Effectiveness of Comprehensive Video Datasets: Toward the Development of an Artificial Intelligence Model for Ultrasonography-Based Severity Diagnosis of Carpal Tunnel Syndrome.

Objectives: Advances in diagnosing carpal tunnel syndrome (CTS) using ultrasonography (US) and artificial intelligence (AI) aim to replace nerve conduction studies. However, a method for accurate severity diagnosis remains unachieved. We explored the potential of comprehensive video data formats for constructing an effective model for diagnosing CTS severity.

Methods: We studied 75 individuals (52 with CTS) from 2019 to 2022, categorizing them into 3 groups based on disease severity. We recorded 132 US videos of carpal tunnel during finger movement. Features of the median nerve (MN) were extracted from automatically segmented US video frames, from which 3 datasets were created: a comprehensive video dataset with full information, a key metrics dataset, and an initial frame dataset with the least information. We compared the accuracy of machine learning algorithms for classifying CTS severity into 3 groups across these datasets using 63-fold cross-validation.

Results: The cross-sectional area of the MN correlated with severity (P < .05) but MN displacement did not. The algorithm using the comprehensive video dataset exhibited the highest sensitivity (1.00) and accuracy (0.75).

Conclusions: Our study demonstrated that utilizing comprehensive video data enables a more accurate US-based diagnosis of CTS severity. This underscores the value of capturing the patterns of MN deformation and movement, which cannot be captured by representative metrics such as medians or maximums. By further developing an AI model based on our findings, a simpler and painless method for assessing CTS severity can be achieved.