OUR APP FEATURES

The system helps to identify an acute condition at an early stage by the type of occlusion, subocclusion, significant stenosis of the coronary arteries, which will serve as an early reason to contact a specialist. Immediate results are a huge advantage over other studies that require results to be expected within 24 to 48 hours.

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Calculations

The Coronarography.AI service is based on artificial intelligence technologies using deep neural networks.

Neural network training

We have trained machine learning algorithms on these hundreds of unique coronary angiograms, and training continues with more data.

Results

The neural network, with high accuracy, determines the pathology of the main coronary arteries, myocardial ischemia, the likelihood of revascularization.

Data input

You only need to select risk factors and upload an ECG image in a supported format: JPEG, PNG.

View use case

APPS SCREENSHOT

You can dynamically watch the results. For example, to predict what will happen if a patient or you develops obesity, stops playing sports, deregulation of blood pressure begins, or the patient begins to break the diet, smoke, consume a lot of alcohol. It is possible to predict the results with the expected appearance of diabetes mellitus, hypertension, aging.

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The application was developed by the author of the technique, cardiologist Timur Pulatovich Abdualimov.

FAQ

A new technique in the detection of coronary heart disease. With the help of neural network analysis, a model for predicting coronary heart disease was created, which reveals myocardial ischemia, pathology of the main coronary arteries.The innovative approach lies in the use of neural networks for the diagnosis of coronary artery pathology based on risk factors and ECG images. At the output of the neural network, we get the presence or absence of pathology on each main coronary artery (trunk of the left coronary artery, anterior interventricular artery, circumflex artery, right coronary artery), the likelihood of atherosclerosis, the need to perform invasive coronary angiography with possible revascularization at the moment.
The advantage of using our method is simplicity (it requires filling out a questionnaire and uploading an ECG image), speed (calculation time is less than a second), non-invasiveness of the technique while maintaining high accuracy. Our technique can be used remotely and will allow performing non-invasive predictive AI coronary angiography in places where there is no possibility of specialized medical care (removed ECG tape is required). It also does not require extensive computer resources and expensive equipment, which makes it easier for a specialist to make a correct diagnosis. The system helps to identify an acute condition at an early stage by the type of occlusion, subocclusion, significant stenosis of the coronary arteries, which will serve as an early reason to contact a specialist. Immediate results are a huge advantage over other studies that require results to be expected within 24 to 48 hours. Our technique allows us to approach the screening of coronary artery pathology at a new level, the study can be used massively due to the lack of "invasiveness", the introduction of contrast studies, myocardial overload. Our program will allow you to independently suspect and identify the presence of pathology in a patient. If a “positive result” is obtained, the patient can immediately make an appointment with a doctor, having an increased risk category. In our work, we tried as much as possible to bring the work of AI closer to the work of a doctor.
The neural network trained by us predicts damage to the main coronary arteries with a sensitivity of 63%, a specificity of 88%, and AUC of 0.74. On the test sample, the neural network works more efficiently than the average cardiologists and, what is especially important, allows the doctor to be directed to perform invasive examination methods in cases where there is not enough input data for this decision. One in five experts was able to get close to the accuracy of the trained neural network model. The efficiency of detecting transient myocardial ischemia in a test sample is higher for a trained neural network compared to classical diagnostic methods, such as daily ECG monitoring, treadmill test. On an extremely large sample of 1500000 observations, a high AUC score was obtained.
Any ECG image on 1 page, it is desirable that the recording speed be 25 mm/sec.
The neural network has identified one of the positive parameters, you need to contact a specialized specialist for clarification. Additional testing may be required.

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If you have any questions please contact us

Email Address

atp@coronarography.ai