Diagnostic Accuracy of Deep Learning for the Prediction of Osteoporosis Using Plain X-rays: A Systematic Review and Meta-Analysis

• Published in: Diagnostics (Basel) Vol. 14; no. 2; p. 207
• Main Authors: Yen, Tzu-Yun, Ho, Chan-Shien, Chen, Yueh-Peng, Pei, Yu-Cheng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2024
• Subjects: Accuracy; Artificial intelligence; Bone density; bone mineral density; Computer software industry; Computers; convolutional neural network; Datasets; Deep learning; Diagnosis; Fractures; International economic relations; Magnetic resonance imaging; Medical imaging equipment; Medical research; Medicine, Experimental; Neural networks; Older people; osteopenia; Osteoporosis; Statistical analysis; X-ray; X-rays; United States; Taiwan; osteopenia; bone mineral density; deep learning; X-ray; osteoporosis; convolutional neural network
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14020207

(1) Background: This meta-analysis assessed the diagnostic accuracy of deep learning model-based osteoporosis prediction using plain X-ray images. (2) Methods: We searched PubMed, Web of Science, SCOPUS, and Google Scholar from no set beginning date to 28 February 2023, for eligible studies that applied deep learning methods for diagnosing osteoporosis using X-ray images. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. The area under the receiver operating characteristic curve (AUROC) was used to quantify the predictive performance. Subgroup, meta-regression, and sensitivity analyses were performed to identify the potential sources of study heterogeneity. (3) Results: Six studies were included; the pooled AUROC, sensitivity, and specificity were 0.88 (95% confidence interval [CI] 0.85-0.91), 0.81 (95% CI 0.78-0.84), and 0.87 (95% CI 0.81-0.92), respectively, indicating good performance. Moderate heterogeneity was observed. Mega-regression and subgroup analyses were not performed due to the limited number of studies included. (4) Conclusion: Deep learning methods effectively extract bone density information from plain radiographs, highlighting their potential for opportunistic screening. Nevertheless, additional prospective multicenter studies involving diverse patient populations are required to confirm the applicability of this novel technique.