Pilot Feasibility Study of a Multi-View Vision Based Scoring Method for Cervical Dystonia

Abstract

Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC)(3,1) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD.

Quantitative assessment of head movement dynamics in dystonia using visual perceptive deep learning: a multi-centre retrospective longitudinal cohort study

“…We first demonstrate the robustness and clinical applicability of our deep learning framework in accurately inferring head-angle deviations during attempted ‘null’ head positions from diverse clinical videos captured using consumer-grade hardware. Our visual perceptive approach surpasses various vision-based frameworks that relied on multiple or specialized depth cameras to automate ratings [21, 22], and achieves comparable performance to a recent study by Zhang et al [20]. However, a distinguishing feature of our approach is the ability to estimate head angles in real-time using a portable device such as smartphones or tablets.…”

Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning

“…We first demonstrate the robustness and clinical applicability of our deep learning framework in accurately inferring head-angle deviations during attempted ’null’ head positions from diverse clinical videos captured using consumer-grade hardware. Our visual perceptive approach surpasses various vision-based frameworks that relied on multiple or specialized depth cameras to automate ratings 22 , 23 , and achieves comparable performance to a recent study by Zhang et al 21 (we note that we use TWSTRS 0-3 ratings for latero-, antero-, and retrocollis, which differs from the TWSTRS-2 0-4 ratings used in Zhang et al 21 , which limits quantitative comparison). However, a distinguishing feature of our approach is the ability to estimate head angles in real-time using a portable device such as smartphones or tablets.…”

“…Contactless, visionbased methods utilising multiple and/or special depth cameras have shown promise in extracting head angles in cervical dystonia. Nevertheless, their clinical validity has been limited, especially when operating under monocular conditions 22,23 . In this context, computer vision, a branch of contemporary artificial intelligence, has emerged as a disruptive and promising technology in clinical neuroscience and broader medical applications [24][25][26][27][28] .…”

Pervasive Monitoring System for Cervical Dystonia: A Feasibility Study Using 9DOF Sensors

“…The advent of wearable sensor technology offers a promising avenue for more objective and continuous monitoring of CD symptoms. Recent studies have employed various forms of wearable sensors, including inertial measurement units (IMUs), to capture the kinematic properties of movement disorders [14,15]. These technologies have shown potential in providing detailed insights into the movement patterns and anomalies associated with conditions like Parkinson's disease and essential tremor [16,17].…”