Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition

PLoS One. 2019 Feb 28;14(2):e0212741. doi: 10.1371/journal.pone.0212741. eCollection 2019.

Abstract

The lifetime prevalence of shoulder pain is nearly 70% and is mostly attributable to subacromial disorders. A rotator cuff tear is the most severe form of subacromial disorders, and most occur in the supraspinatus. For clinical examination, shoulder ultrasound is recommended to detect supraspinatus tears. In this study, a computer-aided tear classification (CTC) system was developed to identify supraspinatus tears in ultrasound examinations and reduce inter-operator variability. The observed cases included 89 ultrasound images of supraspinatus tendinopathy and 102 of supraspinatus tear from 136 patients. For each case, intensity and texture features were extracted from the entire lesion and combined in a binary logistic regression classifier for lesion classification. The proposed CTC system achieved an accuracy rate of 92% (176/191) and an area under receiver operating characteristic curve (Az) of 0.9694. Based on its diagnostic performance, the CTC system has promise for clinical use.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Feasibility Studies
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted*
  • Male
  • Middle Aged
  • Pattern Recognition, Automated
  • ROC Curve
  • Rotator Cuff / diagnostic imaging*
  • Rotator Cuff Injuries / complications
  • Rotator Cuff Injuries / diagnosis*
  • Shoulder Pain / etiology*
  • Ultrasonography

Grant support

The authors would like to thank the Ministry of Science and Technology (MOST 107-2221-E-004-013), New Taipei City Hospital (NTCH104-001) of Taiwan, the Republic of China, for financially supporting this research.