International journal of computer assisted radiology and surgery · 2025
AI-enhanced predictive modeling for treatment duration and personalized treatment planning of cleft lip and palate therapy.
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Aharonyan Artur, Anwar Syed, Choo HyeRan
Аннотация
BACKGROUND: Alveolar molding plate treatment (AMPT) plays a critical role in preparing neonates with cleft lip and palate (CLP) for the first reconstruction surgery (cleft lip repair). However, determining the number of adjustments to AMPT in near-normalizing cleft deformity prior to surgery is a challenging task, often affecting the treatment duration. This study explores the use of machine learning in predicting treatment duration based on three-dimensional (3D) assessments of the pre-treatment maxillary cleft deformity as part of individualized treatment planning.
METHODS: Digital 3D models of maxillary arches were collected from 35 infants with unilateral CLP. Key anatomical landmarks were labeled on the models, and the distances between these landmarks were calculated and fed into the model as features. A multi-layer perceptron (MLP) neural network was trained on this data and applied to predict the treatment duration. The model's performance was evaluated using regression metrics such as mean absolute error (MAE), Pearson's correlation, and coefficient of determination (R-squared: R2), to assess predictive accuracy.
RESULTS: Performance metrics of our model revealed a correlation of 0.96, R2 of 0.91, and a mean absolute error of 3.03 days. The most significant features influencing the predictions were landmarks around the alveolar gap and distances delineating the overall alveolar gap width.
CONCLUSION: The results suggest that our model can reliably predict the treatment duration required for AMPT in neonates with unilateral CLP with a potential to contribute to developing a fully personalized yet efficient AI-based treatment pipeline.
METHODS: Digital 3D models of maxillary arches were collected from 35 infants with unilateral CLP. Key anatomical landmarks were labeled on the models, and the distances between these landmarks were calculated and fed into the model as features. A multi-layer perceptron (MLP) neural network was trained on this data and applied to predict the treatment duration. The model's performance was evaluated using regression metrics such as mean absolute error (MAE), Pearson's correlation, and coefficient of determination (R-squared: R2), to assess predictive accuracy.
RESULTS: Performance metrics of our model revealed a correlation of 0.96, R2 of 0.91, and a mean absolute error of 3.03 days. The most significant features influencing the predictions were landmarks around the alveolar gap and distances delineating the overall alveolar gap width.
CONCLUSION: The results suggest that our model can reliably predict the treatment duration required for AMPT in neonates with unilateral CLP with a potential to contribute to developing a fully personalized yet efficient AI-based treatment pipeline.