Early Detection of Baseball Elbow Injuries with AI Technology
A new AI-powered system developed by researchers at Kyoto Prefectural University of Medicine and the University of Hyogo can accurately detect early signs of elbow injuries in baseball pitchers. This system analyzes ultrasound images to identify abnormalities, potentially leading to earlier diagnosis and treatment, especially for children.
The system focuses on identifying osteochondritis dissecans (OCD), a condition caused by bone collisions on the outside of the elbow, leading to cartilage damage. This condition often affects children between elementary and junior high school, making early detection crucial.
Currently, OCD detection is challenging due to the lack of pain or noticeable symptoms in the early stages. By the time abnormalities are identified, corrective surgery may be too late. Ultrasound examinations reveal that 1-3% of baseball-playing children suffer from OCD, highlighting the need for improved detection methods.
The AI system analyzes ultrasound images of the capitulum humeri, a bone tip on the elbow. By comparing these images with a database of healthy and injured elbows, the system can detect OCD with 97% accuracy.
Researchers aim to commercialize this technology and expand its application to other baseball elbow injuries. They envision a future where individuals can easily monitor their elbow health, similar to measuring blood pressure.
This development holds significant promise for young athletes, potentially preventing serious injuries and allowing them to enjoy the sport without pain. The lead researcher, Yoshikazu Kida, who himself experienced baseball elbow problems, hopes this technology will empower children to play with full confidence and avoid the pain he endured.