Tuesday, January 6, 2026 1/6/2026

Development of deep learning methods to optimize patient personalized treatment for craniosynostosis

Award Number: F31DE033614
ORGANIZATION: NATIONAL INSTITUTE OF DENTAL AND CRANIOFACIAL RESEARCH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS
PERIOD OF PERFORMANCE START DATE: 08/01/2024
PERIOD OF PERFORMANCE END DATE: 08/15/2025

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Development of deep learning methods to optimize patient personalized treatment for craniosynostosis - Project Summary/Abstract Craniosynostosis is the premature fusion of one or more cranial sutures. The growth at the cranial plates normally separated by the fused suture is arrested, resulting in compensatory overgrowth parallel to the fused suture. This abnormal development causes head malformations and can lead to increased intracranial pressure and developmental complications. Patients with this condition normally undergo surgical treatment to remove the growth constraints and create more aesthetically normative head shapes. However, the traditional evaluation of these patients after treatment has been based on subjective clinical expertise. During the last decade, three- dimensional (3D) photogrammetry has gained popularity to evaluate craniofacial anomalies, but existing methods to analyze this data have failed in their clinical translation due to the use of inefficient and inaccurate processing techniques. Hence, there is a lack of quantitative evidence to evaluate and compare the continuous process of head shape normalization between different surgical treatments. This proposal aims to develop new geometric deep learning methods to enable the fully automated, real-time evaluation of head shapes using 3D photogrammetry at the clinic and will address the lack of quantitative evidence in the objective assessment of surgical outcomes. The first aim of this proposal is to characterize the normalization of head shape after corrective surgery for different treatment approaches. A statistical model of head shape normalization for each surgical approach will be created to quantify novel metrics of head shape anomaly and probabilistic risk of craniosynostosis. This model will be the first to incorporate the effects of age at surgery, sex, and the pre-surgical severity of head shape anomalies on the normalization of head shape. The second aim of this proposal is to develop a personalized geometric deep learning framework to determine the optimal surgical approach for each patient with craniosynostosis. This aim will incorporate a novel context-encoding geometric deep learning method to estimate the expected post-surgical head shape normalization for each potential surgical treatment and identify the optimal surgical treatment for every patient based on objective retrospective data. The results from these aims will enable the data-driven, personalized, and objective assessment of surgical treatment for craniosynostosis that can be used to optimize patient management. This proposal includes a comprehensive training plan consisting of mentored computational training in the development of deep learning methods, mentored clinical and translational research training at Children’s Hospital Colorado, and didactic coursework in statistical and machine learning methods. This proposal is uniquely positioned to be successful given the multidisciplinary environment at the University of Colorado Anschutz Medical Campus with resources in translational research at Children’s Hospital Colorado and machine learning expertise within the Medical Image Phenotyping lab through the Department of Biostatistics and Informatics.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = -$29,906 )
2026	2025	THE REGENTS OF THE UNIVERSITY OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Oral Diseases and Disorders Research	000	2	11/26/2025	NON-COMPETING CONTINUATION	-$29,906
														Subtotal = -$29,906

Issue Date FY: 2025 ( Subtotal = $38,285 )
2025	2025	THE REGENTS OF THE UNIV. OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Oral Diseases and Disorders Research	000	2	7/28/2025	NON-COMPETING CONTINUATION	$38,285
														Subtotal = $38,285

Issue Date FY: 2024 ( Subtotal = $37,721 )
2024	2024	THE REGENTS OF THE UNIVERSITY OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Oral Diseases and Disorders Research	001	1	7/8/2024	NEW	$0
2024	2024	THE REGENTS OF THE UNIVERSITY OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Oral Diseases and Disorders Research	000	1	7/8/2024	NEW	$37,721
														Subtotal = $37,721

Grand Total All Awards = $46,100

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Development of deep learning methods to optimize patient personalized treatment for craniosynostosis

Award Number: F31DE033614

ORGANIZATION: NATIONAL INSTITUTE OF DENTAL AND CRANIOFACIAL RESEARCH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

PERIOD OF PERFORMANCE START DATE: 08/01/2024

PERIOD OF PERFORMANCE END DATE: 08/15/2025

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