The Journal of pediatrics · 2026
Outcomes and Predictive Modeling in Helmet Therapy for Plagiocephaly.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Stanek Krystof, Berry Jay G, Wynne James H, Stringfellow Isabel, Balkin Daniel M
Аннотация
OBJECTIVE: To develop and evaluate a machine learning model to forecast treatment-related changes in cranial vault asymmetry index (CVAI) and cranial index (CI) in infants undergoing cranial orthosis therapy for deformational plagiocephaly (DP) and/or deformational brachycephaly (DB).
STUDY DESIGN: This retrospective analysis used serial measurements of CVAI and CI for 6694 infants with DP and/or DB at initiation and throughout cranial orthosis therapy from April 1, 2012 to October 31, 22 across cranial orthosis health services in 9 US states. Extreme Gradient Boosting was employed to predict CVAI and CI at the end of therapy based on initial cranial measurements, patient age, and duration of treatment.
RESULTS: At initial presentation for helmet therapy, the median (IQR) age of infants in months was 6 (5-8), with 36.5% (n = 2446) having isolated DP, 9.0% (n = 600) isolated DB, and 54.5% (n = 3648) both DP and DB. Initial median CVAI and CI were 7.6% (IQR: 6.2%-9.4%) and 95% (IQR: 92.6%-98.5%), respectively. The Extreme Gradient Boosting models demonstrated high predictive accuracy for final CVAI (explaining 79.4% of the variance [R2 = 0.794] and a mean absolute error of 0.756) and CI (86.4% of the variance [R2 = 0.864] with a mean absolute error of 0.936).
CONCLUSIONS: A large, retrospective cohort analysis of patients undergoing cranial molding therapy for DP and/or DB enabled development of a machine learning-based model that forecasts treatment-related changes in cranial shape during helmet therapy. Further investigation is needed to assess how this predictive tool may assist in clinical decision-making and informed parental counseling regarding cranial orthosis for DP and/or DB.
STUDY DESIGN: This retrospective analysis used serial measurements of CVAI and CI for 6694 infants with DP and/or DB at initiation and throughout cranial orthosis therapy from April 1, 2012 to October 31, 22 across cranial orthosis health services in 9 US states. Extreme Gradient Boosting was employed to predict CVAI and CI at the end of therapy based on initial cranial measurements, patient age, and duration of treatment.
RESULTS: At initial presentation for helmet therapy, the median (IQR) age of infants in months was 6 (5-8), with 36.5% (n = 2446) having isolated DP, 9.0% (n = 600) isolated DB, and 54.5% (n = 3648) both DP and DB. Initial median CVAI and CI were 7.6% (IQR: 6.2%-9.4%) and 95% (IQR: 92.6%-98.5%), respectively. The Extreme Gradient Boosting models demonstrated high predictive accuracy for final CVAI (explaining 79.4% of the variance [R2 = 0.794] and a mean absolute error of 0.756) and CI (86.4% of the variance [R2 = 0.864] with a mean absolute error of 0.936).
CONCLUSIONS: A large, retrospective cohort analysis of patients undergoing cranial molding therapy for DP and/or DB enabled development of a machine learning-based model that forecasts treatment-related changes in cranial shape during helmet therapy. Further investigation is needed to assess how this predictive tool may assist in clinical decision-making and informed parental counseling regarding cranial orthosis for DP and/or DB.