and not fixing them. It’s infinitely faster.” Improved Pre-production Testing and Smoother Releases Using Bugsnag has empowered the QA team to improve their processes and facilitate smoother releases. Running test flights have been significantly sped up, and when they do find errors, they are able to easily reproduce them using Bugsnag’s automatic breadcrumbs. “It is incredibly helpful for QA to be able to provide steps to reproduce because it means there’s a 90% chance it’ll get fixed. Without steps to reproduce, it’s more like a 5% chance because it requires so much work from the developer to dig into and find out why or how it’s happening," says Senna. “JavaScript errors in particular are so edge-casey and hard to reproduce, and we’ve really appreciated having breadcrumbs that actually lay out step by step what a user did to get into this weird state that caused an error.” -Senna Bala, QA Engineering Manager Eventbrite now uses custom filters to monitor releases which has made shipping new features a much smoother process. During a release, new code is only deployed to one server which lets them monitor and fix issues while they are isolated. Eventbrite configured a custom filter in Bugsnag to filter errors by server so they can focus on the release and remove other noise from their view. When a release goes out, the engineers use this filter and monitor incoming errors on the server. This has helped them improve their releases and prevent rollbacks. “We’ve been able to catch a few errors that we wouldn’t have found in our QA process otherwise and been able to resolve those quickly rather than waiting for a customer to report them after a release.” DOWNLOAD CUSTOMER STORY PDF Also published . here

Bugsnag's Diagnostics Tools Benefits Eventbrite

The development of artificial intelligence is in the ascendant, and the establishment of relevant standards for the approval and supervision of artificial intelligence products at home and abroad is in full swing. Since July 2017, the FDA has released the Digital Health Innovation Action Plan (DHIAP) 9 medical artificial intelligence-related products that have been approved successively since the instructions for safe and effective mobile medical treatment. 1. Kardia App: the first medical device accessory on Apple Watch On November 30, 2017, the Kardia Band under Alive Cor was approved by the FDA as a standard medical heart rate monitoring device and became the first medical accessory for Apple Watch. The KardiaBand watchband has a built-in artificial intelligence algorithm, which can accurately detect the user's abnormal heart condition and promptly remind the user. KardiaBand is compatible with the 1, 2, and 3 series of Apple Watch. KardiaBand also adds an extra electrode sensor, and uses the Apple Watch's heart rate sensor to monitor the user's heart rate in real time. The user needs to put his finger or thumb on the metal contact on the strap and wait for 30 seconds before KardiaBand passes. The Bluetooth connection sends the data to the Apple Watch, and the electrocardiogram is displayed on the screen. 2. Embrace: AI artifact for epilepsy monitoring and alerting In February 2018, the Embrace smart watch was officially approved by the FDA for monitoring a class of extremely dangerous epileptic seizures, and thus became the first smart watch approved by the FDA to be used in the field of neurology. According to data provided by the Centers for Disease Control and Prevention (CDC), approximately 3.4 million Americans are suffering from epilepsy. Embrace can monitor multiple epilepsy indicators at one time to prevent underreporting caused by the patient's self-report. Its patent-protected "Electrodermal Activity" (EDA) technology can quantitatively detect the physiology related to the activity of the sympathetic nervous system Change, record the time of the seizure. Empatica’s main product is the Embrace smart watch-a stylish wearable device containing medical-grade sensors and machine learning algorithms designed for epilepsy patients. The device uses electronic activity on the skin to track the wearer's seizures, and last week became the first smart watch to pass the FDA medical device certification. A clinical study compared the effectiveness of smart watches with the effectiveness of three neurologists, and the results showed that within 100% of the time, Embrace detected seizures in 135 patients in 272 days. 3. Wave clinical platform: the first doctor to issue an early warning within 6 hours The approval of the Wave clinical platform brings good news to the current limited supply of medical resources. Relying on artificial intelligence algorithms, the Wave clinical platform forms the basis of the hospital clinical monitoring system. All physiological data will be automatically collected, analyzed, and risk-stratified. Active monitoring and easy-to-read warning signs are enabled, and hospital workstations are integrated with real-time and historical data including patient medication history, age, physiological status, past medical history, family status, etc., to provide doctors and patients with risk-based clinical decision support. The Wave clinical platform is developed by Excel Medical. Through this algorithm, it can sense subtle changes in the body and send an alert to the doctor 6 hours before a fatal situation occurs. Since accidental deaths caused by preventable hospital injuries are the third most deadly killer in the United States, the emergence of the Wave clinical platform enables real-time monitoring and early prevention to reduce accidental injuries. In the next step, Excel Medical said it will build an AI wearable device to transfer the monitoring and early warning function from inside the hospital to outside the hospital. 4. Viz.ai: enabling stroke patients to receive precise treatment faster In February 2018, San Francisco-based health care company Viz.ai announced that it had obtained the FDA's marketing authorization for its stroke care applications. The application provides clinical decision support and uses deep learning algorithms to automatically analyze CT neural images to detect stroke-related indicators. The Viz.AI application is designed to analyze CT images of the brain and send text notifications to neurovascular experts when suspicious blockages of large blood vessels are found, while first-line providers conduct standard reviews of the images. Under normal circumstances, patients need to wait for the radiologist to check the CT images and notify the neurovascular specialist. Notifications can be sent to mobile devices, such as smartphones or tablets, but experts still need to view the images on the clinical workstation. The FDA's decision to approve the marketing authorization of Viz.AI also opened a breakthrough for the application of predictive equipment and regulatory classification, enabling subsequent computer-assisted medical software to pass the 510(k) certification process. 5. Cognoa: Use AI to screen for autism In February 2018, the California company Cognoa issued an announcement stating that one of its products for screening autism with the help of AI was recognized by the FDA as a Class II diagnostic medical device. Cognoa's AI algorithm has been trained and iterated through massive research data: there are research data from Denis Wall, a professor of pediatrics and psychiatry at Stanford, which contains an existing behavioral observation data set of approximately 10,000 children. By analyzing children’s natural behavior information and videos provided by parents, the company’s app uses machine learning to assess whether the child is growing at a normal rate and assess their behavioral health. In addition to being a diagnostic tool, the app also provides parents with personalized recommendations for intervention or treatment at home. 6. Guardian Connect: The accuracy rate of blood glucose AI prediction reaches 98.5% On March 12, 2018, medical technology company Medtronic announced that Guardian Connect, an independent intelligent CGM system, has been reviewed and approved by the FDA. The Guardian Connect system is suitable for diabetic patients aged 14-75. It can continuously measure the patient's glucose level and send the information to the smartphone every 5 minutes. Medtronic confirmed in a clinical study that the Guardian Connect system has an accuracy rate of 98.5% in predicting hypoglycemia symptoms. The system consists of a small sensor that, after the user inserts it into the abdomen, can measure the glucose content of the liquid under the skin, and attach it to a slim Bluetooth® transmitter to send the reading to an app on a smartphone. Users can continuously view glucose readings in the app and set customizable alarms. 7. IDx-DR, the first artificial intelligence medical device On April 11, 2018, the Food and Drug Administration (FDA), the US health regulatory agency, has approved the world's first artificial intelligence medical device IDx-DR, which can diagnose diseases without the help of a doctor. IDx-DR will be used to detect diabetic retinopathy, high blood sugar can cause retinal blood vessel damage and vision loss. Its built-in camera is used to take a picture of the patient’s eyes, and then the algorithm is used to evaluate the picture to determine whether the patient has signs of diabetic retinopathy. In a clinical trial, IDx-DR has an accurate recognition rate of 87.4% for mild diabetic retinopathy and 89.5% for mild diabetic retinopathy. The FDA announced the approval of IDx-DR on Wednesday, making IDx-DR the first artificial intelligence device approved by the FDA for independent testing without the need for doctors to interpret the results. This means that any doctor can use it, including primary care doctors who often deal with diabetic patients, and patients do not need to seek the help of an ophthalmologist on their own. 8. OsteoDetect, wrist fracture detection software In May 2018, the U.S. Food and Drug Administration (FDA) recently approved a new artificial intelligence tool called OsteoDetect that can help doctors diagnose hand bone fractures. The tool is a computer-aided detection and diagnosis software application that uses AI algorithms to help doctors determine wrist fractures faster than traditional diagnostic techniques. The FDA said the software can identify fractures (if they exist) and automatically mark them on the image, helping doctors quickly find and diagnose problems. The software is suitable for adult wrists and can identify distal radius fractures. Emergency rooms, primary care offices, and emergency care facilities can all benefit from this technology. It is important to note that this software will not replace healthcare professionals; instead, it is classified as an accessory tool. OsteoDetect research and development company demonstrated the usefulness of this AI software to diagnose carpal fractures through a study involving 1,000 X-ray images, and another study involving 24 providers who examined 200 cases. According to the FDA, these two studies show that OsteoDetect improves the detection and diagnosis of carpal fractures. 9.DreaMed Advisor Pro uses AI to achieve precise control of insulin dose In June 2018, DreamMed Diabetes, a developer of personalized diabetes management solutions, announced that the U.S. Food and Drug Administration (FDA) has approved the De Novo application for its company's product DreamMed Advisor Pro, an AI-based diabetes treatment Decision support software. DreaMed Diabetes was founded in 2014 and is headquartered in Petah Tikva, Israel. It is a medical company dedicated to the development of intelligent treatment solutions for type I and type II diabetes. DreaMed's mission is to simplify treatment, improve patient experience, and provide patients and healthcare providers with personalized diabetes solutions. We have developed the only diabetes solution technology approved by the regulatory authorities, through continuous glucose monitoring data, intelligent adjustment Insulin treatment process. DreaMed's diabetes medical team has developed a solution that introduces artificial intelligence into this process based on years of experience: the MD-Logic artificial pancreas algorithm program.

9 diagnostic aid AI products in 2021

Technological advancement has made the world a better place through easy and faster means in various aspects of life. In the medical sector, we do regular check-ups one way or the other with our physicians or doctors to maintain good health, right? We undergo different tests through several machines and equipment, in the process of living a better and healthier life. Now, when it comes to automobile the same wear and tear happens to the vehicles, such that regular diagnostic tests provides great help in detecting problems or malfunctions before they require costly repairs, or at the worst instance, breaking down at the wrong time. Though, the majority of vehicle owners have diagnostic tests, it has become a norm whereby vehicle owners only take their vehicle to the automobile shop when it has broken down or when it can no longer function properly. But there are many benefits in taking a foresight maintenance approach to your vehicle using standardized software and most importantly car diagnostic tools which include the advantage of detecting issues— practically revealing problems within a vehicle’s engine function, exhaust properties, and other major parts, long before they prompt a breakdown. Presently, there have been several codes or programs that have been enacted in the system software that is used for diagnosis which tells the mechanics or technicians exactly what is not in place more like a hint of the cause. Afterwards, they use their expertise to deduce the fundamental problem and probable solutions. Most standardized system software requires codes and programming to give the exact function it is expected to provide. The produce five long characters such that each of them gives different pieces of information about the vehicle's situation. DTC codes The first character, which is always a letter, suggests the particular control system that has an issue and gives logical meanings for it. The second character is often a digit, commonly 0 (it applies to all models of vehicles) or 1(it's unique to a specific vehicle model), and it indicates if the code is standardized or not. The third character is also mostly a digit, that ranges from 1 to 8. This particular character, discloses the subsystem that is at fault. Both the fourth and fifth characters are examined together as a two-digit number that are between 0 and 99, also known as the specific fault index.  All these DTC characters recognize the specific issue of the vehicle. Here are some system software that can assist in vehicle diagnosis: PROSCAN Source ProScan allows users to generate diagnostics reports for a vehicle using codes. It's in this system software that diagnostic trouble codes (DTC) come into the effect when paired with a scanner that plugs directly into the vehicle's port. Furthermore, when a car's check engine light comes off, a vehicle will save a code that recognizes the problem detected by the (DTC) that conforms to a specific vehicle faulty functioning. Also, the tool will provide a list of such diagnostic trouble codes saved with its explanation, so the user can detect the difficulty and where the issue is. Diagnostic trouble codes (DTCs) are also called engine vehicle fault codes and can be read. EOBD FACILE Source This software application displays vehicle sensors in real-time and saves them in a file. But just as vehicle configuration differs the way you see the car speed, engine speed and temperature, inlet air quantity, torque. The users of the software can bring about diagnostics of a vehicle using the faults of codes and discover their meaning, through which they can also check the engine pointer light and monitor specific manufacturer error codes. OBD2 AUTO DOCTOR Source This is a developed OBD2 car diagnostics tool. It is one of the best OBD software for the OS X platform. The Auto Doctor, being one of the advanced software, allows the user to communicate with their vehicles' onboard diagnostic system. Through this, the device whether a desktop computer, laptop, or mobile phone becomes a competent automotive scanner. All these software and DTC can be used on any automobile, even giving the ease of transferring from one vehicle to the other. Nonetheless, through the port located in the vehicle dashboard or steering wheel, depending on the manufacturer or model of the vehicle, the user would be able to get informed when an action is performed on the vehicle. OBD It would only seem right to mention , which stands for Worldwide Harmonized On-board diagnostics. WWH-OBD It is a standardized international version of the Onboard vehicle diagnostics system, and it is based on the ISO standard- ISO 27145, which was implemented by the United Nations as an aspect of Global Technical Regulations (GTR). This version is used to monitor vehicles data also as a means of communication between the vehicle's onboard diagnostic system software and superficial test equipment. Worldwide Harmonized In-board diagnostics grants access to additional data types, accordingly, OBD-II uses a two-byte diagnostic trouble code (DTC) to reveal when a fault came about which gives it the skill to give more detailed fault. In overview, WWH-OBD enhances the existing OBD II framework to provide even more diagnostic information to the user. PCMSCAN Source The is a diagnostic tool that operates through an OBD2 hardware interconnection with its software system that is based on codes and programs. PCMSCAN Being a general automobile diagnostic scanner and tool that maintains a broad diversity and also enables playback, viewing, creating charts, based on the diagnostics data in the vehicle's OBD2 diagnostic data position. When using this system software to diagnose a vehicle, there are basic steps that should be followed. Which includes; Locating your vehicle's PCM: You should know that as vehicle models vary to one another, so does the PCM location in most vehicles differs. Thus, to know the exact location of the PCM, you should check out your vehicle's manual. But in most cases, the PCM is found behind the engine compartment or beneath the dashboard on the passenger's side. Gaze intently at the wiring harness which is located underneath the driver's dashboard alongside the diagnostic port should be able to relate with the sensors of the system software throughout the vehicle. Connect your OBD-II scanner to the diagnostic data port. The port which might be visible in some vehicles is possible to be hidden in some. You insert your key into the vehicle's ignition column. Turn on the key and watch as the codes appear through the OBD2 (an improved On-board Diagnosis) device. Look at each code and note the ones that show the signs you're looking for. It is now left for you to determine whether the PCM problems can be addressed without a mechanic, such that all it needs is a restart and reconnect. In Conclusion People have made it a usual style of bringing their vehicles to the mechanic after a total breakdown or severe malfunction. But because diagnostic tests have been computerized on vehicles, it makes it easier and faster to detect problems in them and provide the necessary solutions to them. However possible, the diagnosis system software is capable of performing GPS tracking and resetting DTC when a fault code has been noticed.

Vehicle Diagnostic Software: How to Detect Car Trouble Before It Costs You

While creating the OBD2 reader application, we faced many questions, problems, and so on, so we will try to acquaint you with them in this article. As it turned out, you can conduct a dialogue with the car, and quite a productive one. However, in order to start communicating with the car, it is necessary to "establish contact", "ask the right question" and correctly understand the "answer" received from the car. Accordingly, the article will be aimed at clearly explaining the organization of the dialogue, as well as telling you what mistakes you may encounter on the way and how to deal with them. Connection selection Initially, it is necessary to clarify that an ELM327 adapter will be used to connect to the car. ELM327 is a microcircuit that allows converting the protocols used in the diagnostic tires of cars into the RS232 protocol, which we will use for data transmission. Due to the fact that data transmission via the RS232 protocol is carried out sequentially, the problem of low data transfer rate arises, which we will try to bypass in one of the following sections. There are several variations of the ELM327 adapter, which are classified according to the data transfer method - Bluetooth, WIFI, USB. Based on the fact that the development goal is a mobile device under the Android operating system, you can choose the two most suitable versions of ELM327, such as Bluetooth and WIFI. Since there is only one method of receiving and processing data, and they differ only in the options for connecting to the adapter, you can choose only one, organize a dialogue with it, and then add the rest of the connection options. ELM327 1.5 vs ELM327 2.1 One of the first tasks that you can face was the problem of choosing the adapter itself, in our case with Bluetooth support. It turns out that if you want to support all (at least most) cars, you must choose version v1.5 instead of v2.1. In fact, you need to clarify this several times when buying an adapter, because some sellers make mistakes in the version description, because the versions are not much different. In fact, v2.1 lacks support for the J1850 PWM and J1850 VPW protocols, which means that you will not be able to connect to cars that use these protocols. Connection Connection to the adapter takes place in several stages: Adapter connection (Bluetooth, WIFI) Sending initialization commands (initialization string) With the organization of the connection in general, everything is clear. The principle of operation is the same as for any Bluetooth / WIFI chat. In order to understand how to send an initialization string, you need to study what commands exist, as well as what functions they perform. AT Z [reset all] Reset adapter settings to factory state AT L1-0 Enable / Disable line feed characters AT E1-0 Echo on – off AT H1-0 Headers on – off AT AT0-1-2 Adaptive Timing Off — adaptive Timing Auto1 — adaptive Timing Auto2 AT ST FF Set the timeout to max value. AT D [set all to Default] Reset settings to the original user-configured state. AT DP [Describe the current Protocol] The scanner is able to independently determine the vehicle protocol to which it is connected. AT IB10 [set the ISO Baud rate to 10400] The command sets the baud rate for ISO 9141-2 and ISO 14230-4 10400 AT IB96 [ set the ISO Baud rate to 9600] The command sets the baud rate for ISO 9141-2 and ISO 14230-4 9600 for protocols 3,4,5. AT SP h [ Set Protocol h] Protocol selection command h, where h is: 0 – Automatic; 1 — SAE J1850 PWM (41.6 Kbaud); 2 — SAE J1850 VPW (10.4 Kbaud); 3 — ISO 9141-2 (5 baud init, 10.4 Kbaud); 4 — ISO 14230-4 KWP (5 baud init, 10.4 Kbaud); 5 — ISO 14230-4 KWP (fast init, 10.4 Kbaud); 6 — ISO 15765-4 CAN (11 bit ID, 500 Kbaud); 7 — ISO 15765-4 CAN (29 bit ID, 500 Kbaud); 8 — ISO 15765-4 CAN (11 bit ID, 250 Kbaud); 9 — ISO 15765-4 CAN (29 bit ID, 250 Kbaud); AT SP Ah [Set Protocol h with Auto] The command sets the h protocol by default, if the h connection fails, then the adapter starts automatic selection of the protocol. Based on the commands described above, we form an initialization string. It is desirable to enable the user to change the initialization commands because, in order to find the "key" for some cars, it is necessary to choose more specific adapter settings. In our case, the settings are used that are suitable for most standard protocols. It is also desirable to pay attention to the APSP0 command, so we set the automatic selection of the protocol by default, and this may take some time. Accordingly, if the user knows what protocol his car has, he can change 0 to his protocol number using the ability to change the connection protocol Reading diagnostic data To read diagnostic data, special PID's commands are used. PID’s (On-board diagnostics Parameter IDs) - codes that are used to query the values of indicators of certain vehicle sensors. Basic pids can be found on Wikipedia, which contains a complete set of basic commands that all cars should support. There are also sets of commands for certain brands and types of cars, these sets are available for a fee. In our case, the application is focused on basic car diagnostics, so we use a basic set of commands. It is also possible to receive the current data from the car, while the command for receiving data from the car will initially have 01, indicating that we want to receive real data. If we want to get the saved data of the car, then at the beginning of the command it is necessary to specify 02. For example, the command to get the current speed of the car is 010D, and to get the stored speed - 020D. If you look closely at the number of commands that are provided by open resources, then you can just notice the problem that I wrote about at the very beginning, that is, the problem of the low response speed of the adapter. Since the sending and receiving of commands goes sequentially, in order to receive the sensor readings at a given time, you need to wait for a response to all previous commands. Accordingly, if you request to receive all commands, then there is a high probability that the update of real data will be very slow. But even this problem can be solved by displaying only those commands that exist in the car. For example: 0100 – PIDs supported [01 — 20] 0120 – PIDs supported [21 — 40] 0140 – PIDs supported [41 — 60] 0160 – PIDs supported [61 — 80] 0180 – PIDs supported [81 – A0] 01A0 – PIDs supported [A1 — C0] I will demonstrate how to determine which sensors are present in the car using one of the pids. For example: 0100 \\ requestBB1E3211 \\ response from the car We translate the answer from the car into a binary number system translate to a binary number system Using the following table, we can determine which pids are supported by our car, starting from 01 to 20: Based on the resulting data, we can determine that our car supports the following pids: Now, instead of sending all 32 commands and waiting for a response to them, despite the fact that some of them may be missing, we will use only 15 commands. However, this is not the end of the so-called optimization. In order for the data to be updated even faster, I advise you to request only data about those sensors that are displayed on the screen. Although this limits some of the functionality of the application. For example, history recording. Reading and decoding vehicle errors Vehicle errors can also be different and there are specific commands for them. For example: 03 – To display stored error codes0A –To display permanent error codes. As with other commands, vehicle errors come in encoded form, respectively, as in other commands, they need to be decoded to get the necessary information. I will give an example of how error decoding works. The code: And now an explanation. Based on the received response, we can get an error code. To do this, we decode the received message using the following tables. First character: Second character: 3, 4, 5 characters are formed according to this table: Based on this, we can try to parse the following answer: 0001000000111110 Error code: P103E Epilogue At this stage, we figured out how to organize a dialogue with the adapter, send commands to it, receive and decrypt its responses. This is the bulk of the job, considering how much time it takes to study the documentation, but at the same time it is quite interesting. Outside of the discussion of this article, there are many problems associated with the visual interface, as well as many additional functions, such as adding new pids from a file, a standard and advanced way of connecting to the adapter, and plotting graphs. Previously published in SDD Technology

How To Build An OBD2 Reader For Car Diagnostics

Cómo construir un lector OBD2 para diagnóstico de automóviles

Every product or company goes through a similar set of steps on the path to success. For every stage of going to market, there’s a stereotypical set of problems you’ll encounter. In this post, we’ll survey these issues and the vectors by which you might debug them. Programming notes: this post is n in a series of indeterminate length on GTM topics mainly for startup people, mainly leadership, mainly coming from non-GTM backgrounds. There’s a list at the end. Stereotypical Problems Diminishing Returns Sales or marketing strategy and tactics lose efficacy or stop working altogether. You’ve hit saturation, you’re running out of the supply of leads in a particular channel (or in the world!) for what you do, there’s too much competition piling money into a particular tactic, etc. Leaky Funnel The absolute numbers at the top of the funnel are a bit disjoint from the customers you’re getting out of the bottom of the funnel. Your spend on lead generation tactics like AdWords have terrible, or unpredictable, returns. Market Saturation You’ve hit the limit of how much of any market you can acquire because of present demand, competition, product limitation, or some combination thereof. Competition There are sufficient competitors that even if you’re the dominant player, you’re hitting limits in market expansion without significantly ramping spend in the form of undercutting competitors. This is effectively the same as market saturation (for you!). Product Development Bottlenecks Your pace of development has become the limit to customer acquisition, account expansion, and competitive takeout. It may even be causing churn as customers leave for products that are moving faster. Friction You are hard to do business with. There’s no place to put in credit card info. Expansion requires a sales conversation. Changes to basic billing info require support tickets, you’re just mean to people, etc. This manifests as leakiness before purchase and churn after purchase. Whales This is an odd one and I can’t figure out where it goes, so here you are. You’ve had one or a few big customers who are steadily turning your company into an outsourced engineering team as they dominate all your product decisions and warp how you’re doing sales and marketing because you think they are the canonical customer. This is not so much a GTM problem as it is a fact (big customer!) that has a completely irrational impact on your behavior. The revenue that comes from this kind of customer can exact a terrible price. You’ve been warned. Value Proposition Limits Expected account expansion in Land and Expand GTM models does not occur, usually a function of saturation within an account or lagging product dev or failing theories of value Atypical Problems Too Many Leads You can’t process or service people who are interested in talking to you, or doing a free trial, or signing up, etc., fast enough Too Many Customers You can’t provide adequate on boarding or service for all the paying users of your product. Too Much Load You can’t provide sufficient performance or the experience of your product is degrading as more customers are onboard. Debugging Vectors Whole GTM Do you have the correct go to market? If you’ve been approaching the market from the perspective of doing direct outreach to senior buyers with custom, paid POCs and high-touch handholding from engineers or other staff — do you need to start focusing on making things easier and more self-service (and self-finding) from end to end? Whole Funnel Is the funnel not operating the way you expected? It might not be the funnel you thought it was. You might have thought you had separate self-service and sales-led funnels, but it turns out AEs are cannibalizing the self-service funnel because big name logos show up there. So the second or third stage of the self-service funnel is also the first or second stage of the sales-led funnel. By Funnel Stage Are enough people who sign up for a free trial actually using the product? Do enough people who use the product actually check out pricing? Do enough people who check out pricing actually become customers? The most normal way to deal with every perceived marketing issue is to isolate it to a funnel stage and try to optimize performance of that funnel stage. At the very top of the funnel, that’s increasing the absolute number of leads or impressions or whatever. At every following stage, it’s about increasing the conversion rate from the previous stage or to the next stage. In this view, a paying customer in any subscription or subject-to-renewal service, is in also in a funnel stage. It just happen s to be the one before renewal/expansion. By GTM Function Another way to look at things is from a functional perspective. Top of funnel problems are demand gen problems — and often also product marketing problems in the form of positioning or messaging or personas — and those specific strategies or tactics need attention. Mid funnel problems are product marketing or growth or product or sales problems. Bottom of the funnel problems are product marketing — in the form of packaging and pricing and sales enablement — or sales problems. In self-service, they’re usually product marketing and product or growth problems, since the product/app and website do all the selling. Debugging Tactics Throw Resources At It Put more money into lead gen. Put more SDRs on qualification and meeting setting. Put more reps in sales. Create more sales assets. Give deeper discounts. Change Tactics AdWords is working less well. Add Twitter Ads and LinkedIn Ads and Instagram Ads. Typically you’ll add additional tactics instead of wholesale decommissioning and replacing anything. Change Strategy Go from bottoms-up self-service to having sales people who email or call into specific accounts. Typically you won’t abandon your original strategy, but just expand it. Replace GTM On rare occasion, you will discover you were totally wrong about your entire approach to the market and will throw it out. A common version of this is people who thought they would have a channel business but discovered it was much better to go direct to customers. Or vice versa. Replace People Often people who are good at a particular strategy or tactic are not good at others. So if you change things, you will sometimes also have to change people. Many companies don’t even bother attempting to change anything. They just change people first and let the new people change strategy or tactics to new things. Flail This is what happens when there’s no model for GTM and no structured way of looking at the business. Anything or everything will be changed over and over again. Effectively, rolling dice. And the dice will keep getting rolled until something seems to work. TMTOWTDI There is more than one way to do it. Any mix of the above approaches may work in any given case without any predictable rhyme or reason. always It’s s complex system made up of founders, staff, investors, prospects, customers, competitors, the product, and the rate of learning and change of each of those things. There are egos and emotions and data and philosophies. The real question is how do you know if anything is working? If you don’t, it doesn’t really matter what you do because anything you do is indistinguishable from flailing. To Those With PMF Much Is Forgiven* ** The better the fit between your product, your market, and macro trends, the less anything else matters. You can completely fail at sales and marketing and customers will still break down your doors to throw money at you, because you’ve so deeply met a need right now. That’s a rocket ship. It is extremely rare. *Or to those with a warchest **Eventually time catches us all PMF Reality For most startup products, what you find is that they solve a niche problem for a niche audience. And for everyone else, it’s a nice-to-have but not a need-to-have. A certain person or org or team has experienced a particular kind of pain and/or has a particular ethos which makes them regard your product as a need-to-have. But that is totally on the specific experiences of that person or team. This is to plan for or demand gen against. path dependent impossible The economics will make this tough for a VC-backed business in the long run, unless you can charge a hell of a lot for it. Or, unless you can charge so that it’s worth paying for the nice-to-have. little Posts in this series (and templates) Marketing 101 for Engineers: A Functional Introduction Marketing 102 for Engineers: Roughing Out a Funnel Marketing 201 for Engineers: Messaging & Positioning Marketing 202 for Engineers: Launching Marketing 203 for Engineers: Sales Enablement Marketing 204 for Engineers: Generating Demand Marketing 301 for Engineers: Strategy & Planning Marketing 302 for Engineers: Hiring Marketers Marketing 303 for Engineers: Pricing Frame Marketing 401 for Engineers: Stages of GTM Marketing 402 for Engineers: Diagnostics & Troubleshooting Sales 101 for Engineers: A Functional Introduction PR 101 for Engineers Analyst Relations 101 for Engineers Basic Messaging Template [Google Doc] Basic Funnel Metrics Template [Google Sheet] Basic Launch Timeline Template [Google Doc] Basic Battlecard Template [Google Doc] Detailed Battlecard Template [Google Doc] Basic Marketing Calendar Template [Google Sheet] Basic Marketing Ladders Template [Google Sheet] Reading List and Resources Bob Tinker’s GTM Fit and Talk Slides Tomasz Tunguz Tren Griffin Startup Marketing Playbook First Round Capital: PR & Marketing Magazine First Round Capital: Sales Magazine Max Olson: Advantage Flywheels Go-To-Market Fit: The Missing Link for Enterprise Growth HubSpot: Top 10 Sales Methodologies Image credits [in order] Photo by on Jia Ye Unsplash

Marketing 402 for Engineers: Diagnostics & Troubleshooting

Post-mortem diagnostics & debugging comes into the picture when you want to figure out what went wrong with your Node.js application in production. In this chapter of Node.js at Scale we will take a look at , a core project which aims to help you to do post-mortem diagnostics & debugging. node-report The diagnostics module node-report The purpose of the module is to produce a human-readable diagnostics summary file. It is meant to be used in both development and production environments. The generated report includes: JavaScript and native stack traces, heap statistics, system information, resource usage, loaded libraries. Currently node-report supports Node.js v4, v6, and v7 on AIX, Linux, MacOS, SmartOS, and Windows. Adding it to your project just takes an npm install and require: npm install node-report --save & //index.js require('node-report') Once you add node-report to your application, it will automatically Report generation can also be triggered by sending a USR2 signal to the Node.js process. listen on unhandled exceptions and fatal error events, and will trigger a report generation. Use cases of node-report Diagnostics of exceptions For the sake of simplicity, imagine you have the following endpoint in one of your applications: function myListener(request, response) { switch (request.url) { case '/exception':  throw new Error('*** exception.js: uncaught exception thrown from function myListener()'); }} This code simply throws an exception once the route handler is called. To make sure we get the diagnostics information, we have to add the module to our application, as shown previously. /exception node-report require('node-report') function my_listener(request, response) { switch (request.url) { case '/exception':  throw new Error('*** exception.js: uncaught exception thrown from function my_listener()'); } } Let’s see what happens once the endpoint gets called! Our report just got written into a file: Writing Node.js report to file: node-report.20170506.100759.20988.001.txtNode.js report completed Once you open the file, you’ll get something like this: =================== Node Report =================== 

Event: exception, location: "OnUncaughtException" Filename: node-report.20170506.100759.20988.001.txt Dump event time: 2017/05/06 10:07:59 Module load time: 2017/05/06 10:07:53 Process ID: 20988 Command line: node demo/exception.js 

Node.js version: v6.10.0 (ares: 1.10.1-DEV, http_parser: 2.7.0, icu: 58.2, modules: 48, openssl: 1.0.2k,  uv: 1.9.1, v8: 5.1.281.93, zlib: 1.2.8) 

node-report version: 2.1.2 (built against Node.js v6.10.0, 64 bit)

OS version: Darwin 16.4.0 Darwin Kernel Version 16.4.0: Thu Dec 22 22:53:21 PST 2016; root:xnu-3789.41.3~3/RELEASE_X86_64 

Machine: Gergelys-MacBook-Pro.local x86_64 — it includes.. You can think of this part as a header for your diagnostics summary the main event why the report was created, how the Node.js application was started , ( _node demo/exception.js_ ) what Node.js version was used, the host operating system, and the version of itself. node-report The stack traces The next part of the report includes the captured stack traces, both for JavaScript and the native part: =================== JavaScript Stack Trace ===================

Server.myListener (/Users/gergelyke/Development/risingstack/node-report/demo/exception.js:19:5) emitTwo (events.js:106:13) Server.emit (events.js:191:7) HTTPParser.parserOnIncoming [as onIncoming] (_http_server.js:546:12) HTTPParser.parserOnHeadersComplete (_http_common.js:99:23) In the JavaScript part, you can see.. the stack trace (which function called which one with line numbers), and where the exception occurred. In the native part, you can see the same thing — just on a lower level, in the native code of Node.js =================== Native Stack Trace =================== 

 0: [pc=0x103c0bd50] nodereport::OnUncaughtException(v8::Isolate*) [/Users/gergelyke/Development/risingstack/node-report/api.node] 

 1: [pc=0x10057d1c2] v8::internal::Isolate::Throw(v8::internal::Object*, v8::internal::MessageLocation*) [/Users/gergelyke/.nvm/versions/node/v6.10.0/bin/node]

 2: [pc=0x100708691] v8::internal::Runtime_Throw(int, v8::internal::Object**, v8::internal::Isolate*) [/Users/gergelyke/.nvm/versions/node/v6.10.0/bin/node]

 3: [pc=0x3b67f8092a7]

 4: [pc=0x3b67f99ab41]

 5: [pc=0x3b67f921533] Heap and garbage collector metrics You can see in the heap metrics how each heap space performed during the creation of the report: new space, old space, code space, map space, large object space. These metrics include: memory size, committed memory size, capacity, used size, available size. To better understand how memory handling in Node.js works, check out the following articles: Hunting a Ghost — Finding a Memory Leak in Node.js Node.js Garbage Collection Explained =================== JavaScript Heap and GC =================== Heap space name: new_space Memory size: 2,097,152 bytes, committed memory: 2,097,152 bytes         

Capacity: 1,031,680 bytes, used: 530,736 bytes, available: 500,944 bytes Heap space name: old_space Memory size: 3,100,672 bytes, committed memory: 3,100,672 bytes 

Capacity: 2,494,136 bytes, used: 2,492,728 bytes, available: 1,408 bytes Total heap memory size: 8,425,472 bytes Total heap committed memory: 8,425,472 bytes Total used heap memory: 4,283,264 bytes Total available heap memory: 1,489,426,608 bytes Heap memory limit: 1,501,560,832 Resource usage The resource usage section includes metrics on.. CPU usage, the size of the resident set size, information on page faults, and the file system activity. =================== Resource usage =================== Process total resource usage: User mode CPU: 0.119704 secs Kernel mode CPU: 0.020466 secs Average CPU Consumption : 2.33617% Maximum resident set size: 21,965,570,048 bytes Page faults: 13 (I/O required) 5461 (no I/O required) Filesystem activity: 0 reads 3 writes The system information section includes.. environment variables, resource limits (like open files, CPU time or max memory size) and loaded libraries. Diagnostics of fatal errors The module can also help once you have a fatal error, like your application runs out of memory. node-report By default, you will get an error message something like this: <--- Last few GCs ---> 

  23249 ms: Mark-sweep 1380.3 (1420.7) -> 1380.3 (1435.7) MB, 695.6 / 0.0 ms [allocation failure] [scavenge might not succeed]. 

  24227 ms: Mark-sweep 1394.8 (1435.7) -> 1394.8 (1435.7) MB, 953.4 / 0.0 ms (+ 8.3 ms in 231 steps since start of marking, biggest step 1.2 ms) [allocation failure] [scavenge might not succeed]. On its own, this information is not that helpful. You don’t know the context, or what was the state of the application. With , it gets better. node-report First of all, in the generated post-mortem diagnostics summary you will have a more descriptive event: Event: Allocation failed - JavaScript heap out of memory, location: "MarkCompactCollector: semi-space copy, fallback in old gen" Secondly, you will get the native stack trace — that can help you to understand better why the allocation failed. Diagnostics of blocking operations Imagine you have the following loops which block your event loop. This is a performance nightmare. var list = [] for (let i = 0; i < 10000000000; i++) {  for (let j = 0; i < 1000; i++) {   list.push(new MyRecord())  }   for (let j=0; i < 1000; i++) {   list[j].id += 1   list[j].account += 2  }   for (let j = 0; i < 1000; i++) {   list.pop()  }} With you can request reports even when your process is busy, by sending the USR2 signal. Once you do that you will receive the stack trace, and you will see in a minute where your application spends time. node-report (Examples are taken for the node-report repository) The API of node-report Triggering report generation programmatically The creation of the report can also be triggered using the JavaScript API. This way your report will be saved in a file, just like when it was triggered automatically. const nodeReport = require('node-report') nodeReport.triggerReport() Getting the report as a string Using the JavaScript API, the report can also be retrieved as a string. const nodeReport = require('nodereport') const report = nodeReport.getReport() Using without automatic triggering If you don’t want to use automatic triggers you can opt-out of them by requiring the API itself — also, the file name can be specified as well: (like the fatal error or the uncaught exception) const nodeReport = require('node-report/api') nodeReport.triggerReport('name-of-the-report') Contribute If you feel like making Node.js even better, please consider joining the , where you can contribute to the module. Postmortem Diagnostics working group The Postmortem Diagnostics working group is dedicated to the support and improvement of postmortem debugging for Node.js. It seeks to elevate the role of postmortem debugging for Node, to assist in the development of techniques and tools, and to make techniques and tools known and available to Node.js users. In the next chapter of the Node.js at Scale series, we will discuss profiling Node.js Applications. If you have any questions, please let me know in the comments section below. Originally published at blog.risingstack.com on May 9, 2017. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising & sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Node.js Post-Mortem Diagnostics & Debugging

The pressure on healthcare systems to process vast amounts of patient data efficiently has never been greater. The growing need for real-time insights in clinical trials, patient management, and diagnostics is driving the demand for advanced data solutions. According to a 2023 report by IDC, the global healthcare data analytics market is projected to reach $45 billion by 2027. However, as data volumes continue to grow, traditional systems are struggling to keep up, leading to delays in drug development and clinical trials. One of the most critical challenges today is how to handle large datasets without compromising speed or accuracy. For pharmaceutical companies, timely data analysis can mean the difference between a successful trial and costly delays. This is where innovative solutions in data processing become essential for accelerating drug development and improving patient outcomes. Recognizing these challenges, Kirill Sergeev, a seasoned backend developer and machine learning engineer, has been developing cutting-edge systems that streamline the processing of medical data. By leveraging high-performance technologies, Sergeev has optimized data pipelines to enable companies to handle up to 100 terabytes of data daily with remarkable efficiency. "The key to managing large datasets efficiently lies in creating systems that are not just fast but also flexible enough to handle complex, dynamic data flows. In the medical field, where data needs to be processed quickly and securely, we cannot afford inefficiencies,"
-Kirill Sergeev One of the significant challenges in clinical data management is the lengthy process of deploying new machine learning models. Previously, deploying algorithms for clinical trials could take anywhere from two to three days, slowing down the ability to respond to new data. By redesigning data pipelines and integrating robust CI/CD processes, Sergeev has successfully reduced this time to just 1-2 hours. This streamlined process allows pharmaceutical companies to test and integrate new findings more quickly, ultimately accelerating the drug development timeline. "In any system dealing with high volumes of data, efficiency is key. It’s not just about handling data faster; it’s about ensuring that the results are accurate, actionable, and available immediately when needed," Sergeev adds. Speed and accuracy are essential in sectors where real-time insights can significantly impact patient outcomes. Sergeev’s work in optimizing data systems has cut response times for processing large volumes of medical data from 1.5 minutes to just 500 milliseconds. This level of performance is crucial for enabling healthcare providers to make timely decisions based on up-to-date information. By mostly adapting two types of approaches:  batch based and lambda-based, Sergeev has developed a hybrid architecture that ensures secure, scalable, and efficient data processing. This approach enables rapid data retrieval and real-time analysis, which is vital for managing clinical trials and patient records. While the healthcare sector benefits significantly from these advancements, Sergeev’s methodologies are also transforming other industries, such as fintech and e-commerce. By applying similar techniques, companies in these sectors have achieved substantial gains in efficiency. In a fintech project, for instance, Sergeev’s microservice architecture reduced transaction processing times by 35%, while also enhancing system security. In the e-commerce domain, his methods led to a 40% boost in operational efficiency by optimizing real-time inventory management systems. "The approach is universal," Sergeev notes. "Whether it’s healthcare, finance, or retail, the key is to build systems that are scalable and resilient to handle the increasing demands of modern data workloads." The future of healthcare lies in leveraging real-time data to drive faster and more accurate decision-making. As the sector continues to embrace data-driven practices, innovations like those developed by Sergeev will be crucial for enhancing patient care and speeding up drug development. "I think the future of data processing in healthcare lies in real-time insights that can inform quicker decisions. We’re only scratching the surface, but the potential to revolutionize patient care and drug development is immense," Sergeev concludes. By addressing the pressing needs of modern healthcare data management, Kirill Sergeev’s work is paving the way for a more efficient, data-driven approach that not only benefits the medical industry but also sets new standards for data processing in other high-load sectors. The pressure on healthcare systems to process vast amounts of patient data efficiently has never been greater. The growing need for real-time insights in clinical trials, patient management, and diagnostics is driving the demand for advanced data solutions. According to a 2023 report by IDC, the global healthcare data analytics market is projected to reach $45 billion by 2027. However, as data volumes continue to grow, traditional systems are struggling to keep up, leading to delays in drug development and clinical trials. One of the most critical challenges today is how to handle large datasets without compromising speed or accuracy. For pharmaceutical companies, timely data analysis can mean the difference between a successful trial and costly delays. This is where innovative solutions in data processing become essential for accelerating drug development and improving patient outcomes. Recognizing these challenges, Kirill Sergeev, a seasoned backend developer and machine learning engineer, has been developing cutting-edge systems that streamline the processing of medical data. By leveraging high-performance technologies, Sergeev has optimized data pipelines to enable companies to handle up to 100 terabytes of data daily with remarkable efficiency. "The key to managing large datasets efficiently lies in creating systems that are not just fast but also flexible enough to handle complex, dynamic data flows. In the medical field, where data needs to be processed quickly and securely, we cannot afford inefficiencies," -Kirill Sergeev "The key to managing large datasets efficiently lies in creating systems that are not just fast but also flexible enough to handle complex, dynamic data flows. In the medical field, where data needs to be processed quickly and securely, we cannot afford inefficiencies," "The key to managing large datasets efficiently lies in creating systems that are not just fast but also flexible enough to handle complex, dynamic data flows. In the medical field, where data needs to be processed quickly and securely, we cannot afford inefficiencies," -Kirill Sergeev One of the significant challenges in clinical data management is the lengthy process of deploying new machine learning models. Previously, deploying algorithms for clinical trials could take anywhere from two to three days, slowing down the ability to respond to new data. By redesigning data pipelines and integrating robust CI/CD processes, Sergeev has successfully reduced this time to just 1-2 hours. This streamlined process allows pharmaceutical companies to test and integrate new findings more quickly, ultimately accelerating the drug development timeline. "In any system dealing with high volumes of data, efficiency is key. It’s not just about handling data faster; it’s about ensuring that the results are accurate, actionable, and available immediately when needed," Sergeev adds. Speed and accuracy are essential in sectors where real-time insights can significantly impact patient outcomes. Sergeev’s work in optimizing data systems has cut response times for processing large volumes of medical data from 1.5 minutes to just 500 milliseconds. This level of performance is crucial for enabling healthcare providers to make timely decisions based on up-to-date information. By mostly adapting two types of approaches:  batch based and lambda-based, Sergeev has developed a hybrid architecture that ensures secure, scalable, and efficient data processing. This approach enables rapid data retrieval and real-time analysis, which is vital for managing clinical trials and patient records. While the healthcare sector benefits significantly from these advancements, Sergeev’s methodologies are also transforming other industries, such as fintech and e-commerce. By applying similar techniques, companies in these sectors have achieved substantial gains in efficiency. In a fintech project, for instance, Sergeev’s microservice architecture reduced transaction processing times by 35%, while also enhancing system security. In the e-commerce domain, his methods led to a 40% boost in operational efficiency by optimizing real-time inventory management systems. "The approach is universal," Sergeev notes. "Whether it’s healthcare, finance, or retail, the key is to build systems that are scalable and resilient to handle the increasing demands of modern data workloads." The future of healthcare lies in leveraging real-time data to drive faster and more accurate decision-making. As the sector continues to embrace data-driven practices, innovations like those developed by Sergeev will be crucial for enhancing patient care and speeding up drug development. "I think the future of data processing in healthcare lies in real-time insights that can inform quicker decisions. We’re only scratching the surface, but the potential to revolutionize patient care and drug development is immense," Sergeev concludes. By addressing the pressing needs of modern healthcare data management, Kirill Sergeev’s work is paving the way for a more efficient, data-driven approach that not only benefits the medical industry but also sets new standards for data processing in other high-load sectors.

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