Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT &
[EN] Our investigation explored the intricacies of airway evaluation through Cone-Beam Computed Tomography (CBCT) and Computed Tomography (CT) images. By employing innovative data augmentation strategies, we expanded our dataset significantly, enabling a more comprehensive analysis of airway charact...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/229663 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/229663 |
| Access Level: | acceso abierto |
| Palabra clave: | Airway assessment CBCT images CT images Data augmentation Mallampati classification Recurrent neural network K-means clustering Machine learning Image preprocessing Airway evaluation |
| Sumario: | [EN] Our investigation explored the intricacies of airway evaluation through Cone-Beam Computed Tomography (CBCT) and Computed Tomography (CT) images. By employing innovative data augmentation strategies, we expanded our dataset significantly, enabling a more comprehensive analysis of airway characteristics. The utility of these techniques was evident in their ability to yield a diverse array of synthetic images, each representing different airway scenarios with high fidelity. A notable outcome of our study was the effective categorization of the initial image as "Class II" under the Mallampati Classification system. The augmented images further enhanced our understanding by exhibiting a spectrum of airway parameters. Moreover, our approach included training a Recurrent Neural Network (RNN) model on a dataset of CT images. This model, fortified with pseudo-labels created via K-means clustering, showcased its proficiency by accurately predicting airway assessment categories in various test scenarios. These results underscore the model's potential as a tool for swift and precise airway evaluation in clinical settings, marking a significant advancement in medical imaging technologies. |
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