Evaluation of artificial intelligence model for crowding categorization and extraction diagnosis using intraoral photographs

Determining the severity of dental crowding and the necessity of tooth extraction for orthodontic treatment planning are time-consuming processes and there are no firm criteria. Thus, automated assistance would be useful to clinicians. This study aimed to construct and evaluate artificial intelligen...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2023-03, Vol.13 (1), p.5177-5177, Article 5177
Main Authors: Ryu, Jiho, Kim, Ye-Hyun, Kim, Tae-Woo, Jung, Seok-Ki
Format: Article
Language:English
Subjects:
639/705/117
692/699/3020
Artificial Intelligence
Decision making
Deep learning
Diagnosis
Humanities and Social Sciences
Humans
Malocclusion - diagnosis
Malocclusion - therapy
Maxilla
multidisciplinary
Neural networks
Orthodontics
Performance evaluation
Photography, Dental
Science
Science (multidisciplinary)
Teeth
Tooth
Tooth Extraction
Tooth extractions
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Determining the severity of dental crowding and the necessity of tooth extraction for orthodontic treatment planning are time-consuming processes and there are no firm criteria. Thus, automated assistance would be useful to clinicians. This study aimed to construct and evaluate artificial intelligence (AI) systems to assist with such treatment planning. A total of 3,136 orthodontic occlusal photographs with annotations by two orthodontists were obtained. Four convolutional neural network (CNN) models, namely ResNet50, ResNet101, VGG16, and VGG19, were adopted for the AI process. Using the intraoral photographs as input, the crowding group and the necessity of tooth extraction were obtained. Arch length discrepancy analysis with AI-detected landmarks was used for crowding categorization. Various statistical and visual analyses were conducted to evaluate the performance. The maxillary and mandibular VGG19 models showed minimum mean errors of 0.84 mm and 1.06 mm for teeth landmark detection, respectively. Analysis of Cohen’s weighted kappa coefficient indicated that crowding categorization performance was best in VGG19 (0.73), decreasing in the order of VGG16, ResNet101, and ResNet50. For tooth extraction, the maxillary VGG19 model showed the highest accuracy (0.922) and AUC (0.961). By utilizing deep learning with orthodontic photographs, dental crowding categorization and diagnosis of orthodontic extraction were successfully determined. This suggests that AI can assist clinicians in the diagnosis and decision making of treatment plans.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-32514-7