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Background <p>Accurate assessment of upper limb function in children with unilateral cerebral palsy (UCP) is essential for clinical decision-making. Although the Melbourne Assessment 2 (MA2) has been widely adopted to achieve this, this method requires substantial evaluator training and lengthy administration times, and is susceptible to inter-rater variability. To address these challenges, we evaluated the feasibility of employing AI-driven automated video analysis to objectively classify the severity of upper limb impairment in pediatric UCP using retrospectively collected MA2 videos captured under realistic, non-standardized clinical conditions.</p> Methods <p>This study retrospectively enrolled 19 children with UCP (mean age 5.50 ± 2.03 years; 10 males, nine females) who underwent repeated MA2 assessments, yielding 644 candidate item-videos. After pre-specified quality control, the final analytic set comprised 616 item-videos. We fine-tuned four index-specific Video Vision Transformer (ViViT) models—one per MA2 index—and evaluated them using subject-wise five-fold cross-validation to predict dichotomized severity (mild vs. severe) for range of motion (ROM), accuracy, fluency, and dexterity. Videos were recorded with handheld devices without standardized protocols, reflecting typical clinical practice.</p> Results <p>The AI model demonstrated a mean area under the curve (AUC) of 0.890, with the highest performance for dexterity (AUC = 0.936), and similarly high performances for ROM (AUC = 0.866), accuracy (AUC = 0.887), and fluency (AUC = 0.869). Item-wise analyses showed that a limited subset of MA2 tasks contributed disproportionately to the correct classification of mild versus severe cases across indices, highlighting the importance of item-level variability when designing automated MA2 assessment protocols.</p> Conclusions <p>This preliminary study showed that AI-driven automated video analysis can classify dichotomized MA2-based upper limb impairment severity using routinely acquired, nonstandardized clinical videos of children with UCP. However, further methodological refinement—including standardized recording guidelines, prediction models that extend beyond binary labels to ordinal or item-level MA2 scoring, and prospective validation in larger and more diverse cohorts—is required to confirm clinical applicability and enhance generalizability.</p> <p><i>Trial registration</i> Not applicable.</p>

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AI-based video analysis for the assessment of upper limb function in children with unilateral cerebral palsy: feasibility of remote monitoring

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月3日

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  • Youngsub Hwang,
  • Hakje Yoo,
  • Minkyung Kim,
  • Myung Jin Chung,
  • Jeong-Yi Kwon

摘要

Background

Accurate assessment of upper limb function in children with unilateral cerebral palsy (UCP) is essential for clinical decision-making. Although the Melbourne Assessment 2 (MA2) has been widely adopted to achieve this, this method requires substantial evaluator training and lengthy administration times, and is susceptible to inter-rater variability. To address these challenges, we evaluated the feasibility of employing AI-driven automated video analysis to objectively classify the severity of upper limb impairment in pediatric UCP using retrospectively collected MA2 videos captured under realistic, non-standardized clinical conditions.

Methods

This study retrospectively enrolled 19 children with UCP (mean age 5.50 ± 2.03 years; 10 males, nine females) who underwent repeated MA2 assessments, yielding 644 candidate item-videos. After pre-specified quality control, the final analytic set comprised 616 item-videos. We fine-tuned four index-specific Video Vision Transformer (ViViT) models—one per MA2 index—and evaluated them using subject-wise five-fold cross-validation to predict dichotomized severity (mild vs. severe) for range of motion (ROM), accuracy, fluency, and dexterity. Videos were recorded with handheld devices without standardized protocols, reflecting typical clinical practice.

Results

The AI model demonstrated a mean area under the curve (AUC) of 0.890, with the highest performance for dexterity (AUC = 0.936), and similarly high performances for ROM (AUC = 0.866), accuracy (AUC = 0.887), and fluency (AUC = 0.869). Item-wise analyses showed that a limited subset of MA2 tasks contributed disproportionately to the correct classification of mild versus severe cases across indices, highlighting the importance of item-level variability when designing automated MA2 assessment protocols.

Conclusions

This preliminary study showed that AI-driven automated video analysis can classify dichotomized MA2-based upper limb impairment severity using routinely acquired, nonstandardized clinical videos of children with UCP. However, further methodological refinement—including standardized recording guidelines, prediction models that extend beyond binary labels to ordinal or item-level MA2 scoring, and prospective validation in larger and more diverse cohorts—is required to confirm clinical applicability and enhance generalizability.

Trial registration Not applicable.