腦性麻痺與動作發展異常為高危險嬰幼兒及早產兒常見之神經發展問題。由於其腦部發育尚未成熟，較易受到缺氧、感染及腦部損傷等因素影響，進而導致動作與姿勢控制異常，並可能合併感覺、認知、語言及行為等問題，對日常生活功能與社會參與造成長期影響，亦增加家庭與醫療體系之照護負擔。然而，傳統臨床診斷多須待明顯動作異常出現後方能確認，往往錯失神經可塑性最高的早期介入時機，因此，建立有效的早期辨識方法具有重要臨床意義。 嬰幼兒早期自發性動作可反映中樞神經系統之功能完整性，其中動作的流暢度、協調性與變化性，與後續神經發展預後密切相關。一般動作評估（General Movement Assessment， GMA）雖已被證實具良好預測效度，但其高度仰賴專業訓練與臨床經驗，限制了大規模推廣與臨床實務應用。基於此，本研究結合嬰幼兒早期自發性動作影像與人工智慧分析技術，透過骨架姿態特徵擷取與機器學習建模，探討早期動作表現與後續動作發展結果之關聯性，期望建立人工智慧輔助之分析方法，作為高危險嬰幼兒及早產兒早期發展風險評估之依據。 本研究共納入 9 名高危險嬰幼兒及早產兒，蒐集其新生兒期自發性動作影片，並追蹤矯正年齡 6 個月、12 個月及 24 個月之粗大動作發展結果。利用 OpenPose 進行骨架姿態擷取與特徵分析，結合出生週數、出生體重及性別等臨床資料，建立人工智慧預測模型，以評估早期動作表現對後續發展結果之預測能力。 研究結果顯示，部分預測模型最高準確率可達 93.33%，顯示嬰兒早期自發性動作具有預測後續動作發展之潛力。其中，角度位移特徵（AngDisp）於短期預測表現最佳，而相對關節方向特徵（Rel_JO）於中長期預測展現較佳表現，顯示動作協調性、姿勢控制及肢體間相對關係可能為反映神經發展的重要指標。此外，出生體重對模型表現具有一定輔助效果，但動作特徵對預測效能之影響較臨床變數更為顯著。 綜合而言，本研究證實高危險嬰幼兒及早產兒之早期自發性動作與後續發展結果具有關聯性，人工智慧結合動作影像分析可提供客觀之預測依據，具作為臨床早期篩檢與追蹤輔助工具之潛力。未來仍需擴大樣本數並進行多中心驗證，以提升模型之穩定性與臨床應用價值。;Cerebral palsy and motor developmental abnormalities are common neurodevelopmental disorders in high-risk and preterm infants. Due to the immaturity of brain development， these infants are more susceptible to hypoxia， infection， and brain injury， which may lead to impairments in motor and postural control. In addition， sensory， cognitive， language， and behavioral deficits may coexist， resulting in long-term limitations in daily functioning and social participation and imposing a substantial burden on families and healthcare systems. However， conventional clinical diagnosis is often delayed until obvious motor abnormalities become apparent， thereby missing the critical period of maximal neuroplasticity for early intervention. Therefore， establishing an effective method for early identification is of considerable clinical importance. Early spontaneous movements reflect the functional integrity of the central nervous system， and their fluency， coordination， and variability are closely associated with later neurodevelopmental outcomes. Although General Movement Assessment (GMA) has been demonstrated to possess good predictive validity， its implementation relies heavily on specialized training and clinical experience， limiting its application in large-scale screening and routine clinical practice. Therefore， this study aimed to investigate the relationship between early spontaneous motor performance and subsequent developmental outcomes by integrating spontaneous movement video analysis with artificial intelligence techniques. A total of nine high-risk and preterm infants were enrolled in this study. Spontaneous movement videos recorded during the neonatal period were collected， and gross motor developmental outcomes were followed up at corrected ages of 6， 12， and 24 months. Skeletal pose information was extracted using OpenPose， followed by feature extraction and data preprocessing. Machine learning models were constructed by incorporating kinematic features and clinical variables， including gestational age， birth weight， and sex. The results demonstrated that several prediction tasks achieved a maximum accuracy of 93.33%， indicating the potential of early spontaneous movements for predicting later motor development. Among the extracted features， angular displacement (AngDisp) exhibited superior performance in short-term prediction， whereas relative joint orientation (Rel_JO) demonstrated greater robustness in mid- and long-term prediction. These findings suggest that movement coordination， postural control， and inter-limb relationships may serve as important indicators of neurodevelopmental outcomes. In addition， gestational age contributed positively to model accuracy， whereas kinematic characteristics exerted a greater influence on predictive performance than clinical variables. In conclusion， the findings of this study indicate that early spontaneous movements in high-risk and preterm infants are closely associated with subsequent motor developmental outcomes. Artificial intelligence-based motion analysis provides an objective and quantitative approach for early developmental risk assessment and has the potential to serve as an adjunctive tool for clinical screening and follow-up. Future studies with larger sample sizes and multicenter validation are warranted to further improve model robustness and enhance its clinical applicability.
Keywords: high-risk infants， preterm infants， cerebral palsy， General Movement Assessment (GMA)， artificial intelligence modeling， early motor image analysis
