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DeepQCT: Predicting Fragility Fracture from High-Resolution Peripheral Quantitative CT Using Deep Learning
22 Pages Posted: 3 Apr 2024
More...Abstract
Background: Osteoporosis is prevalent in elderly women, which causes fragility fracture and hence increased mortality and morbidity. Predicting osteoporotic fracture risk is both clinically-beneficial and cost-effective. However, traditional tools using clinical factors and bone mineral density (BMD) fail to reflect bone microstructure. Here we aim to use high-resolution peripheral quantitative CT (HR-pQCT) images to construct deep-learning models which predict fragility fracture history in elderly Chinese women.
Methods: We used ChiVOS, a community-based national cohort of 2,664 Chinese elderly women. Demographic data, BMD, and HR-pQCT from 216 patients were used to construct three groups of models: BMD, pQCT-index, and DeepQCT. For DeepQCT, we used ResNet34 as classifier, and logistic regression for late fusion. Models were developed using 6-fold cross-validation in development set (90%, N=195), and tested in internal test set (10%, N=21). We applied unsupervised clustering on HR-pQCT indices to derive patient subgroups.
Findings: DeepQCT (best model AUC 0.86-0.94) was superior or similar to pQCT-index (best model AUC 0.8-0.93), which both outperformed BMD (best model AUC 0.54-0.78). Surprisingly, DeepQCT built from non-weight-bearing bones performed similarly to weight-bearing bones. Furthermore, two distinct patient groups were classified using HR-pQCT indices. The one with higher DeepQCT risk score showed lower volumetric BMD, bone more microarchitectural abnormalities, and had higher probability of osteoporosis and fragility fracture history.
Interpretation: DeepQCT scores and HR-pQCT-index permit early recognition of patients with high risk of fragility fracture. This established framework can be easily adapted for other diagnostic tasks using HR-pQCT scans, which promotes bone health management via digital medicine.
Funding: This research was supported by the National Natural Science Foundation of China (LC, 82100946; WX, 82270938), CAMS Innovation Fund for Medical Sciences (WX, 2021-I2M-1-002), National Key R&D Program of China (WX, 2021YFC2501700), National High Level Hospital Clinical Research Funding (WX, 2022-PUMCH-D-006), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (LC, 2023-PT320-10), and Young Elite Scientists Sponsorship Program by BAST (LC, No.BYESS2023171). Part of the study was supported by Merck Sharp & Dohme China, Hangzhou, China.
Declaration of Interest: All authors have no conflict of interest related to this publication.
Keywords: Deep learning, Prediction model, Fragility fracture, Osteoporosis, High-resolution peripheral quantitative CT (HR-pQCT), Volumetric bone mineral density, Bone microarchitecture
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