Automated postural asymmetry assessment in infants neurodevelopmental evaluation using novel video-based features

Daniel Ledwoń; Marta Danch-Wierzchowska; Iwona Doroniewicz; Katarzyna Kieszczyńska; Alicja Affanasowicz; Dominika Latos; Małgorzata Matyja; Andrzej W Mitas; Andrzej Myśliwiec

doi:10.1016/j.cmpb.2023.107455

Automated postural asymmetry assessment in infants neurodevelopmental evaluation using novel video-based features

Comput Methods Programs Biomed. 2023 May:233:107455. doi: 10.1016/j.cmpb.2023.107455. Epub 2023 Mar 5.

Authors

Daniel Ledwoń¹, Marta Danch-Wierzchowska², Iwona Doroniewicz³, Katarzyna Kieszczyńska³, Alicja Affanasowicz³, Dominika Latos³, Małgorzata Matyja³, Andrzej W Mitas², Andrzej Myśliwiec³

Affiliations

¹ Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland. Electronic address: daniel.ledwon@polsl.pl.
² Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
³ Institute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland.

PMID: 36893565
DOI: 10.1016/j.cmpb.2023.107455

Abstract

Background and objective: Neurodevelopmental assessment enables the identification of infant developmental disorders in the first months of life. Thus, the appropriate therapy can be initiated promptly, increasing the chances for correct motor function. Posture asymmetry is one of the crucial aspects evaluated during the diagnosis. Available diagnostic methods are mainly based on qualitative assessment and subjective expert opinion. Current trends in computer-aided diagnosis focus mostly on analyzing infants' spontaneous movement videos using artificial intelligence methods, based primarily on limbs movement. This study aims to develop an automatic method for determining the infant's positional asymmetry in a video recording using computer image processing methods.

Methods: We made the first attempt to determine positional preferences in a recording automatically. We proposed six quantitative features describing trunk and head position based on pose estimation. As a result of our algorithm, we estimate the percentage of each trunk position in a recording using known machine learning methods. The training and test sets were created from 51 recordings collected during our research and 12 recordings from the benchmark dataset evaluated by five of our experts. The method was assessed using the leave-one-subject-out cross-validation method for ground truth video fragments and different classifiers. Log loss for multiclass classification and ROC AUC were determined to evaluate the results for both our and benchmark datasets.

Results: In a classification of the shortened side, the QDA classifier yields the most accurate results, gaining the lowest log loss of 0.552 and AUC of 0.913. The high accuracy (92.03) and sensitivity (93.26) confirm the method's potential in screening for asymmetry.

Conclusions: The method allows obtaining quantitative information about positional preference, a valuable extension of basic diagnostics without additional tools and procedures. In combination with an analysis of limbs movement, it may constitute one of the elements of a novelty computer-aided infants' diagnosis system in the future.

Keywords: Asymmetry; Computer-aided diagnosis; Features extraction; Infant; Machine learning; Physiotherapy.

MeSH terms

Algorithms
Artificial Intelligence*
Diagnosis, Computer-Assisted / methods
Humans
Infant
Movement
Posture*