Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography.
BMC Oral Health
; 24(1): 952, 2024 Aug 16.
Article
in En
| MEDLINE
| ID: mdl-39152384
ABSTRACT
BACKGROUND:
We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography.METHODS:
Panoramic radiographs of 418 teeth (group 1 209 normal teeth; group 2 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 311.RESULTS:
To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively.CONCLUSION:
We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tooth Extraction
/
Radiography, Panoramic
/
Deep Learning
Limits:
Humans
Language:
En
Journal:
BMC Oral Health
Journal subject:
ODONTOLOGIA
Year:
2024
Document type:
Article
Country of publication: