TY  - JOUR
AU  - Kong, Sung Hye
AU  - Cho, Wonwoo
AU  - Park, Sung Bae
AU  - Choo, Jaegul
AU  - Kim, Jung Hee
AU  - Kim, Sang Wan
AU  - Shin, Chan Soo
PY  - 2024
DA  - 2024/7/12
TI  - A Computed Tomography–Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study
JO  - J Med Internet Res
SP  - e48535
VL  - 26
KW  - fracture
KW  - bone
KW  - bones
KW  - muscle
KW  - muscles
KW  - musculoskeletal
KW  - prediction
KW  - deep learning
KW  - prospective cohort
KW  - fracture risk assessment
KW  - predict
KW  - predictive
KW  - machine learning
KW  - develop
KW  - development
KW  - validate
KW  - validation
KW  - imaging
KW  - tomography
KW  - scanning
AB  - Background: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. Objective: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. Methods: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network–recurrent neural network model from images of the vertebral bone and paravertebral muscles. Results: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). Conclusions: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e48535
UR  - https://doi.org/10.2196/48535
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38995678
DO  - 10.2196/48535
ID  - info:doi/10.2196/48535
ER  -