AI-enhanced predictive modeling for treatment duration and personalized treatment planning of cleft lip and palate therapy.

Artur Aharonyan, Syed Anwar, HyeRan Choo

Abstract read

In one paragraph

Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Artur AharonyanDivision of Plastic and Reconstructive Surgery, Neonatal and Pediatric Craniofacial Airway Orthodontics, Department of Surgery, Stanford University School of Medicine, 770 Welch Road, Palo Alto, CA, 94394, USA.

Syed AnwarSheikh Zayed Institute, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, USA. sanwar@childrensnational.org.ORCID http://orcid.org/0000-0002-8179-3959

HyeRan ChooDivision of Plastic and Reconstructive Surgery, Neonatal and Pediatric Craniofacial Airway Orthodontics, Department of Surgery, Stanford University School of Medicine, 770 Welch Road, Palo Alto, CA, 94394, USA. hchoo@stanford.edu.

Funding

School of Medicine, Stanford University 292590

6 · The paper itself

Abstract

backgroundAlveolar 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.

methodsDigital 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: R

resultsPerformance metrics of our model revealed a correlation of 0.96, R

conclusionThe 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.

Indexed as

Cleft LipCleft PalateImaging, Three-DimensionalMachine LearningPatient Care PlanningPlastic Surgery ProceduresFemaleHumansInfantInfant, NewbornMaleNeural Networks, Computer3D STL modelsAlveolar molding plateCleft lip and palateConstriction screw adjustmentsDeep learningPersonalized treatment planningTreatment duration

Identifiers

PMID40924362

DOI10.1007/s11548-025-03515-w

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.