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The Taub Faculty of Computer Science Events and Talks

A Deep Learning Platform for Diagnosing ECG Tests
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Idan Levy (M.Sc. Thesis Seminar)
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Thursday, 15.02.2024, 11:00
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Zoom Lecture: Link and Taub 401
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Advisor: Prof. A. Schuster, Prof. Yael Yaniv
In clinical settings, a significant portion of ECG data is typically available in printed form, and the most convenient means of digitizing this information involves utilizing a mobile device. Despite notable progress in AI-based techniques for paper-based 12-lead ECG analysis, their adoption in clinical practice remains limited primarily due to challenges such as inadequate accuracy in clinical settings and a restricted ability to diagnose various cardiac conditions. Our objective was to tackle these challenges through BeatBox AI, an automated diagnostic platform for 12-lead ECGs images. This innovative platform is crafted as a continually self-optimizing solution, offering scalability and adaptability to diverse populations and various 12-lead ECG layouts.

BeatBox AI's diagnostic capabilities were assessed using 4,117 12-lead ECGs images gathered during a 10-month prospective clinical study, between March 29th, 2023 to January 10th, 2023. This study involved collaboration with 14 cardiologists from five distinct centers located in Israel, Japan, Italy, and Russia. Additionally, publicly accessible online platforms containing 12-lead ECG images and their corresponding diagnosis were utilized in the evaluation process. The study included participants who were random individuals who visited the centers and data uploaded to blogs.

The main outcomes in the comparative analysis between the automatic diagnosis and the cardiologists' assessments were that the platform demonstrated improvement across all operational dimensions.

The platform could initially diagnose 21 cardiac conditions with an average Matthews Correlation Coefficient (MCC) of 0.19. However, at the concluding evaluation stage, the MCC significantly improved to 0.57, and the platform successfully identified a total of 54 distinct cardiac conditions.

The data-driven strategy used in this study allows the platform to enhance its diagnostic accuracy continually. It enables the expansion of the range of diagnosable conditions beyond critical clinical thresholds, even with a small number of clinical samples. Additionally, the platform can adapt seamlessly to newly encountered ECG layouts, showcasing its flexibility and robust learning capabilities.