Add like
Add dislike
Add to saved papers

Detection of tooth numbering, frenulum attachment, gingival overgrowth, and gingival inflammation signs on dental photographs using convolutional neural network algorithms: a retrospective study.

Quintessence International 2023 September 20
OBJECTIVES: This study aimed to develop an artificial intelligence (AI) model that can determine automatic tooth numbering, frenulum attachments, gingival overgrowth areas, and gingival inflammation signs on intraoral photographs and to evaluate the performance of this model.

METHOD AND MATERIALS: A total of 654 intraoral photographs were used in the study (n = 654). All photographs were reviewed by three periodontists, and all teeth, frenulum attachment, gingival overgrowth areas, and gingival inflammation signs on photographs were labeled using the segmentation method in a web-based labeling software. In addition, tooth numbering was carried out according to the FDI system. An AI model was developed with the help of YOLOv5x architecture with labels of 16,795 teeth, 2,493 frenulum attachments, 1,211 gingival overgrowth areas, and 2,956 gingival inflammation signs. The confusion matrix system and ROC (receiver operator characteristic) analysis were used to statistically evaluate the success of the developed model.

RESULTS: The sensitivity, precision, F1 score, and AUC (area under the curve) for tooth numbering were 0.990, 0.784, 0.875, and 0.989; for frenulum attachment these were 0.894, 0.775, 0.830, and 0.827; for gingival overgrowth area these were 0.757, 0.675, 0.714, and 0.774; and for gingival inflammation sign 0.737, 0.823, 0.777, and 0.802, respectively.

CONCLUSION: The results of the present study show that AI systems can be successfully used to interpret intraoral photographs. These systems have the potential to accelerate the digital transformation in the clinical and academic functioning of dentistry with the automatic determination of anatomical structures and dental conditions from intraoral photographs.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app