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Abstract #3132

Feasibility of Fully Automatic Assessment of Cervical Canal Stenosis at MRI Using Deep Learning

YAYING ZHANG1, Xiaochen Feng2, Panpan Yang1, Yu Luo1, Kai Cao2, Yifan lv1, and Hailin Xu1
1Shanghai Fourth People's Hospital, Shanghai, China, 2Changhai Hospital, Shanghai, China

Synopsis

Keywords: Bone/Skeletal, Spinal Cord, MRI

Motivation: Artificial intelligence can provide a stable and objective cervical spine MRI diagnostic tool, thereby reducing doctors’ labor.

Goal(s): Evaluating the feasibility of fully automatic diagnosis of cervical spine MR imaging assisted by artificial intelligence.

Approach: 890 patients participated in this multicenter retrospective study, which integrated a convolutional neural network (CNN) and Transformer for automated segmentation and classification of cervical spine MRI images, constructing a deep learning model, and comparing its diagnostic capabilities with those of human readers.

Results: The model and readers achieved similar AUCs of 0.931 and 0.936 respectively. Correlation analysis show that the model, the readers had a moderate correlation.

Impact: The deep learning (DL) model could fully automatic and reliably assess cervical canal stenosis at MRI and provide a stable and objective cervical spine MRI diagnostic tool, thereby reducing doctors’ labor.

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