J Med Internet Res. 2024;26:e48535.

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

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제1저자 공성혜, 조원우, 주재걸, 김정희 · 교신저자 김정희

PMID 38995678 PMC11282387 DOI 10.2196/48535 IF 8.2 내분비대사내과

소속 (5)

Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Department of Neurosurgery, Seoul National University Boramae Hospital, Seoul, Republic of Korea.
Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea.

Abstract

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.

©Sung Hye Kong, Wonwoo Cho, Sung Bae Park, Jaegul Choo, Jung Hee Kim, Sang Wan Kim, Chan Soo Shin. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.07.2024.

Keywords

bonebonesdeep learningdevelopdevelopmentfracturefracture risk assessmentimagingmachine learningmusclemusclesmusculoskeletalpredictpredictionpredictiveprospective cohortscanningtomographyvalidatevalidation

Figures

**Figure 1** Flowchart of participant selection for this study on vertebral fracture prediction. CT: computed tomography; SNUBH: Seoul National University Bundang Hospital.

**Figure 2** Architecture of the convolutional neural network–recurrent neural network model for vertebral fracture prediction. CNN: convolutional neural network; FC, fully connected; RNN: recurrent neural network.

**Figure 3** Gradient-weighted class activation mapping heatmaps of image models using (A) bone-only and (B) bone+muscle images. A heatmap was generated by gradient-weighted class activation mapping, a technique used to visualize those parts of an input image that are important for a neural network's decision, especially in convolutional neural networks.

**Figure 4** Receiver operating characteristic curves comparing models for 5-year vertebral fracture prediction: model A includes age and sex, model B additionally includes BMI, and model C additionally includes a history of drinking, smoking, and possible secondary osteoporosis.

그림 출처: PubMed Central 공개 논문 (PMC11282387)

Conflict of interest statement

Conflicts of Interest: None declared.

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