Echovision AI-Predictive Deep Learning for Cardiovascular Diagnostics

Cardiovascular diseases are still on the rise as a cause of morbidity and mortality requiring efficient and effective diagnostic tests. Routine objective methods include the quantification of fractional shortening and end-diastolic dimensions obtained from the standard two-dimensional trans thoracic echo cardiogram, which requires the presence of a cardiologist, increases the time to reach a diagnosis, and at times may give inconclusive results. Our Echovision AI is a predictive deep learning framework that emulates the skill and diagnostic proficiency of a clinician. These include the capacities to take as inputs echocardiographic video, analyze the present conditions of the heart and forecast the tendencies in diseases. The framework integrates multiple state-of-the-art algorithms: For accurate view classification, an AttentionAugmented Convolutional Neural Network (AA-CNN) is used while for comprehensive disease detection using labeled and unlabeled data, Attention-Guided Generative Adversarial Network (AG-GAN) has been proposed. Along with it for the detailed segmentation of cardiac structures at high resolution, U-net++ is used. A new Temporal Prediction module based on Transformers or BiLSTMs processes sequential data to predict disease progression and apply early intervention measures. The system is also developed with the Natural Language Processing (NLP) system in order to produce interpretive reports for clinical decision making. According to it the overall view classification accuracy obtained is more than 94 %, disease detection is about 92 % and temporal prognosis predictions are about 88 %. Scalable, the model uses federated learning, where patient data remains with local institutions and results are shared across patient populations. When predictive results are added to the mix, Echovision AI becomes a quintessential link between diagnostics imaging and decision support thereby arming clinicians with an effective weapon against cardiovascular diseases. This framework can thus help realize a revolutionary technique in cardiac diagnosis, where diagnostic inconsistency is eliminated, real time decision making made possible and patient care improved through timely intervention.