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Enhanced Coronary Artery Disease Diagnosis and Progression Tracking using Artificial Intelligence

  • Unique Paper ID: 167054
  • Volume: 11
  • Issue: 3
  • PageNo: 210-217
  • Abstract:
  • This research focuses on the use of Artificial Intelligence (AI) powered techniques for enhanced coronary artery disease diagnosis (CAD) and progression tracking. According to World Health Organisation (2021), CAD is a major contributor to morbidity and mortality globally. Effective treatment and management of CAD depend on an early diagnosis and ongoing monitoring. Unfortunately, there are still issues with existing diagnostic techniques that make it difficult to identify CAD in a timely and reliable manner, and monitoring the disease's evolution over time is still difficult. In this research we developed an AI-based model that automate CAD diagnosis and progression tracking using image analysis and evalution of key biomarkers. The solution allows user to capture 11 patient attributes and implements the deep neural network (DNN) algorithm to classify the patient as having CAD or not. The DNN based model was trained using 80% of the 70 000 clinical instances from the CAD dataset and 20% of the records were used for testing. The second CAD diagnosis component in the solution evaluates three biomarkers (c-reactive protein, troponin and homocysteine) and classify the patient results as either CAD positive or negative. This solution also does image analysis of coronary artery images to check for plaque using a convolutional neural network (CNN) based model. This component of the solution allows for CAD progression tracking in a patient. The system achieved an overall accuracy of 95.1%, specificity of 96.3% and sensitivity of 95.4% which shows high performance when compared to similar CAD diagnosis models. AI algorithms used in this research have shown superior accuracy in diagnosing CAD, surpassing existing diagnostic methods. This enhancement is crucial for early detection and intervention, ultimately reducing the risk of severe complications and mortality associated with CAD. Key recommendations include adoption of AI technologies in clinical workflow by healthcare centers, use of wearable devices for real time monitoring of patients and advanced data integration.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 3
  • PageNo: 210-217

Enhanced Coronary Artery Disease Diagnosis and Progression Tracking using Artificial Intelligence

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