Open video data sharing in developmental science and clinical practice

In behavioral research and clinical practice video data has rarely been shared or pooled across sites due to ethical concerns of confidentiality, although the need of shared large-scaled datasets remains increasing. This demand is even more imperative when data-heavy computer-based approaches are in...

Full description

Saved in:
Bibliographic Details
Published in:iScience 2023-04, Vol.26 (4), p.106348-106348, Article 106348
Main Authors: Marschik, Peter B., Kulvicius, Tomas, Flügge, Sarah, Widmann, Claudius, Nielsen-Saines, Karin, Schulte-Rüther, Martin, Hüning, Britta, Bölte, Sven, Poustka, Luise, Sigafoos, Jeff, Wörgötter, Florentin, Einspieler, Christa, Zhang, Dajie
Format: Article
Language:English
Subjects:
Clinical neuroscience
Diagnostics
Pediatrics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In behavioral research and clinical practice video data has rarely been shared or pooled across sites due to ethical concerns of confidentiality, although the need of shared large-scaled datasets remains increasing. This demand is even more imperative when data-heavy computer-based approaches are involved. To share data while abiding by privacy protection rules, a critical question arises whether efforts at data de-identification reduce data utility? We addressed this question by showcasing an established and video-based diagnostic tool for detecting neurological deficits. We demonstrated for the first time that, for analyzing infant neuromotor functions, pseudonymization by face-blurring video recordings is a viable approach. The redaction did not affect classification accuracy for either human assessors or artificial intelligence methods, suggesting an adequate and easy-to-apply solution for sharing behavioral video data. Our work shall encourage more innovative solutions to share and merge stand-alone video datasets into large data pools to advance science and public health. [Display omitted] •Face-blurring is an adequate and efficient solution for sharing movement video data•General movement assessment (GMA) can be reliably done on pseudonymized videos with blurred infant faces•Automated GMA delivers comparable classifications with or without head key-points•Innovative solutions are needed for safe, fair, and efficient video data sharing Pediatrics; Diagnostics; Clinical neuroscience
ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2023.106348