The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review

Abstract

Objective To provide a comprehensive overview on the applications of artificial intelligence (AI) in rhinology, highlight its limitations, and propose strategies for its integration into surgical practice. Data Sources Medline, Embase, CENTRAL, Ei Compendex, IEEE, and Web of Science. Review Methods English studies from inception until January 2022 and those focusing on any application of AI in rhinology were included. Study selection was independently performed by 2 authors; discrepancies were resolved by the senior author. Studies were categorized by rhinology theme, and data collection comprised type of AI utilized, sample size, and outcomes, including accuracy and precision among others. Conclusions An overall 5435 articles were identified. Following abstract and title screening, 130 articles underwent full-text review, and 59 articles were selected for analysis. Eleven studies were from the gray literature. Articles were stratified into image processing, segmentation, and diagnostics (n = 27); rhinosinusitis classification (n = 14); treatment and disease outcome prediction (n = 8); optimizing surgical navigation and phase assessment (n = 3); robotic surgery (n = 2); olfactory dysfunction (n = 2); and diagnosis of allergic rhinitis (n = 3). Most AI studies were published from 2016 onward (n = 45). Implications for Practice This state of the art review aimed to highlight the increasing applications of AI in rhinology. Next steps will entail multidisciplinary collaboration to ensure data integrity, ongoing validation of AI algorithms, and integration into clinical practice. Future research should be tailored at the interplay of AI with robotics and surgical education.

International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors

“…Traditional staging systems also may not capture all meaningful prognostic information, and there is a need to rethink and potentially redesign informative staging systems that integrate these critical features (e.g., Hyams grade in ONB, mutational burden). With the rise of artificial intelligence applications in medicine as a whole, systematic assessment of unique indicators of tumor behavior based on radiology and pathology may emerge 2220–2222 . We identify as ongoing research needs in a tumor‐specific manner: Identification of new/alternative strategies, biomarkers, and imaging modalities to more accurately diagnose patients with sinonasal tumors, especially malignancies at an earlier stage. Development of enhanced imaging modalities that evaluate the extent of involvement of sinonasal tumors, in particular orbital and intracranial involvement.…”

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

“…While several reviews have examined AI and its applications in otolaryngology, 2,6‐8 rhinology, 9,10 otological images, 4,5,11 laryngeal cancer, 3,12 and head and neck cancer diagnosis, 13 some of them were based on the data from a few years ago and focused on only some diseases or specialties. With the emergence of new algorithms, it is important to update the literature and provide otolaryngologists with an overview of AI applications in otolaryngology.…”