YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.
BMC Med Imaging
; 24(1): 172, 2024 Jul 11.
Article
en En
| MEDLINE
| ID: mdl-38992601
ABSTRACT
OBJECTIVES:
In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.METHODS:
A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed.RESULTS:
The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation.CONCLUSIONS:
YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Diente
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Radiografía Panorámica
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Odontología Pediátrica
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Dentición Mixta
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Aprendizaje Profundo
Límite:
Adolescent
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Child
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Child, preschool
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Female
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Humans
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Male
Idioma:
En
Revista:
BMC Med Imaging
/
BMC med. imaging
/
BMC medical imaging
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2024
Tipo del documento:
Article
País de afiliación:
Turquía