Determination of the pubertal growth spurt by artificial intelligence analysis of cervical vertebrae maturation in lateral cephalometric radiographs

Fernanda Nogueira-Reis; Deivi Cascante-Sequeira; Amanda Farias-Gomes; Maysa Malfiza Garcia de Macedo; Renato Naville Watanabe; Anderson Gabriel Santiago; Cínthia Pereira Machado Tabchoury; Deborah Queiroz Freitas

doi:10.1016/j.oooo.2024.02.017

Determination of the pubertal growth spurt by artificial intelligence analysis of cervical vertebrae maturation in lateral cephalometric radiographs

Oral Surg Oral Med Oral Pathol Oral Radiol. 2024 Mar 1:S2212-4403(24)00081-6. doi: 10.1016/j.oooo.2024.02.017. Online ahead of print.

Authors

Fernanda Nogueira-Reis¹, Deivi Cascante-Sequeira², Amanda Farias-Gomes², Maysa Malfiza Garcia de Macedo³, Renato Naville Watanabe⁴, Anderson Gabriel Santiago⁴, Cínthia Pereira Machado Tabchoury⁵, Deborah Queiroz Freitas²

Affiliations

¹ Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil. Electronic address: fernanda.nreis@outlook.com.
² Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
³ IBM Research, São Paulo, São Paulo, Brazil.
⁴ Center for Engineering, Modeling and Applied Social Sciences of the Federal University of ABC (UFABC), São Bernardo, São Paulo, Brazil.
⁵ Department of Biosciences, Division of Biochemistry, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.

PMID: 38553310
DOI: 10.1016/j.oooo.2024.02.017

Abstract

Objective: This study aimed to assess the performance of a convolutional neural network (CNN) model in detecting the pubertal growth spurt by analyzing cervical vertebrae maturation (CVM) in lateral cephalometric radiographs (LCRs).

Study design: In total, 600 LCRs of patients from 6 to 17 years old were selected. Three radiologists independently and blindly classified the maturation stages of the LCRs and defined the difficulty of each classification. Subsequently, the stage and level of difficulty were determined by consensus. LCRs were distributed between training, validation, and test datasets across 4 CNN-based models. The models' responses were compared with the radiologists' reference standard, and the architecture with the highest success rate was selected for evaluation. Models were developed using full and cropped LCRs with original and simplified maturation classifications.

Results: In the simplified classification, the Inception-v3 CNN yielded an accuracy of 74% and 75%, with recall and precision values of 61% and 62%, for full and cropped LCRs, respectively. It achieved 61% and 62% total success rates with full and cropped LCRs, respectively, reaching 72.7% for easy-to-classify cropped cases.

Conclusion: Overall, the CNN model demonstrated potential for determining the maturation status regarding the pubertal growth spurt through images of the cervical vertebrae. It may be useful as an initial assessment tool or as an aid for optimizing the assessment and treatment decisions of the clinician.