Opportunistic screening for osteoporosis using chest X-rays and deep learning: A systematic review and meta-analysis

PURPOSE: To evaluate the diagnostic performance of deep learning (DL) algorithms applied to chest radiographs (CXR) for detecting osteoporosis and assess their potential for clinical implementation. METHODS: A systematic review and meta-analysis was conducted including studies that validated convolutional neural network (CNN)-based DL models to detect osteoporosis from CXRs. Exclusion criteria included studies using imaging other than CXR and non-DL models. Quantitative synthesis included pooled sensitivity, specificity, and construction of a summary receiver operating characteristic (SROC) curve. Model quality was evaluated using the APPRAISE-AI framework. RESULTS: > 98%) and driven primarily by country of origin. The APPRAISE-AI assessment indicated nine studies were high quality, supporting their use as a clinical decision support tool, while the others were of moderate quality. CONCLUSION: DL models applied to CXR show promising diagnostic performance for opportunistic osteoporosis screening. However, substantial heterogeneity in internal validation and the limited number of externally validated studies underscore the need for further research to improve generalizability and support real-world clinical implementation.