Pic2Diagnosis: A Method for Diagnosis of Cardiovascular Diseases from the Printed ECG Pictures

A new geometric-stochastic multimodal deep learning framework has been developed to predict vulnerability to Sudden Unexpected Death in Epilepsy (SUDEP) and acute ischemic stroke, integrating various physiological signals such as EEG, ECG, and fMRI. This approach utilizes advanced mathematical models to enhance predictive accuracy and interpretability of biomarkers derived from complex brain dynamics.

A new deep learning model named EfficientECG has been developed to enhance the classification of electrocardiogram (ECG) data, aiming to reduce misdiagnosis rates and alleviate the workload of medical professionals. This model leverages the EfficientNet architecture to efficiently process high-frequency, long-sequence ECG data, providing a promising alternative to existing methods.

A novel three-stage training paradigm has been proposed to enhance the interpretability and trustworthiness of deep learning models in classifying electrocardiograms (ECGs) by transferring knowledge from multimodal clinical data into an ECG encoder. This approach utilizes a self-supervised, joint-embedding pre-training stage to enrich ECG representations with contextual clinical information, while only requiring the ECG signal during inference.