Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study
Mohammad-Rahimi Hossein, Motamadian Saeed Reza, Nadimi Mohadeseh, Hassanzadeh-Samani Sahel, Minabi Mohammad A. S., Mahmoudinia Erfan, Lee Victor Y., Rohban Mohammad Hossein,
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( Mohammad-Rahimi Hossein ) - Sharif University of Technology Department of Computer Engineering
( Motamadian Saeed Reza ) - Shahid Beheshti University of Medical Sciences School of Dentistry Department of Orthodontics
( Nadimi Mohadeseh ) - Tehran University of Medical Sciences Department of Medical Physics and Biomedical Engineering
( Hassanzadeh-Samani Sahel ) - Shahid Beheshti University of Medical Sciences Research Institute of Dental Sciences Dentofacial Deformities Research Center
( Minabi Mohammad A. S. ) - Sirjan University of Technology Department of Computer Engineering
( Mahmoudinia Erfan ) - Sharif University of Technology Department of Computer Engineering
( Lee Victor Y. ) - Private practice
( Rohban Mohammad Hossein ) - Sharif University of Technology Department of Computer Engineering
Abstract
Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs.
Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model’s performance using weighted kappa and Cohen’s kappa statistical analyses.
Results: The model’s validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model’s validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model.
Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.
키워드
Computer algorithm; Growth evaluation; Cervical vertebrae; Artificial intelligence
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