Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome

• Published in: Diagnostics (Basel) 2023-10, Vol.13 (20), p.3211
• Main Authors: Elseddik, Marwa, Alnowaiser, Khaled, Mostafa, Reham R, Elashry, Ahmed, El-Rashidy, Nora, Elgamal, Shimaa, Aboelfetouh, Ahmed, El-Bakry, Hazem
• Format: Article
• Language: English
• Subjects: Accuracy; Adam optimizer; Algorithms; Artificial intelligence; Carpal tunnel syndrome; carpal tunnel syndrome (CTS); Clinical outcomes; cross-sectional area (CSA); Deep learning; deep learning (DL); Development and progression; Diagnostic tests; Edema; Human error; Kruskal-Wallis test; Ligaments; Machine learning; machine learning (ML); Medical diagnosis; Medical research; Medicine, Experimental; Neurophysiology; Patients; Questionnaires; statistical analysis; Tendons; Ultrasonic imaging
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13203211

Carpal tunnel syndrome (CTS) is a prevalent medical condition resulting from compression of the median nerve in the hand, often caused by overuse or age-related factors. In this study, a total of 160 patients participated, including 80 individuals with CTS presenting varying levels of severity across different age groups. Numerous studies have explored the use of machine learning (ML) and deep learning (DL) techniques for CTS diagnosis. However, further research is required to fully leverage the potential of artificial intelligence (AI) technology in CTS diagnosis, addressing the challenges and limitations highlighted in the existing literature. In our work, we propose a novel approach for CTS diagnosis, prediction, and monitoring disease progression. The proposed framework consists of three main layers. Firstly, we employ three distinct DL models for CTS diagnosis. Through our experiments, the proposed approach demonstrates superior performance across multiple evaluation metrics, with an accuracy of 0.969%, precision of 0.982%, and recall of 0.963%. The second layer focuses on predicting the cross-sectional area (CSA) at 1, 3, and 6 months using ML models, aiming to forecast disease progression during therapy. The best-performing model achieves an accuracy of 0.9522, an R2 score of 0.667, a mean absolute error (MAE) of 0.0132, and a median squared error (MdSE) of 0.0639. The highest predictive performance is observed after 6 months. The third layer concentrates on assessing significant changes in the patients’ health status through statistical tests, including significance tests, the Kruskal-Wallis test, and a two-way ANOVA test. These tests aim to determine the effect of injections on CTS treatment. The results reveal a highly significant reduction in symptoms, as evidenced by scores from the Symptom Severity Scale and Functional Status Scale, as well as a decrease in CSA after 1, 3, and 6 months following the injection. SHAP is then utilized to provide an understandable explanation of the final prediction. Overall, our study presents a comprehensive approach for CTS diagnosis, prediction, and monitoring, showcasing promising results in terms of accuracy, precision, and recall for CTS diagnosis, as well as effective prediction of disease progression and evaluation of treatment effectiveness through statistical analysis.