Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome

Abstract

Background: In case of focal neuropathy, the muscle fibers innervated by the corresponding nerves are replaced with fat or fibrous tissue due to denervation, which results in increased echo intensity (EI) on ultrasonography. EI analysis can be conducted quantitatively using gray scale analysis. Mean value of pixel brightness of muscle image defined as EI. However, the accuracy achieved by using this parameter alone to differentiate between normal and abnormal muscles is limited. Recently, attempts have been made to increase the accuracy using artificial intelligence (AI) in the analysis of muscle ultrasound images. CTS is the most common disease among focal neuropathy. In this study, we aimed to verify the utility of AI assisted quantitative analysis of muscle ultrasound in CTS.

Methods: This is retrospective study that used data from adult who underwent ultrasonographic examination of hand muscles. The patient with CTS confirmed by electromyography and subjects without CTS were included. Ultrasound images of the unaffected hands of patients or subjects without CTS were used as controls. Ultrasonography was performed by one physician in same sonographic settings. Both conventional quantitative grayscale analysis and machine learning (ML) analysis were performed for comparison.

Results: A total of 47 hands with CTS and 27 control hands were analyzed. On conventional quantitative analysis, mean EI ratio (i.e. mean thenar EI/mean hypothenar EI ratio) were significantly higher in the patient group than in the control group, and the AUC was 0.76 in ROC analysis. In the analysis using machine learning, the AUC was the highest for the linear support vector classifier (AUC = 0.86). When recursive feature elimination was applied to the classifier, the AUC value improved to 0.89.

Conclusion: This study showed a significant increase in diagnostic accuracy when AI was used for quantitative analysis of muscle ultrasonography. If an analysis protocol using machine learning can be established and mounted on an ultrasound machine, a noninvasive and non-time-consuming muscle ultrasound examination can be conducted as an ancillary tool for diagnosis.

Keywords: Artificial intelligence; Carpal tunnel syndrome; Machine learning; Muscle ultrasound; Quantitative ultrasound.

Fig. 1

Representative US images of hand muscles and derived histograms. White rectangular or free hand regions of US images is region of interest (ROI). Mean and StdDev is the mean and standard deviation of pixel brightness in ROI. [a-b] Thenar muscle (a) and the hypothenar muscle (b) in the control group. [c-d] The thenar muscle (c) and hypothenar muscle (d) of a patient with moderate-degree CTS

Fig. 2

Overview of our approach. We applied machine learning (ML) algorithms to improve prediction accuracy and to identify important features required for classification. We extracted radiomic features from the regions of interests (ROI) of thenar and hypothenar, then predicted performance using four ML classifiers. In addition, important features were selected by applying recursive feature elimination (RFE) to each classifier

Fig. 3

ROC curve and AUC score. (a) ROC using mean echo-intensity ratio (thenar/hypothenar) for distinguishing the hands with CTS. (b – e) ROC for each machine learning (ML) classifier. RFE was not applied to XGB classifier since its performance was better without RFE. (b) Random forest. (c) Adaptive boosting. (d) Linear support vector classifier. (e) XGB classifier

Fig. 4

Distribution of robust mean absolute deviation (rMAD) feature values. T_rMAD denotes rMAD obtained from the thenar muscle, and H_rMAD denotes the rMAD obtained from the hypothenar muscle. (a) Joint plot of T_rMAD and H_rMAD. (b) Distribution of the T_rMAD values within the box plot. (c) Distribution of the H_rMAD values with the box plot

Fig. 5

Distribution of interquartile range (IQR) feature values. T_IQR denotes the IQR obtained from the thenar muscle and H_IQR denotes the IQR obtained from the hypothenar muscle. (a) Joint plot of T_IQR and H_IQR. (b) Distribution of the T_IQR values with the box plot. (c) Distribution of the H_IQR values with the box plot