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Objectives <p>To develop and do multicenter validation on an algorithm that screens for osteoporosis from abdominal CTs.</p> Methods <p>This is a diagnostic accuracy study with retrospective data from January 2022 to July 2022 consisting of two steps: a segmentation step of the lumbar vertebral bodies, involving outpatient non-contrast abdominal CTs from Diagnósticos da América S/A (Dasa), and a multicenter validation step incorporating data from four additional institutions. The segmentation employed a 2D UNet with a ResNet34 backbone. We determined the Pearson correlation coefficient (r) between the mean of the slices’ mean attenuations (MSMA) on CT scans against the bone mineral density (BMD) on recent DEXA scans, calculated AUCs and performance metrics for osteoporosis prediction, including 95% confidence intervals, and evaluated calibration plots.</p> Results <p>The multicenter validation included 504 participants (median age, 66&#xa0;years, interquartile range 56–72; 388 women). A linear regression analysis showed an r of 0.62 (95% CI, 0.56–0.67) between MSMA (HU) and BMD (g/cm<sup>2</sup>). The AUCs (95% CI) for distinguishing between normal and osteoporosis were 0.96 (0.89, 1.0) for the internal dataset and 0.82 (0.74, 0.89) for the external dataset, and the performance metrics (95% CI), for a globally optimized threshold of 202.6 HU, were 100% sensitivity (44, 100) and 91% specificity (84, 95) for internal data and 79% sensitivity (61, 90) and 81% specificity (76, 84) for external sites. MSMA was independently associated with osteoporosis on a mixed-effects logistic regression analysis. The model showed good calibration, with Brier score of 0.054.</p> Conclusion <p>We developed and performed a multicenter validation of a DL model for osteoporosis prediction on CT.</p> Graphical abstract <p></p>

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Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Augusto Sarquis Serpa,
  • Marcelo Straus Takahashi,
  • Eduardo Moreno Júdice de Mattos Farina,
  • Sean Bennett,
  • Nishith Khandwala,
  • Melissa Major,
  • Jack Paparian,
  • Fernanda Garozzo Velloni,
  • Steven Rothenberg,
  • Leonardo Kayat Bittencourt,
  • Ross Warren Filice,
  • John Mongan,
  • Felipe Campos Kitamura

摘要

Objectives

To develop and do multicenter validation on an algorithm that screens for osteoporosis from abdominal CTs.

Methods

This is a diagnostic accuracy study with retrospective data from January 2022 to July 2022 consisting of two steps: a segmentation step of the lumbar vertebral bodies, involving outpatient non-contrast abdominal CTs from Diagnósticos da América S/A (Dasa), and a multicenter validation step incorporating data from four additional institutions. The segmentation employed a 2D UNet with a ResNet34 backbone. We determined the Pearson correlation coefficient (r) between the mean of the slices’ mean attenuations (MSMA) on CT scans against the bone mineral density (BMD) on recent DEXA scans, calculated AUCs and performance metrics for osteoporosis prediction, including 95% confidence intervals, and evaluated calibration plots.

Results

The multicenter validation included 504 participants (median age, 66 years, interquartile range 56–72; 388 women). A linear regression analysis showed an r of 0.62 (95% CI, 0.56–0.67) between MSMA (HU) and BMD (g/cm2). The AUCs (95% CI) for distinguishing between normal and osteoporosis were 0.96 (0.89, 1.0) for the internal dataset and 0.82 (0.74, 0.89) for the external dataset, and the performance metrics (95% CI), for a globally optimized threshold of 202.6 HU, were 100% sensitivity (44, 100) and 91% specificity (84, 95) for internal data and 79% sensitivity (61, 90) and 81% specificity (76, 84) for external sites. MSMA was independently associated with osteoporosis on a mixed-effects logistic regression analysis. The model showed good calibration, with Brier score of 0.054.

Conclusion

We developed and performed a multicenter validation of a DL model for osteoporosis prediction on CT.

Graphical abstract