Utilizing heat maps as explainable artificial intelligence for detecting abnormalities on wrist and elbow radiographs.

INTRODUCTION

Wrist and elbow radiographs, which plays a key role in diagnosing both fractures and degenerative conditions, present a diagnostic challenge due to intricate structures and subtle pathological signs. Artificial intelligence (AI) through deep learning models, has transformed diagnostic imaging, achieving accuracy rates, with explainable AI (XAI) and Gradient-weighted Class Activation Mapping (Grad-CAM) enhancing transparency of AI-driven diagnosis.

METHOD

The MURA-dataset, a comprehensive collection of musculoskeletal radiographs, specifically focuses on wrist and elbow images, ensuring a spectrum of normal and abnormal conditions. An ensemble of transfer-learning models, including VGG16, VGG19, ResNet, DenseNet, InceptionV3 and Xception, was applied, with implemented Grad-CAM techniques, providing interpretable heat maps. The Dice Similarity Coefficient (DSC) evaluated the algorithm's efficiency in recognizing regions of interest.

RESULTS

The average test accuracy of the 20 models were 0.81 (0.72-0.84), and 0.60 (0.49-0.73) for the wrist and elbow radiographs, respectively. The highest performing models were VGG16 with a test accuracy of 0.84, and DenseNet169 with a test accuracy of 0.73. The DSC were calculated for the six highest performing models, and agreements between algorithms were found on radiographs with metal, and only minimal agreement for radiographs with fractures.

CONCLUSION

The study employed twenty transfer-learning models on wrist and elbow radiographs presenting accuracy and partial agreement with Grad-CAM technique evaluation. This study enables comprehension of model performance and avenues for potential enhancement.

IMPLICATION FOR PRACTICE

The utilization of artificial intelligence, specifically transfer-learning models, could greatly enhance the accuracy and efficiency of diagnosing conditions from wrist and elbow radiographs. Additionally, the application of explainable AI techniques such as Grad-CAM can provide visual validation and transparency, thereby strengthening trust and adoption in clinical settings.