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Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants
Shiang-Chin Lin1, Erick Chandra2, Po Nien Tsao3
1School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine, Taipei, Taiwan.
Insights
This study developed an artificial intelligence (AI) model to accurately classify infant movements. The AI model shows promise for assessing neuromotor development in both full-term and preterm infants.
Area of Science:
- Biomedical Engineering
- Developmental Pediatrics
- Artificial Intelligence
Background:
- Preterm infants face a higher risk of neuromotor disorders.
- Advances in AI and digital technology allow for detailed human movement analysis.
- The applicability of adult-focused AI movement models to infant assessment is not well-established.
Purpose of the Study:
- To develop and validate an AI model framework for recognizing infant movements.
- To assess the accuracy of AI-driven infant motor assessment.
- To evaluate the model's effectiveness in distinguishing movements in full-term and preterm infants.
Main Methods:
- An observational study involving 30 full-term and 54 preterm infants aged 4-18 months.
- Movement data collected using 5 synchronized video cameras during Alberta Infant Motor Scale assessments.
- An AI algorithm comprising a 17-point pose estimation and skeleton-based action recognition model was developed and tested.
Main Results:
- 153 assessment sessions yielded 13,139 infant movement videos.
- High intra- and interrater reliability (88%-100%) for manual video annotation.
- The AI algorithm achieved high accuracy (0.91), recall (0.91), precision (0.91), and F1 score (0.91) in classifying 31 key infant movements.
Conclusions:
- The developed AI algorithm accurately classifies 31 infant movements in a clinical setting.
- This AI framework provides a foundation for remote infant movement assessment using home videos.
Objective:
Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whether the proposed pose estimation model and the skeleton-based action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants.
Methods:
This observational study prospectively assessed 30 full-term infants and 54 preterm infants using the Alberta Infant Motor Scale (58 movements) from 4 to 18 months of age with their movements recorded by 5 video cameras simultaneously in a standardized clinical setup. The movement videos were annotated for the start/end times and presence of movements by 3 pediatric physical therapists. The annotated videos were used for the development and testing of an AI algorithm that consisted of a 17-point human pose estimation model and a skeleton-based action recognition model.
Results:
The infants contributed 153 sessions of Alberta Infant Motor Scale assessment that yielded 13,139 videos of movements for data processing. The intra and interrater reliabilities for movement annotation of videos by the therapists showed high agreements (88%-100%). Thirty-one of the 58 movements were selected for machine learning because of sufficient data samples and developmental significance. Using the annotated results as the standards, the AI algorithm showed satisfactory agreement in classifying the 31 movements (accuracy = 0.91, recall = 0.91, precision = 0.91, and F1 score = 0.91).
Conclusion:
The AI algorithm was accurate in classifying 31 movements in full-term and preterm infants from 4 to 18 months of age in a standardized clinical setup.
Impact:
The findings provide the basis for future refinement and validation of the algorithm on home videos to be a remote infant movement assessment.
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