A review of evaluation methods for Duchenne muscular dystrophy

Abstract

In recent years, the study of body movement performance in disease assessment has attracted significant attention. Duchenne Muscular Dystrophy (DMD) is a disease that significantly affects mobility. It is crucial to assess the movement of both the upper and lower limbs to diagnose this condition effectively. This research categorizes DMD assessment methods into two types: human-based assessments (based on questionnaires) and Machine-based assessments (using motion tracking and computer algorithms). The validity of human-based assessments, which often focus on daily activities and muscle strength, may be called into question. This issue underscores the necessity of utilizing qualitative and observational methods to achieve more accurate assessments. In contrast, Machine-based assessment have benefited from recent technological advancements that enable innovative techniques for disease evaluation, thereby improving the accuracy and validity of DMD diagnosis. The integration of motion tracking systems with artificial intelligence algorithms mitigates the limitations of visual and subjective assessments, providing more precise and objective results. These advancements aim to enhance the accuracy, reliability, and validity of DMD diagnosis while offering a more comprehensive approach to assessing individuals with the condition. In this study, the existing assessment methods in both categories are introduced and compared, analyzing their advantages and limitations to provide an accurate evaluation of their performance in diagnosing DMD. The aim of this review is to identify the best methods for diagnosing and monitoring DMD based on accuracy, reliability, and efficiency.