Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.

Abstract— To comparatively evaluate various driving methods of an electronic display in respect to image sticking, a consistent and reliable quantification method is required. For proper evaluation, the entire area of a display is often monitored by using a chessboard pattern, and long‐range gradual luminous variation in the background is eliminated. Estimation in terms of a single number is also preferred for simple comparison of image sticking. However, the prior method that uses the initial luminance for normalization and estimates the range‐to‐maximum ratio is not well‐suited for the driving methods that relieve image sticking by restoring luminance uniformity. We have developed a method of extracting reference values for normalization and introduced the relative standard deviation (RSD) into our estimation. The resulting method is insensitive to the temporal change in the long‐range gradual luminous variation and sufficiently indicative to allow driving methods to be compared effectively. The reference extraction method and the indicative capability of the RSD have been assessed by experiments using a real active‐matrix organic light‐emitting‐diode (AMOLED) display cell.