Applications and challenges of neural networks in otolaryngology (Review)

Taciuc,Iulian-Alexandru; Dumitru,Mihai; Vrinceanu,Daniela; Gherghe,Mirela; Manole,Felicia; Marinescu,Andreea; Serboiu,Crenguta; Neagos,Adriana; Costache,Adrian

doi:10.3892/br.2024.1781

Applications and challenges of neural networks in otolaryngology (Review)

Authors:
- Iulian-Alexandru Taciuc
- Mihai Dumitru
- Daniela Vrinceanu
- Mirela Gherghe
- Felicia Manole
- Andreea Marinescu
- Crenguta Serboiu
- Adriana Neagos
- Adrian Costache
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Affiliations: Department of Pathology, ‘Carol Davila’ University of Medicine and Pharmacy, 020021 Bucharest, Romania, Department of ENT, ‘Carol Davila’ University of Medicine and Pharmacy, 050751 Bucharest, Romania, Department of Nuclear Medicine, ‘Carol Davila’ University of Medicine and Pharmacy, 022328 Bucharest, Romania, Department of ENT, Faculty of Medicine University of Oradea, 410073 Oradea, Romania, Department of Radiology and Medical Imaging ‘Carol Davila’ University of Medicine and Pharmacy, 050096 Bucharest, Romania, Department of Cell Biology, Molecular and Histology, ‘Carol Davila’ University of Medicine and Pharmacy, 050096 Bucharest, Romania, Department of ENT, ‘George Emil Palade’ University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Mures, Romania
Published online on: April 19, 2024 https://doi.org/10.3892/br.2024.1781
Article Number: 92
Copyright: © Taciuc et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Artificial Intelligence (AI) has become a topic of interest that is frequently debated in all research fields. The medical field is no exception, where several unanswered questions remain. When and how this field can benefit from AI support in daily routines are the most frequently asked questions. The present review aims to present the types of neural networks (NNs) available for development, discussing their advantages, disadvantages and how they can be applied practically. In addition, the present review summarizes how NNs (combined with various other features) have already been applied in studies in the ear nose throat research field, from assisting diagnosis to treatment management. Although the answer to this question regarding AI remains elusive, understanding the basics and types of applicable NNs can lead to future studies possibly using more than one type of NN. This approach may bypass the actual limitations in accuracy and relevance of information generated by AI. The proposed studies, the majority of which used convolutional NNs, obtained accuracies varying 70-98%, with a number of studies having the AI trained on a limited number of cases (<100 patients). The lack of standardization in AI protocols for research negatively affects data homogeneity and transparency of databases.

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