Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy

Qiang Gao, Siqiong Yao, Yuan Tian, Chuncao Zhang, Tingting Zhao, Dan Wu, Guangjun Yu () and Hui Lu ()
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Qiang Gao: Shanghai Jiao Tong University
Siqiong Yao: Shanghai Jiao Tong University
Yuan Tian: Shanghai Jiao Tong University
Chuncao Zhang: Shanghai Jiao Tong University
Tingting Zhao: Shanghai Jiao Tong University
Dan Wu: Shanghai Jiao Tong University
Guangjun Yu: Shanghai Jiao Tong University
Hui Lu: Shanghai Jiao Tong University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract The Prechtl General Movements Assessment (GMA) is increasingly recognized for its role in evaluating the integrity of the developing nervous system and predicting motor dysfunctions, particularly in conditions such as cerebral palsy (CP). However, the necessity for highly trained professionals has hindered the adoption of GMA as an early screening tool in some countries. In this study, we propose a deep learning-based motor assessment model (MAM) that combines infant videos and basic characteristics, with the aim of automating GMA at the fidgety movements (FMs) stage. MAM demonstrates strong performance, achieving an Area Under the Curve (AUC) of 0.967 during external validation. Importantly, it adheres closely to the principles of GMA and exhibits robust interpretability, as it can accurately identify FMs within videos, showing substantial agreement with expert assessments. Leveraging the predicted FMs frequency, a quantitative GMA method is introduced, which achieves an AUC of 0.956 and enhances the diagnostic accuracy of GMA beginners by 11.0%. The development of MAM holds the potential to significantly streamline early CP screening and revolutionize the field of video-based quantitative medical diagnostics.

Date: 2023
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DOI: 10.1038/s41467-023-44141-x

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