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Training Computers To Diagnose Human Injuries
December 19, 2019

Musculoskeletal injuries, such as small tendon tears, can be challenging for the human eye to detect on ultrasound images.

As demonstrated in a video of the research, Vincent Wang, the Kevin P. Granata faculty fellow and associate professor in biomedical engineering and mechanics, is using clinical ultrasound images to train computers to detect these injuries, with the goal of facilitating more accurate medical diagnoses, according to information provided by Virginia Tech.

Carrie Cheung, a graduate student in biomedical engineering, works with Prof. Wang to develop algorithms to identify ultrasound image features unique to injured tendons. Their hope is that these algorithms can be used in clinical settings where machines can identify injuries in real-time. These analyses may assist with clinical diagnosis and injury prevention.

“Our approach resembles that used for facial recognition in commonly used smartphone apps,” Prof. Wang explained.

This project is a collaboration with Bert Huang and Wu Feng in computer science at Virginia Tech for creation of algorithms and code and Albert Kozar in sports medicine at the Edward via College of Osteopathic Medicine to supply the tendon images.

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