Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres

Jaerong Kim, Inhwan Kim, Yoon Ji Kim, Minji Kim, Jin Hyoung Cho, Mihee Hong, Kyung Hwa Kang, Sung Hoon Lim, Su Jung Kim, Young Ho Kim, Namkug Kim, Sang Jin Sung, Seung Hak Baek

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Objective: To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres. Settings and sample population: A total of 3150 lateral cephalograms were acquired from 10 university hospitals in South Korea for training. Materials and Methods: We evaluated the accuracy of the developed model with independent 100 lateral cephalograms as an external validation. Two orthodontists independently identified the anatomic landmarks of the test data set using the V-ceph software (version 8.0, Osstem, Seoul, Korea). The mean positions of the landmarks identified by two orthodontists were regarded as the gold standard. The performance of the CNN model was evaluated by calculating the mean absolute distance between the gold standard and the automatically detected positions. Factors associated with the detection accuracy for landmarks were analysed using the linear regression models. Results: The mean inter-examiner difference was 1.31 ± 1.13 mm. The overall automated detection error was 1.36 ± 0.98 mm. The mean detection error for each landmark ranged between 0.46 ± 0.37 mm (maxillary incisor crown tip) and 2.09 ± 1.91 mm (distal root tip of the mandibular first molar). A significant difference in the detection accuracy among cephalograms was noted according to hospital (P =.011), sensor type (P <.01), and cephalography machine model (P <.01). Conclusion: The automated cephalometric landmark detection model may aid in preliminary screening for patient diagnosis and mid-treatment assessment, independent of the type of the radiography machines tested.

Original languageEnglish
Pages (from-to)59-67
Number of pages9
JournalOrthodontics and Craniofacial Research
Volume24
Issue numberS2
DOIs
StatePublished - Dec 2021

Bibliographical note

Funding Information:
This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute and funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI18C1638).

Publisher Copyright:
© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

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