Automated postural asymmetry assessment in infants neurodevelopmental evaluation using novel video-based features
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Daniel Ledwoń1, Marta Danch-Wierzchowska1, Iwona Doroniewicz2
1Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
Insights
This study introduces an automated method to detect infant positional asymmetry from video recordings, aiding early diagnosis of developmental disorders. The computer vision approach offers objective insights, complementing existing assessments for better motor function outcomes.
Area of Science:
- Medical Imaging
- Developmental Pediatrics
- Computer Vision
Background:
- Infant neurodevelopmental assessment is crucial for early identification of developmental disorders.
- Current diagnostic methods for posture asymmetry rely on subjective expert opinions.
- Computer-aided diagnosis trends focus on limb movement analysis, overlooking positional asymmetry.
Purpose of the Study:
- To develop an automated method for quantifying infant positional asymmetry using computer image processing.
- To establish an objective measure of trunk and head position from video recordings.
- To enhance early detection of developmental disorders through advanced diagnostics.
Main Methods:
- Proposed six quantitative features for trunk and head position based on pose estimation.
- Utilized machine learning algorithms to estimate the percentage of each trunk position.
- Trained and validated the method on 51 research recordings and 12 benchmark recordings.
Main Results:
- The Quadratic Discriminant Analysis (QDA) classifier achieved the lowest log loss (0.552) and highest AUC (0.913).
- The developed method demonstrated high accuracy (92.03%) and sensitivity (93.26%) in classifying asymmetry.
- Results confirm the method's potential for effective infant asymmetry screening.
Conclusions:
- The automated method provides valuable quantitative data on infant positional preferences.
- This approach extends basic diagnostics without requiring additional tools or procedures.
- It can be integrated into a comprehensive computer-aided infant diagnosis system, alongside limb movement analysis.
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.
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