Deep learning-based screening model for low bone mineral density on chest radiographs with multicentre, multinational validation

Purpose or Learning object

This study aimed to develop and validate a deep learning model for screening of patients with low bone mineral density (BMD) using chest radiographs (CXRs). 

Methods or Background

We retrospectively collected CXR data paired with DXA results from patients aged 50 and above from four different resources. Each patient's BMD was classified using a T-score threshold of -1.0, with scores of -1.0 or above defined as ‘normal’ and those below as ‘low BMD’. Of the 57,589 CXRs from Hospital A, 55,600 were utilized for training, and 1,989 were used for internal validation. For external validation, 3338, 938, and 295 CXRs were collected from B, C hospitals and D platform, respectively, representing diverse patient demographics and clinical backgrounds. A deep learning model was developed to perform binary classification of patients' BMD as either normal or low, based on their CXRs.

Results or Findings

In the A dataset, the model yielded an AUC of 0.95 and demonstrated sensitivity of 0.97, specificity of 0.65, and F1 score of 0.90. In the datasets B, C, and D, the model achieved AUCs of 0.91, 0.89, and 0.82. The model’s sensitivity was 0.87, 0.88, and 0.64; specificity was 0.77, 0.71, and 0.85; and F1 score was 0.80, 0.88, and 0.76, respectively. 

Conclusion

The proposed low BMD screening system demonstrated performance exceeding an AUC of 0.8 in all external datasets, highlighting the robustness of the system. Notably, the system showed promising performance even on the D dataset, which comprised individuals of completely different racial backgrounds. This suggests the potential to promptly identify patients with low BMD from CXRs, the most widely used imaging modality globally.

Limitations

First, this is a retrospective study. Second, a large proportion of the dataset comprises a single national population.

Funding for this study

Not applicable 

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