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<i>Summary</i> <p>Low bone mass (LBM), which can lead to osteoporosis, is often undetected and increases the risk of bone fractures. This study presents OsPenScreen, a deep learning model that can identify low bone mass early using standard chest X-rays (CXRs). By detecting low bone mass sooner, this tool helps prevent the disease progression to osteoporosis, potentially reducing health complications and treatment costs. OsPenScreen was validated across four external datasets and consistently performed well, showing its potential as a reliable, cost-effective solution for opportunistic early screening in CXR.</p> Purpose <p>Low bone mass, an often-undiagnosed precursor to osteoporosis, significantly increases fracture risk and poses a substantial public health challenge. This study aimed to develop and validate a deep learning model, OsPenScreen, for the opportunistic detection of low bone mass using routine chest X-rays (CXRs).</p> Methods <p>OsPenScreen, a convolutional neural network-based model, was trained on 77,812 paired CXR and dual-energy X-ray absorptiometry (DXA) datasets using knowledge distillation techniques. Validation was performed across four independent datasets (5,935 images) from diverse institutions. The model’s performance was assessed using area under the curve (AUC), accuracy, sensitivity, and specificity. Grad-CAM visualizations were employed to analyze model decision-making. Osteoporosis cases were pre-excluded by a separate model; OsPenScreen was applied only to non-osteoporotic cases.</p> Results <p>Our model achieved an AUC of 0.95 (95% CI: 0.94–0.97) on the external test datasets, with consistent performance across sex and age subgroups. The model demonstrated superior accuracy in detecting cases with significantly reduced bone mass and showed focused attention on weight-bearing bones in normal cases versus non-weight-bearing bones in low bone mass cases.</p> Conclusion <p>OsPenScreen represents a scalable and effective tool for opportunistic low bone mass screening, utilizing routine CXRs without additional healthcare burdens. Its robust performance across diverse datasets highlights its potential to enhance early detection, preventing progression to osteoporosis and reducing associated healthcare costs.</p>

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Opportunistic screening of low bone mass using knowledge distillation-based deep learning in chest X-rays with external validations

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Junhyeok Park,
  • Nha-Young Kim,
  • Hyun-Jin Bae,
  • Jinhoon Jeong,
  • Miso Jang,
  • Sung Jin Bae,
  • Jung-Min Koh,
  • Seung Hun Lee,
  • Joo Hee Yoon,
  • Chang Hoon Lee,
  • Namkug Kim

摘要

Summary

Low bone mass (LBM), which can lead to osteoporosis, is often undetected and increases the risk of bone fractures. This study presents OsPenScreen, a deep learning model that can identify low bone mass early using standard chest X-rays (CXRs). By detecting low bone mass sooner, this tool helps prevent the disease progression to osteoporosis, potentially reducing health complications and treatment costs. OsPenScreen was validated across four external datasets and consistently performed well, showing its potential as a reliable, cost-effective solution for opportunistic early screening in CXR.

Purpose

Low bone mass, an often-undiagnosed precursor to osteoporosis, significantly increases fracture risk and poses a substantial public health challenge. This study aimed to develop and validate a deep learning model, OsPenScreen, for the opportunistic detection of low bone mass using routine chest X-rays (CXRs).

Methods

OsPenScreen, a convolutional neural network-based model, was trained on 77,812 paired CXR and dual-energy X-ray absorptiometry (DXA) datasets using knowledge distillation techniques. Validation was performed across four independent datasets (5,935 images) from diverse institutions. The model’s performance was assessed using area under the curve (AUC), accuracy, sensitivity, and specificity. Grad-CAM visualizations were employed to analyze model decision-making. Osteoporosis cases were pre-excluded by a separate model; OsPenScreen was applied only to non-osteoporotic cases.

Results

Our model achieved an AUC of 0.95 (95% CI: 0.94–0.97) on the external test datasets, with consistent performance across sex and age subgroups. The model demonstrated superior accuracy in detecting cases with significantly reduced bone mass and showed focused attention on weight-bearing bones in normal cases versus non-weight-bearing bones in low bone mass cases.

Conclusion

OsPenScreen represents a scalable and effective tool for opportunistic low bone mass screening, utilizing routine CXRs without additional healthcare burdens. Its robust performance across diverse datasets highlights its potential to enhance early detection, preventing progression to osteoporosis and reducing associated healthcare costs.