Welcome To CORONAROGRAPHY.AI

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.