- Calcium & Bone Metabolism
- Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm
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Sung Hye Kong, Jae-Won Lee, Byeong Uk Bae, Jin Kyeong Sung, Kyu Hwan Jung, Jung Hee Kim, Chan Soo Shin
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Endocrinol Metab. 2022;37(4):674-683. Published online August 5, 2022
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DOI: https://doi.org/10.3803/EnM.2022.1461
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 Abstract
 PDF Supplementary Material PubReader  ePub  Crossref - TDM
- Background
Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data.
Methods
This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models.
Results
Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women.
Conclusion
DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.
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Citations
Citations to this article as recorded by  - Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer
Min Wook Joo, Taehoon Ko, Min Seob Kim, Yong-Suk Lee, Seung Han Shin, Yang-Guk Chung, Hong Kwon Lee
Clinical Orthopaedics & Related Research.2023;[Epub] CrossRef - Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT
Sophia S. Goller, Jon F. Rischewski, Thomas Liebig, Jens Ricke, Sebastian Siller, Vanessa F. Schmidt, Robert Stahl, Julian Kulozik, Thomas Baum, Jan S. Kirschke, Sarah C. Foreman, Alexandra S. Gersing
Diagnostics.2023; 13(12): 2119. CrossRef - A Meaningful Journey to Predict Fractures with Deep Learning
Jeonghoon Ha
Endocrinology and Metabolism.2022; 37(4): 617. CrossRef - New Horizons: Artificial Intelligence Tools for Managing Osteoporosis
Hans Peter Dimai
The Journal of Clinical Endocrinology & Metabolism.2022;[Epub] CrossRef
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