Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement
Issued Date
2024-12-01
Resource Type
ISSN
17237785
eISSN
21961042
Scopus ID
2-s2.0-85204089363
Pubmed ID
39279025
Journal Title
Progress in Orthodontics
Volume
25
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Progress in Orthodontics Vol.25 No.1 (2024)
Suggested Citation
Kanchanapiboon P., Tunksook P., Tunksook P., Ritthipravat P., Boonpratham S., Satravaha Y., Chaweewannakorn C., Peanchitlertkajorn S. Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement. Progress in Orthodontics Vol.25 No.1 (2024). doi:10.1186/s40510-024-00535-1 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/101322
Title
Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Objectives: This study aimed to assess the accuracy of machine learning (ML) models with feature selection technique in classifying cervical vertebral maturation stages (CVMS). Consensus-based datasets were used for models training and evaluation for their model generalization capabilities on unseen datasets. Methods: Three clinicians independently rated CVMS on 1380 lateral cephalograms, resulting in the creation of five datasets: two consensus-based datasets (Complete Agreement and Majority Voting), and three datasets based on a single rater’s evaluations. Additionally, landmarks annotation of the second to fourth cervical vertebrae and patients’ information underwent a feature selection process. These datasets were used to train various ML models and identify the top-performing model for each dataset. These models were subsequently tested on their generalization capabilities. Results: Features that considered significant in the consensus-based datasets were consistent with a CVMS guideline. The Support Vector Machine model on the Complete Agreement dataset achieved the highest accuracy (77.4%), followed by the Multi-Layer Perceptron model on the Majority Voting dataset (69.6%). Models from individual ratings showed lower accuracies (60.4–67.9%). The consensus-based training models also exhibited lower coefficient of variation (CV), indicating superior generalization capability compared to models from single raters. Conclusion: ML models trained on consensus-based datasets for CVMS classification exhibited the highest accuracy, with significant features consistent with the original CVMS guidelines. These models also showed robust generalization capabilities, underscoring the importance of dataset quality.