Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia

项目摘要/摘要 摘要孤立性肌张力障碍是一种病理生理不明的运动障碍，可导致不自主肌肉。 宫缩导致异常的、典型的有模式的扭曲的动作和姿势。一项重大挑战 肌张力障碍的临床治疗是由于缺乏生物标志物和相关的‘金’标准 诊断性测试。目前，肌张力障碍的诊断是通过对其症状的临床评估来指导的， 导致临床医生之间的低符合率和高诊断错误率。据估计，只有 5%的患者在症状出现时得到了准确的诊断，平均诊断延迟延长 到10.1岁。因此，临床上迫切需要建立一种客观和病理生理学-- 孤立性肌张力障碍的CALY相关诊断试验及其临床效度 肌张力障碍的认知障碍。该项目的目标是进行平行的回顾和前瞻性研究，以 基于生物标记物的诊断深度学习平台DystoniaNet的临床验证 孤立性肌张力障碍。在我们强劲的初步数据的支持下，我们的中心假设是经过验证的绩效- DystonianNet的Mance特性是可以接受的，可以在临床上翻译和实施- TING是诊断孤立性肌张力障碍的客观、准确、快速的平台。我们假设其效用- DystoniaNet平台在临床环境下的能力将显著提高肌张力障碍诊断的准确性。 SIS，并显著减少诊断时间，特别是在表型复杂和不确定的病例中。 我们将追求以下两个具体目标：(1)DystoniaNet治疗肌张力障碍的回顾性临床验证 诊断，以及(2)DystoniaNet的前瞻性随机临床验证。拟议的研究是创新的-- 目的是因为它建立在新的概念和方法学概念的基础上，用于临床验证双 基于omarker的诊断平台，专门解决当前尚未满足的肌张力障碍的临床需求 管理层。这项拟议的研究具有重要意义，因为它将推进第一个客观诊断平台-- 从发现和分析验证到临床应用的肌张力障碍诊断表，从而填补了关键临床-- 这种疾病的护理标准存在着严重的差距。早期发现和诊断肌张力障碍将使其及早 治疗和改善预后，对医疗保健和患者的生活质量产生了总体积极的影响。

PROJECT SUMMARY / ABSTRACT Isolated dystonia is a movement disorder of unknown pathophysiology, which causes involuntary muscle contractions leading to abnormal, typically patterned, twisting movements and postures. A significant challenge in the clinical management of dystonia is due to the absence of a biomarker and associated ‘gold’ standard diagnostic test. Currently, the diagnosis of dystonia is guided by clinical evaluations of its symptoms, which lead to a low agreement between clinicians and a high rate of diagnostic inaccuracies. It is estimated that only 5% of patients receive an accurate diagnosis at symptom onset, and the average diagnostic delay extends up to 10.1 years. There is, therefore, an urgent unmet clinical need to establish an objective and pathophysiologi- cally relevant diagnostic test for isolated dystonia and to determine its clinical validity for accurate and fast di- agnosis of dystonia. The objective of this project is to conduct parallel retrospective and prospective studies to clinically validate the performance of DystoniaNet, a biomarker-based deep learning platform for diagnosis of isolated dystonia. Supported by our strong preliminary data, our central hypothesis is that validated perfor- mance characteristics of DystoniaNet are acceptable for its translation and implementation in the clinical set- ting as an objective, accurate, and fast platform for diagnosis of isolated dystonia. We postulate that the avail- ability of DystoniaNet platform in the clinical setting will significantly increase the accuracy of dystonia diagno- sis and significantly decrease the time to diagnosis, especially in phenotypically complex and uncertain cases. We will pursue the following two specific aims: (1) retrospective clinical validation of DystoniaNet for dystonia diagnosis, and (2) prospective randomized clinical validation of DystoniaNet. The proposed research is innova- tive because it is built on the novel conceptual and methodological concepts for clinical validation of a bi- omarker-based diagnostic platform that specifically addresses the current unmet clinical need for dystonia management. The proposed research is significant because it will advance the first objective diagnostic plat- form for dystonia diagnosis from its discovery and analytical validation to clinical use, thus filling the critical clin- ical gap in the standard of care of this disorder. Early detection and diagnosis of dystonia will enable its early therapy and improved prognosis, having an overall positive impact on healthcare and patient’s quality of life.