Prediction and Detection of Glaucomatous Visual Field Progression Using Deep Learning on Macular Optical Coherence Tomography.

Prcis: A deep learning model trained on macular OCT imaging studies detected clinically significant functional glaucoma progression and was also able to predict future progression.

Objective: To use macular optical coherence tomography (OCT) imaging to predict the future and detect concurrent visual field progression, respectively, using deep learning.

Design: A retrospective cohort study.

Deep learning-based artificial intelligence model for identifying swallow types in esophageal high-resolution manometry.

Background: This study aimed to build and evaluate a deep learning, artificial intelligence (AI) model to automatically classify swallow types based on raw data from esophageal high-resolution manometry (HRM).

Methods: HRM studies on patients with no history of esophageal surgery were collected including 1,741 studies with 26,115 swallows labeled by swallow type (normal, hypercontractile, weak-fragmented, failed, and premature) by an expert interpreter per the Chicago Classification. The dataset was stratified and split into train/validation/test datasets for model development.