Machine learning-assisted osteoporosis detection using radiographic canal bone ratio (CBR-7) and demographic features.

<h4>Purpose</h4>Osteoporosis remains underdiagnosed in aging populations due to its asymptomatic nature and limited access to diagnostic imaging. The Canal Bone Ratio measured 7 cm distal to the lesser trochanter (CBR-7), obtainable from standard full-length spinal radiographs, has emerged as a potential surrogate marker for bone quality. This study investigates the utility of CBR-7 in combination with machine learning (ML) models for opportunistic osteoporosis screening.<h4>Methods</h4>We retrospectively analyzed 162 patients (mean age, 73.3 years) who underwent DXA and standing full-length spinal radiography before lumbar spine surgery. Osteoporosis was defined as the lower of the lumbar spine and femoral neck T-scores being ≤ -2.5. Six regression models were evaluated using nested five-fold cross-validation to estimate the CBR-7 value corresponding to this diagnostic threshold. Separately, a logistic regression model incorporating CBR-7, age, sex, and BMI was evaluated using stratified five-fold cross-validation to classify DXA-defined osteoporosis.<h4>Results</h4>Of the 162 patients, 63 (38.9%) met the DXA-based definition of osteoporosis. CBR-7 was significantly higher in the osteoporosis group than in the non-osteoporosis group (0.517 ± 0.062 vs. 0.468 ± 0.058, p < 0.001). In the threshold-estimation analysis, Linear Regression showed the lowest cross-validated prediction error (MSE = 0.0033 ± 0.0006; R² = 0.181 ± 0.117) and estimated a CBR-7 value of 0.499 ± 0.001 corresponding to a Low T-score of -2.5. The logistic regression model combining CBR-7, age, sex, and BMI achieved an out-of-fold AUC of 0.778. At the Youden-derived probability threshold of 0.317, sensitivity was 82.5%, specificity was 57.6%, accuracy was 67.3%, precision was 55.3%, and the F1 score was 0.662.<h4>Conclusions</h4>CBR-7 was associated with DXA-defined osteoporosis and may serve as a preliminary radiographic marker for identifying patients who warrant further assessment with DXA. The simple linear model provided a stable estimate of the CBR-7 value corresponding to the diagnostic T-score threshold, whereas the classification model demonstrated moderate discrimination. External validation in independent cohorts, together with prospective multicenter validation, is required before clinical implementation.