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

• Published in: Otolaryngology-head and neck surgery
• Main Authors: Wu, Qingwu, Wang, Xinyue, Liang, Guixian, Luo, Xin, Zhou, Min, Deng, Huiyi, Zhang, Yana, Huang, Xuekun, Yang, Qintai
• Format: Journal Article
• Language: English
• Published: England 01.11.2023
• Subjects: computed tomography; otolaryngology; endoscopic images; machine learning; artificial intelligence; medical imaging; rhinology
• ISSN: 1097-6817
• DOI: 10.1002/ohn.391

To update the literature and provide a systematic review of image-based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges. Web of Science, Embase, PubMed, and Cochrane Library. Studies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies. Overall, 686 studies were identified. After screening titles and abstracts, 325 full-text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively. This state-of-the-art review aimed to highlight the increasing applications of image-based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real-world clinical practice. Future studies should consider 3-dimensional (3D)-based AI, such as 3D surgical AI.