AI-Powered Early Detection of Cardiovascular Diseases: A Global Health Priority

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Abstract

Timely identification of Cardiovascular diseases (CVDs) is critical in their prevention. However, conventional diagnostic techniques encounter challenges like late identification of the dangers and inadequate utilization of multiple risk factors. This work perfectly illustrates the possibilities of AI in improving the identification of CVD by integrating EHRs, imaging data, and data from wearable devices. An analysis involving a dataset of 50,000 patients developed and assessed AI models using three configurations: Electronic health record data, imaging data, and integrated data. This is also supported by the results of the integrated model, which had 92 percent accuracy with an AUC-ROC of 0.94, which added to the percent accuracy of single-source models. Multimodal data were used in the integrated model to assess the risk factors related to CVD, the changes in the patient’s physiology throughout the study, and the historical trends. It was also found that this type of diagnostics brings many clinical and societal benefits since it has better prediction accuracy, costs less, and leads to better patient outcomes.

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