Add to Favourites
To login click here
Mount Sinai’s AI model HeartBEiT improves the accuracy and detail of ECG diagnostics, even in rare conditions with limited data. It interprets the ECG as language, outperforms traditional CNNs, and highlights specific ECG regions that cause heart disease. The research team reported that the new deep learning model created using HeartBEiT outperformed established ECG analysis methods in comparative tests. This makes ECG-based diagnosis significantly more viable, especially for rare conditions with low patient numbers and limited data availability.
