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.

Applications and challenges of neural networks in otolaryngology (Review)

“…This form of training requires specific parameter adjustments. The dataset is generally divided into two sets, namely the training set and the testing set, with the most common division being 80% for training and 20% for testing (65). The training process uses the input data and the output data (labeled 'da-ta') to learn patterns.…”

“…One was used to detect osteomeatal complex occlusion, one was used to predict the location of the anterior ethmoid artery and the other was used to identify a concha bullosa at the level of the osteomeatal complex. However, all three of the models had the same underlying purpose, which was to reduce the incidence of intraoperative complications (65)(66)(67)(68). The CNN developed to detect the ostemeatal complex occlusion specifically incorporated individual two-dimensional coronal CT images.…”

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.…”

Exploring the Role of Artificial Intelligence Chatbots in Preoperative Counseling for Head and Neck Cancer Surgery

“…AI is increasingly employed in medicine, ranging from diagnostics and treatment planning to drug development, thanks to its unparalleled capacity for pattern recognition using large volumes of data. In otolaryngology, for instance, AI and machine learning have been used for screening, diagnosis, and treatment decision in rhinology, 5 otology, 6 laryngology, 7 and head and neck oncology 8 . We have also previously demonstrated the utility of AI for predicting head and neck melanoma patients with a low risk of nodal metastasis 9 and identifying patients with oral cavity squamous cell carcinoma at risk for occult nodal disease 10 …”