Measurement-grade video for computer vision in neurology: an international consensus framework for acquisition and reporting
Year: 2026
Type: preprint
Abstract: Background : Neurological motor assessment relies on semi-quantitative, scale-based measures with inherent clinimetric limitations that disproportionately affect clinical trials aimed at early disease stages, where sensitivity is critical. Computer vision (CV) enables objective, scalable quantification of motor signs from standard video, yet clinical translation remains limited. A central bottleneck lies in the dependence of these methods on visual input: variability in video acquisition propagates into algorithmic outputs, constraining reproducibility and generalizability, while inconsistent reporting obscures a major source of variance. These limitations necessitate a clear definition of measurement-grade video as a prerequisite for developing reliable and scalable computer vision-based biomarkers in neurology. Methods : We conducted an international, multidisciplinary two-round modified Delphi process (n=23/20 experts across five continents) to define requirements for measurement-grade video in neurological motor assessment. In round one, items were elicited through predominantly open-ended queries on acquisition practices, failure modes, and outcome priorities. In round two, 16 acquisition and 21 metadata items were evaluated using a tiered scheme, with consensus pre-specified at ≥70% endorsement. In parallel, four domain-specific workgroups synthesized literature and real-world experience into domain-specific extensions. Results : Acquisition variability emerged as a principal barrier to reliable measurement across domains, with disagreement reflecting differences in enforceability rather than relevance. Three acquisition items reached mandatory consensus: continuous body-region coverage, protocolized video setup, and standardized task instructions. For metadata, task script, patient characteristics, video frame rate, and patient demographics were deemed mandatory. Feasibility varied by domain, highest for hand movements and lowest for eye movements. Most panelists identified underrepresentation of patient diversity and insufficient reference standards as key limitations in current studies. Notably, none of the mandatory elements requires additional hardware or substantial setup. Conclusion : This consensus-defined framework establishes measurement-grade video as a prerequisite for reliable and scalable computer vision-based movement analysis, providing an immediate, infrastructure-independent foundation for digital biomarker development in neurology.
Source: Research Square
Authors: Jane Alty, Gianluca Amprimo, Jung Hwan Shin, Max Wuehr, Michal Novotny, Helga Haberfehlner, Claudia Ferraris, Renjie Li, Marcelo Merello, Marina de Koning-Tijssen, Samuel Relton, David Wong, Johannes Taeger, Andreas Zwergal, Sebastian Walther, Jochen Weishaupt, Karl Georg Häusler, Jan Rusz, Babak Taati, Ryan Roemmich, Martin McKeown, Anoopum S. Gupta, Maximilian U. Friedrich
Institutions: University of Tasmania, Politecnico di Torino, Seoul National University Hospital, LMU Klinikum, Deutsches Schwindel- und Gleichgewichtszentrum, Ludwig-Maximilians-Universität München, Czech Technical University in Prague, Amsterdam University Medical Centers, Amsterdam Movement Sciences, Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad de Buenos Aires, University of Groningen, University of Leeds, HNO-Praxis, Universitätsklinikum Würzburg, University Hospital Ulm, University Health Network, Toronto Western Hospital, KITE Research Institute, Kennedy Krieger Institute, Vancouver Coastal Health, University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Harvard University, Massachusetts General Hospital
Language: English
Topic: Delphi Technique in Research
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7154956319