AI-enabling Diagnostic Tools-AI-enabling Diagnostic Tools

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Video Summary

The GE Healthcare-sponsored session on AI in diagnostic tools, chaired by Foo Siong Ng, an electrophysiologist from Imperial College, featured several expert talks. Dr. Dawit Darbar, Chief of Cardiology at UIC, discussed AI-enabled ECG screening for atrial fibrillation (AF) and related cardiovascular conditions. He emphasized the potential of convolutional neural networks (CNNs) in detecting silent AF, asymptomatic LV dysfunction, and genetic forms of AF associated with titan mutations. Despite the promise, he noted challenges like the model's "black box" nature and false positives.<br /><br />Following Dr. Darbar, Dr. Stavros Boutantakis elaborated on mapping atrial fibrillation using AI, emphasizing the importance of understanding electrograms in identifying ablation sites. He highlighted the challenges of signal noise and the need for large datasets to develop effective AI models.<br /><br />Foo Siong Ng discussed predicting mortality timelines using AI models from ECGs. The models provide personalized survival curves by learning patterns from labeled data. They perform well across diverse populations, surpassing traditional risk assessments in predicting outcomes like heart failure and arrhythmias.<br /><br />Lastly, Michael Shihata focused on the future of the EP lab, emphasizing AI's role in improving workflow and integrating imaging data and patient management tools. He also envisioned advancements in virtual reality to enhance procedural accuracy and efficiency in electrophysiology.<br /><br />These presentations collectively underscored AI's transformative potential in cardiovascular diagnostics, though challenges like data quality, interpretability, and integration into clinical practice remain significant obstacles to widespread implementation.

Keywords

AI in healthcare

diagnostic tools

atrial fibrillation

convolutional neural networks

electrocardiogram

cardiovascular diagnostics

predictive modeling

electrophysiology

virtual reality

clinical integration