Shanghai Jiao Tong University team uses deep learning for motor assessment to promote early screening of cerebral palsy

Editor | Cabbage Leaf

Prechtl Global Movement Assessment (GMA) is increasingly recognized for its role in assessing the developmental integrity of the nervous system and predicting motor dysfunction. , especially in diseases such as cerebral palsy (CP). However, the need for trained professionals has hindered the adoption of GMA as an early screening tool in some countries.

In the latest study, researchers from Shanghai Jiao Tong University proposed a deep learning-based movement assessment model (MAM), which combines infant videos and basic features, and is designed to detect fussy movements (FM). ) stage to realize GMA automation.

MAM demonstrated strong performance, achieving an area under the curve (AUC) of 0.967 during external validation. Importantly, it strictly follows the principles of GMA and has strong interpretability, as it can accurately identify FM in videos, which is basically consistent with expert evaluation.

Using the predicted FM frequency, the quantitative GMA method was introduced, and its AUC reached 0.956, improving the diagnostic accuracy of GMA novice doctors by 11.0%. The development of MAM has the potential to significantly simplify early CP screening and revolutionize the field of quantitative video-based medical diagnosis.

The study was titled "Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy" and was published in "Nature Communications" on December 14, 2023.

Please click the following link to view the paper details: https://www.nature.com/articles/s41467-023-44141-x

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