Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.
Dentomaxillofac Radiol
; 53(4): 256-266, 2024 Apr 29.
Article
en En
| MEDLINE
| ID: mdl-38502963
ABSTRACT
OBJECTIVES:
The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.METHODS:
In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.RESULTS:
F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.CONCLUSIONS:
Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
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Tomografía Computarizada de Haz Cónico
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Seno Maxilar
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Dento-maxillo-facial radiol
/
Dento-maxillo-facial radiology
/
Dentomaxillofac Radiol
Año:
2024
Tipo del documento:
Article
País de afiliación:
Turquía