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Page 223 Dababneh et al. Art Int Surg 2024;4:214-32 https://dx.doi.org/10.20517/ais.2024.50

In 2023, Knight et al. also assessed the diagnostic accuracy of 3DUS for the detection of distal radius
[57]
fractures while also comparing it to two-dimensional ultrasound (2DUS) . AI models, ResNet34 and
Densenet121, were trained on 16,865 images for 2DUS and 15,882 images for 3DUS. Densenet121 had a
higher accuracy than ResNet34 with 2DUS (0.94 vs. 0.89), while ResNet34 achieved perfect accuracy with
3DUS (1.00), compared to 0.94 for DenseNet121. Overall, AI’s ability to accurately read images was
demonstrated, performing comparably to experts in the field with over a decade of experience.

AI-assisted OA diagnosis and management
In Caratsch et al.’s study, an automated ML model was used for distal hand osteoarthritis (DIP-OA)
detection and classification on radiographs . The ML platform used for this study was Giotto [learn to
[58]
forecast (L2F)], which achieved an overall accuracy of 75%, but its precision decreased for higher grades of
OA. Similarly, Overgaard et al. used a CNN-based model (U-Net++) to assess OA severity according to the
[59]
EULAR-OMERACT grading system (EOGS) . The AI model achieved strong agreement with expert
judgments, slightly outperforming previous studies. Moreover, this model provided visual explanations by
marking bone (red), synovium (blue), and osteophytes (pink) on the images, aiding clinicians in
understanding how the AI arrived at its assessments.

Loos et al. published an article exploring the potential of AI in predicting pain and hand function
[60]
improvement one year post thumb carpometacarpal OA surgery . Among the models used, the
random forest model showed superior performance in predicting pain outcomes using 27 variables,
but it still produced a relatively poor AUC of 0.59. On the other hand, gradient boosting machine
(GBM) outperformed other models in predicting hand function outcomes, achieving an AUC of 0.74.

CTS diagnosis and management
CTS, the most prevalent compressive mononeuropathy, significantly impacts patients’ quality of life. This
justifies hand surgeons’ exploration of AI applications to enhance the diagnostic accuracy and management
of this condition which frequently requires surgical decompression to improve symptoms.

Symptoms, physical examination and electromyography (EMG) remain the gold standard for CTS diagnosis
and severity assessment. Nevertheless, no widely used screening test has been implemented. In 2021,
Watanabe et al. explored the accuracy of an application designed for CTS screening . Their app requires
[54]
users to draw spirals using a stylus while a pretrained algorithm analyzes the trajectory and the pressure
applied during the drawing. The application achieved a sensitivity of 82% and a specificity of 71% in
diagnosing CTS, which was inferior to other previously developed apps.

Koyama et al. also developed an application programmed with an anomaly detection algorithm to screen for
[61]
CTS based on patients’ difficulty with thumb opposition . The app, available for download on
smartphones, was able to diagnose CTS, achieving an AUC of 0.86, demonstrating a performance
comparable to traditional physical examination methods. To enhance the model’s accuracy, the data were
subsequently modified to focus only on the directions that corresponded with thumb opposition.

US is widely used for the diagnosis of CTS. Faeghi et al. explored the diagnostic accuracy of a computer-
aided diagnosis (CAD) system developed using radiomics features extracted from US images of the median
nerve . The CAD system outperformed both radiologists in this study by achieving an AUC of 0.926.
[62]
Shinohara et al. also investigated the role of DL in diagnosing CTS using US images . The primary focus of
[63]
their study was to bypass the traditional method of measuring the median nerve’s cross-sectional area
(CSA). The authors applied transfer learning to three pretrained AI models. The algorithm achieved an