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Dababneh et al. Art Int Surg 2024;4:214-32 https://dx.doi.org/10.20517/ais.2024.50 Page 224

accuracy of 0.96, thereby demonstrating its potential to detect CTS without relying on CSA measurements.
Despite these promising outcomes, the study faced limitations due to the lack of external validation and the
relatively small dataset. To address these limitations, Mohammadi et al. conducted a similar study to that of
Faeghi et al., incorporating a larger dataset of 416 median nerves extracted from two countries, Iran and
Colombia, which was used to train and evaluate multiple DL models [62,64] . The highest-performing algorithm
achieved an AUC of 0.910 in the internal validation test and an AUC of 0.890 in the external validation test.

In 2023, Kim et al. also published on this topic. Their study compared ML analysis to conventional
[65]
quantitative grayscale analysis of US images for diagnosing CTS . The conventional quantitative analysis
evaluated the mean echo intensity (EI) by calculating mean thenar EI/mean hypothenar EI ratio. Their
findings indicate that hands affected by CTS had a higher EI ratio. However, this method had poor
performance metrics, achieving an AUC of 0.755. In contrast, the ML model significantly outperformed the
conventional method, achieving an AUC of 0.89.

[66]
Similarly, Kuroiwa et al. investigated the role of DL in US diagnosis of CTS . Their study introduced an
innovative approach that focuses on measuring the volume of the median nerve to diagnose CTS on US
images in contrast to CSA measurement. The DL prediction model used achieved a Dice score of 0.80,
which is highly comparable to the manual tracing, which had a Dice score of 0.76. Additionally, compared
to a human reader, the DL model achieved a 0.99 accuracy rate with an AUC of 0.91 with the test data set.

Electrophysiological nerve conduction studies (NCS) have long been the gold standard in diagnosing and
classifying CTS. Tsamis et al. explored different AI models’ ability to automatically classify and accurately
[67]
diagnose this condition . Five ML models were trained with common electrodiagnostic features, as well as
additional physiological and mathematical characteristics. Support vector machine (SVM) achieved the
highest accuracy rate and demonstrated its superiority when classifying disease severity, outperforming both
NSC and clinical diagnosis.

Bakalis et al. conducted a study comparing AI’s role in diagnosing CTS through motor versus sensory nerve
conduction approaches . For the motor approach, various CNNs were employed to analyze motor signals
[68]
recorded from the participants’ median nerve and to subsequently classify subjects into patients or controls.
CONV2D outperformed other CNNs, achieving an overall accuracy rate of 94%. In the sensory approach,
the RF model excelled with a 97.12% accuracy in diagnosing the severity of CTS and excluding other
mononeuropathies, making it the top performer in this section.

In 2023, Elseddik et al. published a study focusing on the development of a ML model designed to
[69]
determine CTS severity . The proposed model demonstrated its ability to accurately diagnose CTS and
classify its severity, even when presented with data from other conditions with overlapping symptoms.
Additionally, the AI model was able to precisely predict patient improvement probability following median
nerve hydrodissection, making it a potentially useful tool for preoperative patient expectation management.
Similarly, Park et al. conducted a study to assess AI’s efficacy in classifying the severity of CTS using
[70]
personal, clinical, and imaging features . All the models in the study had an overall accuracy rate of over
70%.

In 2022, Harrison et al. explored AI’s ability to predict which patients would benefit most from carpal
tunnel decompression (CTD) . The highest-performing model for predicting functional and symptomatic
[71]
improvement was Extreme Gradient Boosting (XGBoost), which achieved an accuracy rate of 71.8% and
75.9% for functional and symptomatic improvement, respectively. Hoogendam et al. also focused on