2023
DOI: 10.3390/diagnostics13203254
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Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI

Kyu-Chong Lee,
Yongwon Cho,
Kyung-Sik Ahn
et al.

Abstract: This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of th… Show more

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