Abstract #4048

Assessment of the potential of a Deep Learning Knee Segmentation and Anomaly Detection Tool in the clinical routine

Laura Carretero¹, Pablo García-Polo¹, Suryanarayanan Kaushik ², Maggie Fung², Bruno Astuto^3,4, Rutwik Shah^3,4, Pablo F Damasceno^3,4, Valentina Pedoia^3,4, Sharmila Majumdar^3,4, and Mario Padrón⁵

¹Global Research Organization, GE Healthcare, Madrid, Spain, ²GE Healthcare, Waukesha, WI, United States, ³Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, ⁴Center for Digital Health Innovation, UCSF, San Francisco, CA, United States, ⁵Department of Radiology, Clínica Cemtro, Madrid, Spain

This study evaluates the clinical accuracy of a deep learning (DL)-based tool to segment articular cartilage and menisci on 50 knee MRI exams; detect lesions and stage its severity. An experienced MSK radiologist assessed independently the images for the presence of any lesions on the different compartments and checked the accuracy of its segmentation, resulting in no disagreement with the segmentation output in 92.8% of the compartments and correspondence in the detection of lesions in 75.94% of them. The shown results assessed the clinical potential of this tool and present a step forward into structured MSK imaging reports.

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