Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation - Gu, Fanbin; Fan, Jingyuan; Cai, Chengfeng; Wang, Zhaoyang; Liu, Xiaolin; Yang, Jiantao; Zhu, Qingtang (Frontiers in Neurology, 2022)
Background: Radial, ulnar or median nerve injuries are common peripheral nerve injuries. They usually present specific abnormal signs on the hands as evidence for hand surgeons to make diagnoses. However, it is difficult for primary healthcare providers without specialized knowledge to realize the clinical meaning and the potential nerve injuries through the abnormalities, often leading to misdiagnosis. Developing technologies for automatically detecting abnormal hand gestures would assist general medical service practitioners with an early diagnosis and treatment. Methods: Based on expert experience, we selected three hand gestures with predetermined features and rules as three independent binary classification tasks for abnormal gesture detection. Images from patients with unilateral radial, ulnar or median nerve injuries and healthy volunteers were obtained using a smartphone. The landmark coordinates were extracted using Google MediaPipe Hands to calculate the features. The Receiver Operating Characteristic Curve was employed for feature selection. We compared the performance of rules-based models with Logistic Regression, Support Vector Machine and Random Forest machine learning models by evaluating the accuracy, sensitivity, and specificity. Results: Twenty-two patients and thirty-four volunteers participated in the study with 1344 images. In rules-based models, eight features were finally selected. The accuracy, sensitivity, and specificity were (1) 98.2%, 91.7% and 99.0% for radial nerve injury detection; (2) 97.3%, 83.3% and 99.0% for ulnar nerve injury detection; and (3) 96.4%, 87.5% and 97.1% for median nerve injury detection. All machine learning models achieved accuracy above 95% and sensitivity ranged from 37.5% to 100%. Conclusion: Our study provides a helpful tool for detecting abnormal gestures in radial, ulnar or median nerve injuries with satisfying accuracy, sensitivity and specificity. It confirms that hand pose estimation could automatically analyze and detect the abnormalities from images of these patients. It has the potential to be a simple and convenient screening method for primary healthcare and telemedicine application.