Feasibility of fully automatic assessment of cervical canal stenosis using MRI via deep learning.

ABSTRACT:

Background

Currently, there is no fully automated tool available for evaluating the degree of cervical spinal stenosis. The aim of this study was to develop and validate the use of artificial intelligence (AI) algorithms for the assessment of cervical spinal stenosis.

Methods

In this retrospective multi-center study, cervical spine magnetic resonance imaging (MRI) scans obtained from July 2020 to June 2023 were included. Studies of patients with spinal instrumentation or studies with suboptimal image quality were excluded. Sagittal T2-weighted images were used. The training data from the Fourth People's Hospital of Shanghai (Hos. 1) and Shanghai Changzheng Hospital (Hos. 2) were annotated by two musculoskeletal (MSK) radiologists following Kang's system as the standard refer

SUBMITTER: Feng X 

PROVIDER: S-EPMC12397646 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature