Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm.

ABSTRACT: The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our mode

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10938848 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature