Integrating Machine Vision in Rhinology: The Advent of Automated Diagnostic Systems and their Clinical Implications
Year: 2025
Type: article
Study design: Systematic Review
Abstract: Objective: The purpose of this review is to provide a comprehensive analysis of the potential transformational impact of machine vision in the field of rhinology, specifically examining its implications for both diagnosis and treatment.Methodology: A thorough review of the literature was performed on databases, which include PubMed, Google Scholar, Medline, and DOAJ.By employing a combination of keywords and MeSH phrases, we were able to identify academic articles that reveal an association between the areas of rhinology and machine vision.Out of an initial selection of 42 publications, a total of 30 were carefully selected using rigorous inclusion and exclusion criteria.Results: The integration of machine vision technology in the rhinology field provides a major improvement in diagnosis accuracy and the development of treatment strategies.The review emphasized the importance of supervised learning, especially deep learning methods, in the analysis and interpretation of rhinological images.However, there remain persistent issues that primarily result from the limited accessibility of thoroughly annotated datasets.Conclusion: The application of machine vision technology presents an opportunity for improving the accuracy of rhinological diagnoses and treatment approaches.The success of this endeavor depends on the building of synergistic relationships between professionals in artificial intelligence (AI) and clinical practitioners, along with the collection of datasets of superior quality.The integration of human capabilities and technological advancement is anticipated to create a transformative effect on patient care in the field of rhinology as it keeps on evolving.
Institutions: INHS Asvini, Command Hospital Air Force
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409366372