Application of deep learning to classify skeletal growth phase on 3D radiographs

Abstract

Cervical vertebral maturation (CVM) is widely used to evaluate growth potential in the field of orthodontics. The aim of this study is to develop an artificial intelligence (AI) algorithm to automatically predict the CVM stages in terms of growth phases using the cone-beam computed tomographic (CBCT) images. A total of 30,016 slices obtained from 56 patients with the age range of 7-16 years were included in the dataset. After cropping the region of interest (ROI), a convolutional neural network (CNN) was built to classify the slices based on the presence of a good vision of vertebrae for classification of the growth stages. The output was used to train another model capable of categorizing the slices into phases of growth, which were defined as Phase I (prepubertal, CVM stages 1 and 2), phase II (circumpubertal, CVM stage 3), and phase III (postpubertal, CVM stages 4, 5, and 6). After training the model, 88 unused images belonging to 3 phases were used to evaluate the performance of the model using multi-class classification metrics. The average classification accuracy of the first and second CNN-based deep learning models were 96.06% and 95.79%, respectively on the validation dataset. The multi-class classification metrics applied to the new testing dataset also showed an overall accuracy of 84% for predicting the growth phase. Moreover, phase I ranked the highest accuracy in terms of F1 score (87%), followed by phase II (83%), and phase III (80%) on new images. Our proposed models could automatically detect the C2-C4 vertebrae required for CVM staging and accurately classify slices into 3 growth phases without the need for annotating the shape and configuration of vertebrae. This will result in developing a fully automatic and less complex system with reasonable performance, comparable to expert practitioners.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Not Applicable. Research was not funded by grants or commercial funding for any of the authors.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

University of Alberta HREB Approval - Pro00118171

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Data Availability

Code is available upon request to corresponding author. Images are not available due to privacy.

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