Personalized phenotype encoding and prediction of pathological head development from cross-sectional images

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Artificial Intelligence

Background:

• Accurate prediction of anatomical development is vital for pediatric surgery.
• Young patients' rapid growth and potential pathologies complicate anatomical change prediction.

Purpose of the Study:

• To present a novel deep learning architecture for personalized predictions of normative and pathologic head development.
• To enable accurate predictions using only cross-sectional data, overcoming the need for longitudinal data.

Main Methods:

• Developed a novel phenotype encoder using domain adversarial training for age- and sex-independent representations.
• Created a growth predictor generating head shape based on age, sex, and pathology.
• Trained the model on CT images and 3D photograms, evaluated on longitudinal data.

Main Results:

• Achieved high accuracy in head surface growth prediction (4.93 ± 2.29 mm for pathology, 4.61 ± 3.28 mm for normative).
• Demonstrated low volumetric error (0.16 ± 0.11 L for pathology, 0.27 ± 0.19 L for normative).
• Successfully created age- and sex-agnostic phenotype representations.

Conclusions:

• The proposed deep learning model enables personalized predictions of pathological head development without longitudinal data.
• This approach significantly advances the ability to plan pediatric surgical treatments by accurately forecasting anatomical changes.
• The method offers a breakthrough in creating patient-specific developmental predictions for both healthy and pathologically affected individuals.