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Research paper

0348 Utility of Large Language Models for Extracting Sleep Apnea Specific Symptoms and Major Adverse Cardiovascular Events from Electronic Health Records

Research paper by Nagharjun Mathi Mariappan and collaborators, published 2026 in SLEEP. Research area: Machine Learning in Healthcare.

Nagharjun Mathi Mariappan, Sajila Wickramaratne, Zachary Roberts, Li Zhou, Yutong Dong, Korey Kam, Thomas Tolbert, Andrew W. Varga, Indu A. Ayappa, David Rapoport, Ankit Parekh
Not specifiedView resource
Research paper

3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation

We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of U-net is applied to propagate the segmentation of a slice to the adjacent slice below it. In other words, the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice. 3D-consistency is hence explicitly enforced. The method is trained on a large database of 3078 cases from UK Biobank. It is then tested on 756 different cases from UK Biobank and three other sta

Zheng, Qiao, Delingette, Hervé, Duchateau, Nicolas, Ayache, Nicholas
arXiv.org perpetual, non-exclusive licenseView resource
Research paper

3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation

We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes. The segmentation is consistent in 3D across the slices such that it can be directly used for mesh generation. Two specific neural networks with multi-scale coarse-to-fine prediction structure are proposed to cope with the small training dataset and trained using an original loss function. The former segments a slice in the middle of the volume. Then the latter iteratively propagates the slice segmentations towards the base and the apex, in a spatially consistent way. We perform 5-fold cross-validation on the 15 cases from STACOM to

Zheng, Qiao, Delingette, Hervé, Duchateau, Nicolas, Ayache, Nicholas
arXiv.org perpetual, non-exclusive licenseView resource

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