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

DystoniaNet 深度学习平台诊断孤立性肌张力障碍的临床验证

• 批准号: 10391712
• 负责人: Kristina Simonyan
• 金额: $ 67.93万
• 依托单位: MASSACHUSETTS EYE AND EAR INFIRMARY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10391712
• 关键词: Address; Affect; Age; Agreement; Anterior; Biological Markers; Brain imaging; Characteristics; Chronic; Clinic; Clinical; Clinical Management; Complex; Corpus Callosum; Data; Development; Diagnosis; Diagnostic; Diagnostic tests; Differential Diagnosis; Disease; Disease Management; Dystonia; Early Diagnosis; Early treatment; Evaluation; Functional disorder; General Population; Gold; Healthcare; Hospitals; Human; Inferior; Knowledge; Lead; Machine Learning; Methodology; Mission; Movement; Movement Disorders; Muscle Contraction; Occupational; Outcome; Output; Partner in relationship; Patients; Pattern; Performance; Phenotype; Posture; Predictive Value; Prognosis; Prospective Studies; Provider; Psychological Stress; Public Health; Quality of life; Randomized; Research; Resources; Retrospective Studies; Smooth Muscle; Social isolation; Specificity; Structure of inferior temporal gyrus; Symptoms; Telemedicine; Testing; Time; Translations; Tweens; United States National Institutes of Health; Validation; accurate diagnosis; accurate diagnostics; base; brain magnetic resonance imaging; comparative; data registry; deep learning; deep learning algorithm; diagnostic algorithm; diagnostic biomarker; diagnostic platform; diagnostic strategy; disability; executive function; improved; innovation; motor control; motor impairment; nervous system disorder; neural network; novel; novel diagnostics; prospective; psychiatric comorbidity; relating to nervous system; research clinical testing; standard of care; teration; thalamocortical tract; tool; translational applications; underserved area; web based interface

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.

项目摘要 /摘要 孤立的肌张力障碍是一种未知病理生理学的运动障碍，会导致非自愿肌肉 宫缩导致异常，通常是图案的扭曲运动和位置。一个重大的挑战 在肌张力障碍的临床管理中，是由于缺乏生物标志物和相关的“黄金”标准 诊断测试。目前，肌张力障碍的诊断受到对其症状的临床评估的指导， 导致临床医生之间的一致性较低，并且诊断性不准确。据估计，只有 5％的患者在症状发作时接受准确的诊断，平均诊断延迟延伸 到10。1年。因此，紧急无法满足的临床需要建立客观和病理生理学 - 调用有关孤立肌张力障碍的相关诊断测试，并确定其临床有效性，以进行准确而快速 肌张力障碍。该项目的目的是进行并行回顾性和前瞻性研究 临床上验证Dystonianet的性能，Dounstonianet是一个基于生物标记的深度学习平台，用于诊断 孤立的肌张力障碍。在我们强大的初步数据的支持下，我们的核心假设是经过验证的性能 dounstonianet的管理特征是可以接受的，因为它在临床集合中的翻译和实施 - 作为诊断孤立肌张力障碍的客观，准确且快速的平台。我们假设可用性 - 在临床环境中肌张力障碍平台的能力将显着提高肌张力障碍诊断的准确性 - SIS并显着减少了诊断时间，尤其是在表型复杂且不确定的情况下。 我们将追求以下两个具体目的：（1）肌张力障碍的肌张力障碍的回顾性临床验证 诊断和（2）donsonianet的前瞻性随机临床验证。拟议的研究是创新的 因为它建立在新颖的概念和方法学概念上，用于临床验证双期验证 基于Omarker的诊断平台，该平台专门满足当前未满足的肌张力障碍临床需求 管理。拟议的研究很重要，因为它将推进第一个客观的诊断平台 从发现和分析验证到临床使用的肌张力障碍诊断形式，从而填补了关键的临床 护理标准的差距。肌张力障碍的早期检测和诊断将使其早期 治疗和预后改善，对医疗保健和患者的生活质量产生积极的积极影响。