Applications and challenges of neural networks in otolaryngology (Review)

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.

Mixed Reality Biopsy Navigation System Utilizing Markerless Needle Tracking and Imaging Data Superimposition

“…Artificial intelligence can be an important supporting tool for mixed reality systems in the medical technology field. It is currently being utilized in various specialities, including oncology, radiology, radiotherapy, and surgery [27]. Deep learning models can be used to, e.g., detect cancer at an early stage [28], improve image quality, decide on important imaging examinations [29], predict spatial dose distribution [30], and automatically identify surgical phases or instruments [31].…”