Comprehensive Evaluation of Facet Joints Osteoarthritis Radiological Features on Lumbar CT: A Multitask Deep Learning Approach.

ABSTRACT:

Background

Accurately evaluating the radiological features of facet joint osteoarthritis (FJOA) may help to elucidate its relationship with pain. Multitask deep learning (DL) models have emerged as promising tools for this purpose.

Materials and methods

This retrospective study employed a dataset of 13 223 axial CT facet joint (FJ) patches cropped from 1 360 patients across two hospitals. At the image level, the dataset was categorized as training dataset (n = 7430), validation dataset (n = 2000), internal test dataset (n = 1890), and external test dataset (n = 1903). The radiologic features of FJOA were qualitatively assessed using a multitask DL model based on ResNet-18 according to the FJOA grading guidelines proposed by Weishaupt. Two batches

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12424607 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature