Abstract #4060

A pipeline combining deep learning and radiomics to automatically identify chronic lateral ankle instability from FS-PD MRI

Yibo Dan¹, Hongyue Tao², Chengxiu Zhang¹, Chenglong Wang¹, Yida Wang¹, Shuang Chen², and Guang Yang¹

¹Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, shanghai, China, ²Department of Radiology, Huashan Hospital, Fudan University, shanghai, China

The naked eye can only recognize the morphological changes of cartilage and subchondral bone on conventional MRI, but cannot recognize the subtle changes in their internal structure. The aim is to use radiomics to evaluate the cartilage and subchondral bone changes in patients with chronical ankle joint instability (CAI) on conventional MRI images¹. We built a pipeline to automatically identify CAI from FS-PD images. The pipeline automatically segmented cartilage regions and subchondral bone (5mm) regions, then used SVM based on radiomics features extracted from these regions for classification. In the test dataset, the proposed model achieved an AUC of 0.965.

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