Deep-SITAR: A SITAR-Based Deep Learning Framework for Growth Curve Modeling via Autoencoders

• URL: http://arxiv.org/abs/2505.09506v1
• Date: Wed, 14 May 2025 15:55:16 GMT
• Title: Deep-SITAR: A SITAR-Based Deep Learning Framework for Growth Curve Modeling via Autoencoders
• Authors: María Alejandra Hernández, Oscar Rodriguez, Dae-Jin Lee,
• Abstract summary: We introduce a supervised deep learning framework based on an autoencoder architecture that integrates a deep neural network (neural network) with a B-spline model to estimate the SITAR model.<n>Deep-SITAR offers a powerful approach to predicting growth trajectories, combining the flexibility and efficiency of deep learning with the interpretability of traditional mixed-effects models.
• Score: 1.274952786182905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Several approaches have been developed to capture the complexity and nonlinearity of human growth. One widely used is the Super Imposition by Translation and Rotation (SITAR) model, which has become popular in studies of adolescent growth. SITAR is a shape-invariant mixed-effects model that represents the shared growth pattern of a population using a natural cubic spline mean curve while incorporating three subject-specific random effects -- timing, size, and growth intensity -- to account for variations among individuals. In this work, we introduce a supervised deep learning framework based on an autoencoder architecture that integrates a deep neural network (neural network) with a B-spline model to estimate the SITAR model. In this approach, the encoder estimates the random effects for each individual, while the decoder performs a fitting based on B-splines similar to the classic SITAR model. We refer to this method as the Deep-SITAR model. This innovative approach enables the prediction of the random effects of new individuals entering a population without requiring a full model re-estimation. As a result, Deep-SITAR offers a powerful approach to predicting growth trajectories, combining the flexibility and efficiency of deep learning with the interpretability of traditional mixed-effects models.

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