Prediction of extraction difficulty for impacted maxillary third molars with deep learning approach.
J Stomatol Oral Maxillofac Surg
; 125(4S): 101817, 2024 Sep.
Article
em En
| MEDLINE
| ID: mdl-38458545
ABSTRACT
OBJECTIVE:
The aim of this study is to determine if a deep learning (DL) model can predict the surgical difficulty for impacted maxillary third molar tooth using panoramic images before surgery. MATERIALS ANDMETHODS:
The dataset consists of 708 panoramic radiographs of the patients who applied to the Oral and Maxillofacial Surgery Clinic for various reasons. Each maxillary third molar difficulty was scored based on dept (V), angulation (H), relation with maxillary sinus (S), and relation with ramus (R) on panoramic images. The YoloV5x architecture was used to perform automatic segmentation and classification. To prevent re-testing of images, participate in the training, the data set was subdivided as 80 % training, 10 % validation, and 10 % test group.RESULTS:
Impacted Upper Third Molar Segmentation model showed best success on sensitivity, precision and F1 score with 0,9705, 0,9428 and 0,9565, respectively. S-model had a lesser sensitivity, precision and F1 score than the other models with 0,8974, 0,6194, 0,7329, respectively.CONCLUSION:
The results showed that the proposed DL model could be effective for predicting the surgical difficulty of an impacted maxillary third molar tooth using panoramic radiographs and this approach might help as a decision support mechanism for the clinicians in perisurgical period.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Extração Dentária
/
Dente Impactado
/
Radiografia Panorâmica
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Aprendizado Profundo
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Maxila
/
Dente Serotino
Limite:
Adolescent
/
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
J Stomatol Oral Maxillofac Surg
Ano de publicação:
2024
Tipo de documento:
Article