# Learning-Based Approach for Personalized Craniomaxillofacial Surgical Planning

> **NIH NIH R01** · METHODIST HOSPITAL RESEARCH INSTITUTE · 2021 · $574,725

## Abstract

Abstract:
Our main clinical objective for this project is to provide personalized precision care to patients with
craniomaxillofacial (CMF) deformities by significantly improving the surgical planning method. CMF
deformities involve congenital and acquired deformities of the jaws and face. A large number of patients in the
US and around the world suffer from CMF deformities. The basic principles of CMF surgery involve the
restoration of deformed CMF structures back to normal anatomy and functions with osteotomy, autologous, bone
grafts, or vascularized free flaps. The success of CMF surgery depends on not only the technical aspects of the
operation, but also, to a large extent, the precise formulation of a surgical plan. However, CMF surgical planning
is extremely challenging due to the complex nature of CMF anatomy and deformity. During a routine CMF
surgical planning, a surgeon first acquires a three-dimensional (3D) model of the patient's skull. He then performs
3D cephalometric analysis to quantify the deformity. Finally, the surgery is simulated by virtually cutting the 3D
model into multiple bony segments. The surgeon then tries his best to move and rotate each segment individually
to a desired position within the normal range of cephalometric values (the current standard of care). This is
problematic as “normal” cephalometric values are the averageness of normal population, in which each value
has a mean and a standard deviation. Due to the variation within the normal values, the surgeon must often
guess what the exact value the patient's cephalometric measurement should be corrected to. In addition,
cephalometry is a group of only linear and angular measurements, which certainly cannot represent the complex
nature of human CMF structures. Therefore, surgical outcomes are often subjective and heavily dependent on
the surgeons' experience and artistic talent. Because each human face is different, the average “normal
values” cannot represent the complex morphology of each individual face.
To this end, we hypothesize that if a surgeon can foresee what the normal CMF shape of the patient should be,
the surgical plan will be objective and personalized. Therefore, in this project, we propose developing and
validating a new surgical planning method of using patient-specific and anatomically-correct reference models.
The feasibility of our approach has already been proven by our preliminary studies. The results of this project
will significantly improve the quality of patient care by developing personalized and precise surgical
plans for CMF surgery objectively. The results will be especially beneficial to patients with jaw deformities,
syndromic and non-syndromic craniofacial deformities, trauma, and CMF cancer. In the future, our approach can
also be used to design and print 3D patient-specific resorbable bone implants with tissue engineering capability
for bone regeneration.

## Key facts

- **NIH application ID:** 10197880
- **Project number:** 5R01DE027251-05
- **Recipient organization:** METHODIST HOSPITAL RESEARCH INSTITUTE
- **Principal Investigator:** JAIME GATENO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $574,725
- **Award type:** 5
- **Project period:** 2017-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10197880

## Citation

> US National Institutes of Health, RePORTER application 10197880, Learning-Based Approach for Personalized Craniomaxillofacial Surgical Planning (5R01DE027251-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10197880. Licensed CC0.

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