TY - JOUR
T1 - AI-based dental caries and tooth number detection in intraoral photos
T2 - Model development and performance evaluation
AU - Yoon, Kyubaek
AU - Jeong, Hye Min
AU - Kim, Jin Woo
AU - Park, Jung Hyun
AU - Choi, Jongeun
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/2
Y1 - 2024/2
N2 - Objectives: In this study, we aimed to integrate tooth number recognition and caries detection in full intraoral photographic images using a cascade region-based deep convolutional neural network (R-CNN) model to facilitate the practical application of artificial intelligence (AI)-driven automatic caries detection in clinical practice. Methods: Our dataset comprised 24,578 images, encompassing 4787 upper occlusal, 4347 lower occlusal, 5230 right lateral, 5010 left lateral, and 5204 frontal views. In each intraoral image, tooth numbers and, when present, dental caries, including their location and stage, were annotated using bounding boxes. A cascade R-CNN model was used for dental caries detection and tooth number recognition within intraoral images. Results: For tooth number recognition, the model achieved an average mean average precision (mAP) score of 0.880. In the task of dental caries detection, the model's average mAP score was 0.769, with individual scores spanning from 0.695 to 0.893. Conclusions: The primary objective of integrating tooth number recognition and caries detection within full intraoral photographic images has been achieved by our deep learning model. The model's training on comprehensive intraoral datasets has demonstrated its potential for seamless clinical application. Clinical Significance: This research holds clinical significance by achieving AI-driven automatic integration of tooth number recognition and caries detection in full intraoral images where multiple teeth are visible. It has the potential to promote the practical application of AI in real-life and clinical settings.
AB - Objectives: In this study, we aimed to integrate tooth number recognition and caries detection in full intraoral photographic images using a cascade region-based deep convolutional neural network (R-CNN) model to facilitate the practical application of artificial intelligence (AI)-driven automatic caries detection in clinical practice. Methods: Our dataset comprised 24,578 images, encompassing 4787 upper occlusal, 4347 lower occlusal, 5230 right lateral, 5010 left lateral, and 5204 frontal views. In each intraoral image, tooth numbers and, when present, dental caries, including their location and stage, were annotated using bounding boxes. A cascade R-CNN model was used for dental caries detection and tooth number recognition within intraoral images. Results: For tooth number recognition, the model achieved an average mean average precision (mAP) score of 0.880. In the task of dental caries detection, the model's average mAP score was 0.769, with individual scores spanning from 0.695 to 0.893. Conclusions: The primary objective of integrating tooth number recognition and caries detection within full intraoral photographic images has been achieved by our deep learning model. The model's training on comprehensive intraoral datasets has demonstrated its potential for seamless clinical application. Clinical Significance: This research holds clinical significance by achieving AI-driven automatic integration of tooth number recognition and caries detection in full intraoral images where multiple teeth are visible. It has the potential to promote the practical application of AI in real-life and clinical settings.
KW - Artificial intelligence
KW - Caries detection
KW - Deep learning
KW - Intraoral photograph
KW - Tooth numbering
UR - http://www.scopus.com/inward/record.url?scp=85182550983&partnerID=8YFLogxK
U2 - 10.1016/j.jdent.2023.104821
DO - 10.1016/j.jdent.2023.104821
M3 - Article
C2 - 38145804
AN - SCOPUS:85182550983
SN - 0300-5712
VL - 141
JO - Journal of Dentistry
JF - Journal of Dentistry
M1 - 104821
ER -