Objective: High-resolution ultrasound is an emerging tool for diagnosing carpal tunnel syndrome caused by the compression of the median nerve at the wrist. This systematic review and meta-analysis aimed to explore and summarize the performance of deep learning algorithms in the automatic sonographic assessment of the median nerve at the carpal tunnel level.
Methods: PubMed, Medline, Embase, and Web of Science were searched from the earliest records to May 2022 for studies investigating the utility of deep neural networks in the evaluation of the median nerve in carpal tunnel syndrome. The quality of the included studies was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies. The outcome variables included precision, recall, accuracy, F-score, and Dice coefficient.
Results: In total, seven articles were included, comprising 373 participants. The deep learning and related algorithms comprised U-Net, phase-based probabilistic active contour, MaskTrack, ConvLSTM, DeepNerve, DeepSL, ResNet, Feature Pyramid Network, DeepLab, Mask R-CNN, region proposal network, and ROI Align. The pooled values of precision and recall were 0.917 (95 % confidence interval [CI], 0.873-0.961) and 0.940 (95 % CI, 0.892-0.988), respectively. The pooled accuracy and Dice coefficient were 0.924 (95 % CI, 0.840-1.008) and 0.898 (95 % CI, 0.872-0.923), respectively, whereas the summarized F-score was 0.904 (95 % CI, 0.871-0.937).
Conclusion: The deep learning algorithm enables automated localization and segmentation of the median nerve at the carpal tunnel level in ultrasound imaging with acceptable accuracy and precision. Future research is expected to validate the performance of deep learning algorithms in detecting and segmenting the median nerve along its entire length as well as across datasets obtained from various ultrasound manufacturers.
Keywords: Artificial intelligence; Carpal tunnel; Convolutional neural network; Neuropathy; Ultrasonography.
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