# Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia > **NIH NIH R01** · MASSACHUSETTS EYE AND EAR INFIRMARY · 2022 · $679,279 ## Abstract 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. ## Key facts - **NIH application ID:** 10391712 - **Project number:** 1R01NS124228-01A1 - **Recipient organization:** MASSACHUSETTS EYE AND EAR INFIRMARY - **Principal Investigator:** Kristina Simonyan - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $679,279 - **Award type:** 1 - **Project period:** 2022-06-01 → 2027-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10391712 ## Citation > US National Institutes of Health, RePORTER application 10391712, Clinical Validation of DystoniaNet Deep Learning Platform for Diagnosis of Isolated Dystonia (1R01NS124228-01A1). Retrieved via AI Analytics 2026-06-02 from https://api.ai-analytics.org/grant/nih/10391712. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*