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Review
. 2024 Apr 19;20(6):92.
doi: 10.3892/br.2024.1781. eCollection 2024 Jun.

Applications and challenges of neural networks in otolaryngology (Review)

Iulian-Alexandru Taciuc  1 , Mihai Dumitru  2 , Daniela Vrinceanu  2 , Mirela Gherghe  3 , Felicia Manole  4 , Andreea Marinescu  5 , Crenguta Serboiu  6 , Adriana Neagos  7 , Adrian Costache  1
Affiliations
Review

Applications and challenges of neural networks in otolaryngology (Review)

Iulian-Alexandru Taciuc et al. Biomed Rep. .

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.

Keywords: artificial intelligence; head and neck cancer; neural networks; otorhinolaryngology.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Learning process. First, the labeled data is processed by the AI program, which searches for patterns of recognition. When new similar data is given to the AI, it will provide a result based on the recognition patterns developed during the training process. AI, artificial intelligence.
Figure 2
Figure 2
Schematic of a simple neural network. The most basic neural network structure is shown, with three layers, always having the same direction of information propagation.
Figure 3
Figure 3
Ways in which artificial intelligence can help the ENT physician. Summaries of the three main ways in which artificial intelligence can be applied to assist the ENT physician. ENT, ear, nose and throat.
Figure 4
Figure 4
Relevant studies used in the present article. Examples of artificial intelligence used in ear, nose and throat application are shown. NN, neural network; ENT, ear, nose and throat.
See this image and copyright information in PMC

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