Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm - Zhang, Zhewei; Ke, Chunhai; Zhang, Zhibin; Chen, Yujiong; Weng, Hangbin; Dong, Jieyang; Hao, Mingming; Liu, Botao; Zheng, Minzhe; Li, Jin; Ding, Shaohua; Dong, Yihong; et al. (Frontiers in Artificial Intelligence, 2024)
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 five years. With an accuracy rate of 96.93%, our model successfully predicted postoperative re-tears before the surgery, and achieved an accuracy rate of 79.55% when tested on a separate dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness, and to apply the strategies and approaches in other medical fields.