Deep convolutional neural network algorithm for the automatic segmentation of oral potentially malignant disorders and oral cancers.
Proc Inst Mech Eng H
; 237(6): 719-726, 2023 Jun.
Article
en En
| MEDLINE
| ID: mdl-37222098
ABSTRACT
This study aimed to develop an algorithm to automatically segment the oral potentially malignant diseases (OPMDs) and oral cancers (OCs) of all oral subsites with various deep convolutional neural network applications. A total of 510 intraoral images of OPMDs and OCs were collected over 3 years (2006-2009). All images were confirmed both with patient records and histopathological reports. Following the labeling of the lesions the dataset was arbitrarily split, using random sampling in Python as the study dataset, validation dataset, and test dataset. Pixels were classified as the OPMDs and OCs with the OPMD/OC label and the rest as the background. U-Net architecture was used and the model with the best validation loss was chosen for the testing among the trained 500 epochs. Dice similarity coefficient (DSC) score was noted. The intra-observer ICC was found to be 0.994 while the inter-observer reliability was 0.989. The calculated DSC and validation accuracy across all clinical images were 0.697 and 0.805, respectively. Our algorithm did not maintain an excellent DSC due to multiple reasons for the detection of both OC and OPMDs in oral cavity sites. A better standardization for both 2D and 3D imaging (such as patient positioning) and a bigger dataset are required to improve the quality of such studies. This is the first study which aimed to segment OPMDs and OCs in all subsites of oral cavity which is crucial not only for the early diagnosis but also for higher survival rates.
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Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Neoplasias de la Boca
/
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
/
Screening_studies
Límite:
Humans
Idioma:
En
Revista:
Proc Inst Mech Eng H
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Chipre