Artificial Intelligence Grading of Rotator Cuff Tendinopathy Using Real-Time Elastography

  • Lo, Chung-Ming (PI)

Project: A - Government Institutionb - Ministry of Science and Technology

Project Details

Description

In American, the incidence of shoulder disorders had 4.5 million cases. The costs for the treatment of shoulder disorders were $7 billion per year.The sick leave caused by shoulder disorders achieves 13% loss of labor force. The shoulder tendons undergo degenerative changes with age, predisposing to tendinosis. The main shoulder disorder is rotator cuff tears of supraspinatus and consists 70% of all shoulder pains. Conventionally, MRI were used to examine tendon lesions, but in terms of availability, cost-effectiveness, ultrasound has advantages for the examination of tendon lesions. Through dynamic elastography, the elasticity of muscle tissue can be more effectively assessed. This project further proposes the artificial intelligence grading of tendon lesions by automatic quantification of elastography features. The project includes the quantification of dynamic elastography image features, automated assessment the quality of image by segmenting tendon and fat areas, Standardization of selecting representative images of time series. By combining a variety of quantitative features of elastic ultrasound image in the artificial intelligence classifier, a diagnostic model of tendon lesions will be established. An automatic, efficient and accurate diagnostic process will then be achieved.
StatusFinished
Effective start/end date8/1/187/1/19

Keywords

  • rotator cuff tendon lesions
  • elastography
  • artificial intelligence classification of lesions