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Orthodontic craniofacial pattern diagnosis: cephalometric geometry and machine learning

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Abstract

Efficient and reliable diagnosis of craniofacial patterns is critical to orthodontic treatment. Although machine learning (ML) is time-saving and high-precision, prior knowledge should validate its reliability. This study proposed a craniofacial ML diagnostic workflow base on a cephalometric geometric model through clinical verification. A cephalometric geometric model was established to determine the landmark location by analyzing 408 X-ray lateral cephalograms. Through geometric information and feature engineering, nine supervised ML algorithms were conducted for sagittal and vertical skeleton patterns. After dimension reduction, plane decision boundary and landmark contribution contours were depicted to demonstrate the diagnostic consistency and the consistency with clinical norms. As a result, multi-layer perceptron achieved 97.56% accuracy for sagittal, while linear support vector machine reached 90.24% for the vertical. Sagittal diagnoses showed average superiority (91.60 ± 5.43)% over the vertical (82.25 ± 6.37)%, where discriminative algorithms exhibited more steady performance (93.20 ± 3.29)% than the generative (85.98 ± 9.48)%. Further, the Kruskal-Wallis H test was carried out to explore statistical differences in diagnoses. Though sagittal patterns had no statistical difference in diagnostic accuracy, the vertical showed significance. All aspects of the tests indicated that the proposed craniofacial ML workflow was highly consistent with clinical norms and could supplement practical diagnosis.

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Data availability

The datasets generated and analyzed during the current study are not publicly available due to the sensitive nature of patient privacy in this study, but are available from the corresponding author on reasonable request.

Abbreviations

ML:

machine learning

MPA:

mandibular plane angle

FH:

Frankfort horizontal plane

SN:

sella-nasion plane

COVID-19:

coronavirus disease

IoT:

Internet of Things

NN:

neural network

SVM:

support vector machine

CNN:

convolutional NN

HRA:

horizontal reference axis

VRA:

vertical reference axis

ANB:

subspinale-nasion-supramental angle

MP:

mandibular plane

MP-SN:

angle of the MP-SN

FH-MP:

angle of the FH-MP

G-algs:

generative algorithms

D-algs:

discriminative algorithms

KNN:

K-nearest neighbor

GaussianNB:

Gaussian Naive Bayes

MLP:

multi-layer perceptron

GPC:

Gaussian process classifier

XGBoost:

extreme gradient boosting

Adaboost:

adaptive boosting

QDA:

quadratic discriminant analysis

RF:

random forest

ROC:

receiver-operating characteristic curve

AUC:

area under the ROC

TPR:

true-positive rate

FPR:

false-positive rate

NTF:

number of top-ranking features

SD:

standard deviation

CI:

confidence interval

Truncated SVD:

truncated singular value decomposition

t-SNE:

t-distributed stochastic neighbor embedding

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Funding

This work was supported by the National Natural Science Foundation of China (grant number 62076011) and the Research Foundation of Peking University School and Hospital of Stomatology, Peking, China (grant number PKUSS20200114).

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Authors and Affiliations

  1. Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing, 100081, China

    Yuqing Zhou, Jiwu Zhang & Qiguo Rong

  2. Department of Orthodontics, Peking University School of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing, 100081, China

    Bochun Mao, Yanheng Zhou & Jing Li

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Contributions

Yuqing Zhou proposed the methodology, conducted the investigation and data analysis, and wrote the original/revised manuscript. Bochun Mao, Jiwu Zhang, and Jing Li acquired resources and curated data. Jiwu Zhang, Yanheng Zhou, Jing Li, and Qiguo Rong contributed to the conception. Jing Li and Qiguo Rong helped with manuscript editing and revision. All authors approved the submitted version.

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Correspondence to Jing Li or Qiguo Rong.

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The institutional review board approved the retrospective study of Peking University School and Hospital of Stomatology (no. PKUSSIRB-202058135). All the patients received orthodontic treatment and informed consent to this study.

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Not applicable.

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The authors declare no competing interests.

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Zhou, Y., Mao, B., Zhang, J. et al. Orthodontic craniofacial pattern diagnosis: cephalometric geometry and machine learning. Med Biol Eng Comput 61, 3345–3361 (2023). https://doi.org/10.1007/s11517-023-02919-7

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