Bone Mineral Density and Intermuscular Fat Derived from Computed Tomography Images Using Artificial Intelligence Are Associated with Fracture Healing.

To employ artificial intelligence (AI) to automatically measure bone mineral density (BMD) and intramuscular fat in computed tomography (CT) images of patients with fractures and explore the association between these parameters and fracture healing. This retrospective study included patients who underwent baseline CT scans for rib fracture diagnosis and follow-up CT scans for fracture healing assessment at our hospital between 2012 and 2023. The volumetric BMD of the entire first lumbar vertebra (L1) and the paraspinal intramuscular fat area (PIFA) at the midsection of L1 in the baseline CT were extracted using AI. The primary outcomes, including callus formation, volume increase, and poor healing, and logistic regression were used to analyze the relationships between BMD and PIFA with primary outcomes. Overall, 297 fractures from 53 patients (24 males; mean age: 53.83 ± 10.86 years) were included in this study. In multivariate regression analysis, a 1 standard deviation (SD) decrease in BMD was identified as an independent prognostic factor for reduced callus formation (odds ratio [OR] = 0.70, 95% confidence interval [CI] = 0.50-0.97), diminished volume increase (OR = 0.70, 95% CI = 0.51-0.96), and elevated poor fracture healing at follow-up (OR = 2.08, 95% CI = 1.38-3.13). Similarly, a 1 SD increase in PIFA was an independent prognostic factor for reduced callus formation (OR = 0.24, 95% CI = 0.16-0.37), diminished volume increase (OR = 0.33, 95% CI = 0.23-0.49), and elevated poor fracture healing at follow-up (OR = 2.09, 95% CI = 1.50-2.93). Therefore, a model combining BMD, PIFA, and clinical characteristics significantly outperformed a model that included only clinical characteristics in predicting callus formation, volume increase, and poor fracture healing, with areas under the curve of 0.790, 0.749, and 0.701, respectively (all < 0.001). BMD and PIFA can be used as early predictors of fracture healing outcomes and can help clinicians select appropriate interventions to prevent poor healing.