Identifying Anterior Cruciate Ligament Injuries Through Automated Video Analysis of In-Game Motion Patterns.

ABSTRACT:

Background

Failure to diagnose anterior cruciate ligament (ACL) injury during a game can delay adequate treatment and increase the risk of further injuries. Artificial intelligence (AI) has the potential to be an accurate, cost-efficient, and readily available diagnostic tool for ACL injury in in-game situations.

Purpose

To develop an automated video analysis system that uses AI to identify biomechanical patterns associated with ACL injury and to evaluate whether the system can enhance the ability of orthopaedic and sports medicine specialists to identify ACL injuries on video.

Study design

Descriptive laboratory study.

Methods

A total of 91 ACL injury and 38 control movement scenes from online available match recordings were analyzed. The videos were processed

SUBMITTER: Schulc A 

PROVIDER: S-EPMC10935765 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature