This study is a retrospective analysis that uses abdominal CT scans, which were originally taken for other medical reasons, to estimate bone age. By applying advanced deep learning methods, the investigators aim to develop a tool that can evaluate bone health and detect early signs of osteoporosis without requiring additional scans or radiation. This approach may help doctors better understand bone aging, improve screening for bone weakness, and provide patients with more personalized information about their bone health.

Inclusion Criteria:

• Adults aged over 18 years.
• Underwent routine noncontrast abdominal CT scans.
• CT scans fully included the proximal femur.
• Scans were performed for non-orthopedic clinical indications.
• Provided necessary demographic information (e.g., age, sex).

Exclusion Criteria:

• CT scans with poor image quality or severe artifacts that precluded accurate analysis.
• History of hip surgery or presence of internal fixation devices.
• Presence of bone tumors in the proximal femur.
• Severe hip deformity or prior fractures affecting the proximal femur.
• Pediatric patients or pregnant individuals (if applicable).

This retrospective study included about 3,000 adult participants (aged over 18 years) who underwent noncontrast abdominal CT scans that fully covered the proximal femur across multiple regions in China. Participants with poor image quality, prior hip surgery or internal fixation, bone tumors, severe hip deformities, or prior proximal femur fractures were excluded. All scans were performed for non-orthopedic indications. The study was approved by the Institutional Ethics Committee (approval number: 2024PHB388-001).

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