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Abstract #0804

Feasibility of deep learning–based automated rotator cuff tear measurements on shoulder MRI

Dana Lin1, Michael Schwier2, Bernhard Geiger2, Esther Raithel3, and Michael Recht1
1NYU Grossman School of Medicine, New York, NY, United States, 2Siemens Medical Solutions USA, Princeton, NJ, United States, 3Siemens Healthcare GmbH, Erlangen, Germany

Rotator cuff tear size is a critical determinant of patient prognosis and surgical outcomes. Radiologists routinely make rotator cuff measurements as part of their MRI interpretation, which can be tedious and subject to variation among readers. This lends itself to a potential application for deep learning to increase efficiency and decrease variability in this task. In this study, we developed a DL model to generate measurements for full-thickness supraspinatus tendon tears.

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