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Abstract #0244

Deep-learning Diagnosis of Supraspinatus Tendon Tears: Comparison of Multi-sequence Versus Single Sequence Input

Dana J. Lin, MD1, JinHyeong Park, PhD2, Michael Schwier, PhD2, Bernhard Geiger, PhD2, Esther Raithel, PhD3, and Michael P. Recht, MD1
1Department of Radiology, NYU School of Medicine, New York, NY, United States, 2Siemens Healthineers, Princeton, NJ, United States, 3Siemens Healthineers, Erlangen, Germany

Rotator cuff tears are a common cause of shoulder pain and typically diagnosed on shoulder MRI. Using 1,218 MR examinations performed at multiple field strengths and from multiple vendors, we developed a deep-learning (DL) model for the diagnosis of supraspinatus tendon tears on MRI using an ensemble of 3D ResNets combined via logistic regression to classify tears into no tear, partial tear, and full-thickness tear. We compared the effect of using multiple sequences as input versus a single sequence. Our results show that deep-learning diagnosis of supraspinatus tendon tears is feasible and that multi-sequence input improves model performance.

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