Meeting Banner
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here