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Found 8 papers, 2 papers with code
BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors
Finally, we evaluate performance on out-of-distribution data collected at different hospitals than the training data, representing naturally-occurring distribution shifts that frequently degrade the performance of medical AI models.
CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks
Our results demonstrate that multiple purpose-built neural networks can function in sequence to accomplish the complex series of tasks required for automated analysis of real-world angiograms.
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations
We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram.
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes.
Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification
We develop a multi-task convolutional neural network (CNN) to classify multiple diagnoses from 12-lead electrocardiograms (ECGs) using a dataset comprised of over 40, 000 ECGs, with labels derived from cardiologist clinical interpretations.
Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery
We identified 36, 186 ECGs from the UCSF database that were 1) in normal sinus rhythm and 2) would enable training of specific models for estimation of cardiac structure or function or detection of disease.
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
We train and validate a semi-supervised, multi-task LSTM on 57, 675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0. 8451), high cholesterol (0. 7441), high blood pressure (0. 8086), and sleep apnea (0. 8298).
A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography
Automated cardiac image interpretation has the potential to transform clinical practice in multiple ways including enabling low-cost serial assessment of cardiac function in the primary care and rural setting.
