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In addition, Yang et al [79] developed an ML-based predictive model using data from surveys on risk factors for OP, which is highly prospective for early screening and treating OP in the Hong Kong population. Similarly, ML models based on community health examinations and serum bone turnover markers have demonstrated a high area under the receiver operating characteristic curve, F1-scores, and accuracy [80,81]. These findings highlight the efficiency of ML in the diagnosis and management of OP.
J Med Internet Res 2026;28:e75965
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Yang et al [48] developed an MRI-based model with a sensitivity of 0.71, a specificity of 0.97, and an ROC AUC of 0.90 (95% CI 0.85-0.95).
A total of 27 models in the validation set provided complete 2×2 diagnostic tables, with a VETC-positive proportion of 41%.
J Med Internet Res 2026;28:e82839
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