Early semi-supervised sequence model for estimating cardiovascular and metabolic risk directly from consumer wearable sensor data (heart rate, step count, and activity level), rather than from clinical-grade ECG or imaging. A multi-task long short-term memory (LSTM) network is first pretrained using semi-supervised sequence learning or heuristic pretraining on unlabeled wearable time series, then fine-tuned to jointly predict four self-reported conditions: diabetes, high cholesterol, high blood pressure, and sleep apnea. Trained and validated on 57,675 person-weeks of data from participants in UCSF's Health eHeart study using the Cardiogram app on Fitbit, Apple Watch, or Android Wear devices, DeepHeart outperformed hand-engineered heart-rate-variability biomarkers from the medical literature, reaching AUROCs of 0.845 (diabetes), 0.744 (high cholesterol), 0.809 (high blood pressure), and 0.830 (sleep apnea). The paper was an early demonstration that population-scale, passively-collected wearable heart-rate data could support cardiometabolic risk screening without any dedicated clinical measurement.

Framework for training an echocardiography left-ventricle segmentation model purely by data-free knowledge distillation: a ConvLSTM-based student network learns to reproduce the masks produced by an EchoNet-Dynamic (DeepLabV3-ResNet50) teacher on entirely synthetic echo videos, with no real labeled data or even real videos required. Achieves state-of-the-art results identifying end-diastolic/end-systolic frames, reaching segmentation quality close to real-data training with substantially fewer weights; also introduces a human-annotation-free evaluation method using a large auxiliary model.

ECG foundation model built on the xLSTM (extended LSTM) architecture: a bidirectional stack of nine alternating scalar- and matrix-memory LSTM blocks that scales linearly with sequence length, unlike the quadratic cost of transformer-based ECG models. Pretrained with SimDINOv2, a coding-rate-regularized self-distillation (DINO) objective adapted from computer vision to ECG time series, on roughly 8 million recordings from CODE, INCART, and Chapman-Shaoxing-Ningbo. Introduced alongside BenchECG, a standardized 8-dataset/10-task benchmark, on which xECG achieves the best average rank of any publicly available ECG foundation model, with particular strength on long-context tasks (30-minute ambulatory arrhythmia classification, multi-hour sleep-apnea segmentation) where transformer-based models are computationally limited.