@article{nokey,

title = {Craniofacial growth percentile curves: A clinical tool from the Craniofacial Growth Consortium Study},

author = {Heesoo Oh and Kevin M. Middleton and Manish Valiathan and Dana L. Duren and Kieran P. McNulty and James A. McNamara Jr. and Mark Hans and Richard J. Sherwood},

doi = {https://doi.org/10.1016/j.ajodo.2025.11.026},

issn = {0889-5406},

year  = {2026},

date = {2026-03-04},

journal = {American Journal of Orthodontics and Dentofacial Orthopedics},

abstract = {This study aimed to develop new cephalometric standards incorporating samples from across North America, focusing on creating sex-specific percentile growth curves for craniofacial cephalometric measures using the extensive longitudinal data from the Craniofacial Growth Consortium Study. This study comprised 2100 subjects (1056 males and 1044 females) with 17,290 lateral cephalometric radiographs, spanning ages of 2.5-31.3 years. Twenty-four linear cephalometric measurements were calculated, including traits from the basicranium, maxilla, and mandible. For each measurement, multilevel nonlinear growth models were used to estimate growth milestones by sex, percentile growth curves were created, and a web-interface tool was developed to facilitate the practical application of these growth curves. Growth milestones, including age at peak growth velocity and peak growth velocity, were estimated for each sex using double logistic growth models. The timing of peak growth velocity varies across different regions of the craniofacial complex. Craniofacial growth percentile curves were created and cross-validated. A web interface tool was created to allow users to retrieve individual-specific percentile scores. The developed percentile growth curves and web-based tool offer a robust framework for clinicians to assess individual growth patterns, identify deviations from normative growth, and estimate future growth potential, supporting more personalized treatment planning and timing. },

keywords = {cephalometrics, clinical tool, craniofacial growth, growth modeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokeyi,

title = {Factors influencing the predictive performance of artificial intelligence for craniofacial growth},

author = {Naeun Kwona and Jong-Hak Kima and Heeyeon Suh and Heesoo Oh and Shin-Jae Lee},

url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/96/1/article-p106.xml?isSearch=true},

doi = {10.2319/031025-197.1},

year  = {2025},

date = {2025-09-29},

urldate = {2025-09-29},

journal = {Angle Orthodontist},

volume = {96},

issue = {1},

pages = {106-113},

abstract = {Objectives: To evaluate factors influencing the prediction error of artificial intelligence (AI) that predict craniofacial growth and to identify an optimal AI training condition to improve the predictive performance of the AI model. Materials and Methods: Original growth data were collected from the Mathews longitudinal serial growth study. From the original data consisting of 1257 datasets from 33 growing children of northern European descent, 60 data subsets were generated using random resampling procedures to include 12, 18, and 24 subjects, with data sizes of 100, 200, 300, 400, and 500 datasets. The resampling procedures were repeated four times. Each subset was used to train and create a total of 60 AI models. The prediction accuracy of these models was evaluated using growth prediction errors at the lower lip landmark, labrale inferius, as a benchmark indicator. The prediction errors of the 60 AI models were analyzed according to the number of subjects and data sizes. Results: Prediction error decreased as the data size increased. However, increasing the number of subjects within the growth data led to higher prediction errors. Notably, the increase in prediction error caused by adding more subjects was more substantial than the improvement achieved by increasing the data size. Conclusions: The findings suggest that developing highly accurate AI-based craniofacial growth prediction models remains a significant challenge, even with extensive datasets.},

keywords = {artificial intelligence, craniofacial growth, data quantity, individual variability, prediction error},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2022d,

title = {Craniofacial growth and morphology among intersecting clinical categories},

author = {R Knigge and A Hardin and K Middleton and K McNulty and H Oh and M Valiathan and D Duren and R Sherwood},

url = {https://pubmed.ncbi.nlm.nih.gov/35076186/},

doi = {10.1002/ar.24870},

year  = {2022},

date = {2022-02-11},

journal = {Anatomical Record (Hoboken)},

abstract = {Differential patterns of craniofacial growth are important sources of variation that can result in skeletal malocclusion. Understanding the timing of growth milestones and morphological change associated with adult skeletal malocclusions is critical for developing individualized orthodontic growth modification strategies. To identify patterns in the timing and geometry of growth, we used Bayesian modeling of cephalometrics and geometric morphometric analyses with a dense, longitudinal sample consisting of 15,407 cephalograms from 1,913 individuals between 2 and 31 years of age. Individuals were classified into vertical facial types (hyper-, normo-, hypo-divergent) and anteroposterior (A-P) skeletal classes (Class I, Class II, Class III) based on adult mandibular plane angle and ANB angle, respectively. These classifications yielded eight facial type-skeletal class categories with sufficient sample sizes to be included in the study. Four linear cephalometrics representing facial heights and maxillary and mandibular lengths were fit to standard double logistic models generating type-class category-specific estimates for age, size, and rate of growth at growth milestones. Mean landmark configurations were compared among type-class categories at four time points between 6 and 20 years of age. Overall, morphology and growth patterns were more similar within vertical facial types than within A-P classes and variation among A-P classes typically nested within variation among vertical types. Further, type-class-associated variation in the rate and magnitude of growth in specific regions identified here may serve as targets for clinical treatment of complex vertical and A-P skeletal malocclusion and provide a clearer picture of the development of variation in craniofacial form.},

keywords = {cephalometrics, craniofacial growth, geometric morphometrics, growth modeling, malocclusion},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2022i,

title = {Geometric morphometric analysis of growth patterns among facial types},

author = {R Knigge and K McNulty and H Oh and A Hardin and E Leary and D Duren and M Valiathan and R Sherwood},

url = {https://pubmed.ncbi.nlm.nih.gov/34175161/},

doi = {10.1016/j.ajodo.2020.04.038},

year  = {2021},

date = {2021-09-02},

journal = {American Journal of Orthodontics & Dentofacial Orthopedics},

volume = {160},

issue = {3},

pages = {430-441},

abstract = {Introduction: Extreme patterns of vertical facial divergence are of great importance to clinicians because of their association with dental malocclusion and functional problems of the orofacial complex. Understanding the growth patterns associated with vertical facial divergence is critical for clinicians to provide optimal treatment. This study evaluates and compares growth patterns from childhood to adulthood among 3 classifications of vertical facial divergence using longitudinal, lateral cephalograms from the Craniofacial Growth Consortium Study.



Methods: Participants (183 females, 188 males) were classified into 1 of 3 facial types on the basis of their adult mandibular plane angle (MPA): hyperdivergent (MPA >39°; n = 40), normodivergent (28° ≤ MPA ≤ 39°; n = 216), and hypodivergent (MPA <28°; n = 115). Each individual had 5 cephalograms between ages 6 and 20 years. A set of 36 cephalometric landmarks were digitized on each cephalogram. Landmark configurations were superimposed to align 5 homologous landmarks of the anterior cranial base and scaled to unit centroid size. Growth trajectories were calculated using multivariate regression for each facial type and sex combination.



Results: Divergent growth trajectories were identified among facial types, finding more similarities in normodivergent and hypodivergent growth patterns than either share with the hyperdivergent group. Through the use of geometric morphometric methods, new patterns of facial growth related to vertical facial divergence were identified. Hyperdivergent growth exhibits a downward rotation of the maxillomandibular complex relative to the anterior cranial base, in addition to the increased relative growth of the lower anterior face. Conversely, normodivergent and hypodivergent groups exhibit stable positioning of the maxilla relative to the anterior cranial base, with the forward rotation of the mandible. Furthermore, the hyperdivergent maxilla and mandible become relatively shorter and posteriorly positioned with age compared with the other groups.



Conclusions: This study demonstrates how hyperdivergent growth, particularly restricted growth and positioning of the maxilla, results in a higher potential risk for Class II malocclusion. Future work will investigate growth patterns within each classification of facial divergence.},

keywords = {craniofacial consortium study, craniofacial growth, facial types, geometric morphometrics, growth patterns},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2022j,

title = {Estimating Craniofacial Growth Cessation: Comparison of Asymptote- and Rate-Based Methods. },

author = {Hardin AM and Knigge RP and Oh H},

url = {https://pubmed.ncbi.nlm.nih.gov/33998905/},

doi = {10.1177/10556656211002675},

year  = {2021},

date = {2021-02-01},

urldate = {2021-02-01},

journal = {Cleft Palate Craniofacial Journal },

volume = {59},

issue = {2},

pages = {230-238},

abstract = {Objective: To identify differences between asymptote- and rate-based methods for estimating age and size at growth cessation in linear craniofacial measurements.



Design: This is a retrospective, longitudinal study. Five linear measurements were collected from lateral cephalograms as part of the Craniofacial Growth Consortium Study (CGCS). Four estimates of growth cessation, including 2 asymptote- (GCasym, GCerr) and 2 rate-based (GCabs, GC10%) methods, from double logistic models of craniofacial growth were compared.



Participants: Cephalometric data from participants in 6 historic longitudinal growth studies were included in the CGCS. At least 1749 individuals (870 females, 879 males), unaffected by craniofacial anomalies, were included in all analyses. Individuals were represented by a median of 11 images between 2.5 and 31.3 years of age.



Results: GCasym consistently occurred before GCerr and GCabs consistently occurred before GC10% within the rate-based approaches. The ordering of the asymptote-based methods compared to the rate-based methods was not consistent across measurements or between males and females. Across the 5 measurements, age at growth cessation ranged from 13.56 (females, nasion-basion, GCasym) to 24.39 (males, sella-gonion, GCerr).



Conclusions: Adolescent growth cessation is an important milestone for treatment planning. Based on our findings, we recommend careful consideration of specific definitions of growth cessation in both clinical and research settings since the most appropriate estimation method may differ according to patients' needs. The different methods presented here provide useful estimates of growth cessation that can be applied to raw data and to a variety of statistical models of craniofacial growth.},

keywords = {cephalometry, craniofacial growth, craniofacial morphology, facial growth},

pubstate = {published},

tppubtype = {article}

}