Details
| Original language | English |
|---|---|
| Article number | 45 |
| Journal | Head and Face Medicine |
| Volume | 20 |
| Issue number | 1 |
| Early online date | 2 Sept 2024 |
| Publication status | Published - 2024 |
Abstract
Background: To support dentists with limited experience, this study trained and compared six convolutional neural networks to detect crossbites and classify non-crossbite, frontal, and lateral crossbites using 2D intraoral photographs. Methods: Based on 676 photographs from 311 orthodontic patients, six convolutional neural network models were trained and compared to classify (1) non-crossbite vs. crossbite and (2) non-crossbite vs. lateral crossbite vs. frontal crossbite. The trained models comprised DenseNet, EfficientNet, MobileNet, ResNet18, ResNet50, and Xception. Findings: Among the models, Xception showed the highest accuracy (98.57%) in the test dataset for classifying non-crossbite vs. crossbite images. When additionally distinguishing between lateral and frontal crossbites, average accuracy decreased with the DenseNet architecture achieving the highest accuracy among the models with 91.43% in the test dataset. Conclusions: Convolutional neural networks show high potential in processing clinical photographs and detecting crossbites. This study provides initial insights into how deep learning models can be used for orthodontic diagnosis of malocclusions based on intraoral 2D photographs.
Keywords
- Artificial intelligence, Crossbite, Deep learning, Neural networks, Orthodontic diagnosis
ASJC Scopus subject areas
- Medicine(all)
- Otorhinolaryngology
- Dentistry(all)
- Medicine(all)
- Clinical Neurology
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In: Head and Face Medicine, Vol. 20, No. 1, 45, 2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Comparison of deep learning models to detect crossbites on 2D intraoral photographs
AU - Noeldeke, Beatrice
AU - Vassis, Stratos
AU - Sefidroodi, Mohammedreza
AU - Pauwels, Ruben
AU - Stoustrup, Peter
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - Background: To support dentists with limited experience, this study trained and compared six convolutional neural networks to detect crossbites and classify non-crossbite, frontal, and lateral crossbites using 2D intraoral photographs. Methods: Based on 676 photographs from 311 orthodontic patients, six convolutional neural network models were trained and compared to classify (1) non-crossbite vs. crossbite and (2) non-crossbite vs. lateral crossbite vs. frontal crossbite. The trained models comprised DenseNet, EfficientNet, MobileNet, ResNet18, ResNet50, and Xception. Findings: Among the models, Xception showed the highest accuracy (98.57%) in the test dataset for classifying non-crossbite vs. crossbite images. When additionally distinguishing between lateral and frontal crossbites, average accuracy decreased with the DenseNet architecture achieving the highest accuracy among the models with 91.43% in the test dataset. Conclusions: Convolutional neural networks show high potential in processing clinical photographs and detecting crossbites. This study provides initial insights into how deep learning models can be used for orthodontic diagnosis of malocclusions based on intraoral 2D photographs.
AB - Background: To support dentists with limited experience, this study trained and compared six convolutional neural networks to detect crossbites and classify non-crossbite, frontal, and lateral crossbites using 2D intraoral photographs. Methods: Based on 676 photographs from 311 orthodontic patients, six convolutional neural network models were trained and compared to classify (1) non-crossbite vs. crossbite and (2) non-crossbite vs. lateral crossbite vs. frontal crossbite. The trained models comprised DenseNet, EfficientNet, MobileNet, ResNet18, ResNet50, and Xception. Findings: Among the models, Xception showed the highest accuracy (98.57%) in the test dataset for classifying non-crossbite vs. crossbite images. When additionally distinguishing between lateral and frontal crossbites, average accuracy decreased with the DenseNet architecture achieving the highest accuracy among the models with 91.43% in the test dataset. Conclusions: Convolutional neural networks show high potential in processing clinical photographs and detecting crossbites. This study provides initial insights into how deep learning models can be used for orthodontic diagnosis of malocclusions based on intraoral 2D photographs.
KW - Artificial intelligence
KW - Crossbite
KW - Deep learning
KW - Neural networks
KW - Orthodontic diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85202951295&partnerID=8YFLogxK
U2 - 10.1186/s13005-024-00448-8
DO - 10.1186/s13005-024-00448-8
M3 - Article
C2 - 39223562
AN - SCOPUS:85202951295
VL - 20
JO - Head and Face Medicine
JF - Head and Face Medicine
SN - 1746-160X
IS - 1
M1 - 45
ER -