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

Abstract

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. 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 (<i>n</i> = 7430), validation dataset (<i>n</i> = 2000), internal test dataset (<i>n</i> = 1890), and external test dataset (<i>n</i> = 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 of images from each of the internal and external test datasets were used to test the change in readers' assessment accuracy with and without DL assistance, as measured using a paired <i>t</i> test. In this study, the accuracy of the model on the internal and external test datasets was 89.8% and 76.6% for joint space narrowing (JSN), 79.6% and 80.2% for osteophytes, 65.5% and 56% for hypertrophy, 88% and 89.6% for subchondral bone erosions, and 82.8% and 89.8% for subchondral cysts. The model's Gwet <i>κ</i> values reach 0.88. When junior readers used the DL model for assistance, the accuracy was significantly improved (<i>p</i> value ranged from < 0.001 to 0.043). A multitask DL model is a viable method for assessing the severity of radiological features in FJOA, offering support to readers during image evaluation.