AI bone lesion classifier with sensitivity-driven optimization for radiographs.

AIM: This study aims to develop a deep learning classifier for detecting primary bone lesions on radiographs, emphasizing high sensitivity while maintaining practical clinical usability.

MATERIAL AND

METHODS

Radiographs of the upper and lower extremities were reviewed by board-certified radiologists and categorized into two groups: "Normal" (without bone lesions) and "Abnormal" (with bone lesions). The final dataset comprised 1,177 radiographs from 310 patients, including 547 abnormal and 630 normal cases. The MobileNetV2 architecture was trained with a sensitivity-driven approach designed to minimize false negatives. Model performance was evaluated on a hold-out test set, and attention maps were generated to enhance interpretability and visualize regions contributing to the model's decisions.

RESULTS

The model was tested on a naïve hold-out test set. The results received on the test set: sensitivity of 96.6%, specificity of 82.2%, accuracy of 87.9%, area under the curve (AUC) of 0.94, and 95% confidence interval of [0.901, 0.981].

CONCLUSION

The study demonstrates the feasibility of deploying AI-based tools for radiographic detection of bone tumors with a sensitivity-focused optimization. These tools have the potential to enhance diagnostic accuracy, reduce diagnostic delays, and support population health initiatives.