Investigative radiology · 2026
Chest Radiograph-based Artificial Intelligence for Osteoporosis: Accuracy and Associations With Fracture and Mortality.
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Lee Ji Young, Kim Mi-Sook, Kim Jinhee, Hwang Eui Jin
Аннотация
OBJECTIVES: To evaluate an AI tool for opportunistic osteoporosis screening using chest radiographs (CXRs), focusing on its diagnostic accuracy against dual-energy x-ray absorptiometry (DXA) and associations with long-term risks of fracture and mortality.
MATERIALS AND METHODS: This retrospective, external validation study included a health checkup cohort with same-day CXR-DXA pairs and a chronic obstructive pulmonary disease (COPD) cohort with available CXRs. We evaluated a commercialized AI tool (InceptionV3 backbone, trained with 55,600 CXR-DXA pairs) designed to identify osteoporosis from a single frontal CXR. Diagnostic performance was assessed against DXA using the area under the receiver operating characteristic curve (AUC). Associations between AI results and subsequent fractures or all-cause mortality were investigated using multivariable Cox proportional-hazard regression, adjusted for sex, age, low body mass index, and COPD stage. Mediation analyses were conducted to examine whether these associations were mediated by a diagnosis of osteoporosis.
RESULTS: In total, 8618 (male-to-female ratio, 2882:5736; mean age, 58 y; median follow-up, 2868 d) and 4941 (male-to-female ratio, 4110:831; mean age, 69 y; median follow-up, 2656 d) patients were included in the health checkup and COPD cohorts, respectively. The AI achieved AUCs of 0.94 (95% CI: 0.93-0.95) and 0.81 (95% CI: 0.76-0.87) for osteoporosis identification in the health checkup and COPD cohorts, respectively. Higher AI-predicted osteoporosis probability was associated with subsequent fracture and mortality in both cohorts ( Ps <0.05). Mediation analyses showed that indirect effects of AI scores on fracture or mortality mediated through DXA-defined or clinically diagnosed osteoporosis were not significant ( P s>0.05), suggesting that AI may identify fracture risk in individuals not yet clinically diagnosed with osteoporosis.
CONCLUSIONS: AI could identify osteoporosis from chest radiographs, and AI results were associated with subsequent fracture and mortality.
MATERIALS AND METHODS: This retrospective, external validation study included a health checkup cohort with same-day CXR-DXA pairs and a chronic obstructive pulmonary disease (COPD) cohort with available CXRs. We evaluated a commercialized AI tool (InceptionV3 backbone, trained with 55,600 CXR-DXA pairs) designed to identify osteoporosis from a single frontal CXR. Diagnostic performance was assessed against DXA using the area under the receiver operating characteristic curve (AUC). Associations between AI results and subsequent fractures or all-cause mortality were investigated using multivariable Cox proportional-hazard regression, adjusted for sex, age, low body mass index, and COPD stage. Mediation analyses were conducted to examine whether these associations were mediated by a diagnosis of osteoporosis.
RESULTS: In total, 8618 (male-to-female ratio, 2882:5736; mean age, 58 y; median follow-up, 2868 d) and 4941 (male-to-female ratio, 4110:831; mean age, 69 y; median follow-up, 2656 d) patients were included in the health checkup and COPD cohorts, respectively. The AI achieved AUCs of 0.94 (95% CI: 0.93-0.95) and 0.81 (95% CI: 0.76-0.87) for osteoporosis identification in the health checkup and COPD cohorts, respectively. Higher AI-predicted osteoporosis probability was associated with subsequent fracture and mortality in both cohorts ( Ps <0.05). Mediation analyses showed that indirect effects of AI scores on fracture or mortality mediated through DXA-defined or clinically diagnosed osteoporosis were not significant ( P s>0.05), suggesting that AI may identify fracture risk in individuals not yet clinically diagnosed with osteoporosis.
CONCLUSIONS: AI could identify osteoporosis from chest radiographs, and AI results were associated with subsequent fracture and mortality.