Development of a Deep Learning Model for Opportunistic Screening of Osteoporosis using Chest Radiographs
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Purpose
Prevention and early detection of osteoporosis remains a global challenge, more so in regions like the Philippines where screening barriers exist. Chest x-rays meanwhile are relatively inexpensive, and more frequently done, and therefore can be used for opportunistic screening. This study aimed to develop a deep learning model for osteoporosis detection from chest x-rays using DXA as the gold standard.
Methods
A convolutional neural network called Osteo-AI was developed using 406 pairs of chest x-rays and DXA scans of Filipino patients aged 50 and above. With data augmentation, the training set expanded to 6,300 pairs. Gradient-weighted class activation mapping technique was applied to localize and identify patterns and areas in the chest x-ray images correlating with osteoporosis.
Results
Training data consisted of 369 female patients and 37 males. Ages of the patients ranged from 50 to 89 with a mean age of 63 years old. Initial testing yielded promising results, with Osteo-AI achieving a diagnostic accuracy of 85.71%, easily outperforming a benchmark of 33.33%
Conclusion
Our findings suggest the potential of Osteo-AI to enhance osteoporosis screening accessibility, aiding in early intervention to prevent fragility fractures. Further research involving larger datasets is warranted to refine and optimize the model, potentially improving detection accuracy and expanding its utility in global healthcare settings.
SUMMARY
Osteoporosis screening is challenging and costly, especially in low-resource settings. An A.I. tool called Osteo-AI was developed using chest x-rays to detect osteoporosis, achieving promising accuracy. This approach may increase accessibility to early diagnosis, potentially preventing fractures. Further data and research could refine the tool, expanding its healthcare impact globally.
