CLINICAL QUESTION
What are the implications of applying various branches of artificial intelligence (AI) to the subfields of otology, rhinology, laryngology, and head and neck?
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February 2025BOTTOM LINE
Given its progression, present-day implementations, and prospects, the integration of AI into the field of otorhinolaryngology holds promising developments for otorhinolaryngologists and their patients.
BACKGROUND: AI has become a ubiquitous part of human life. Its roles in otorhinolaryngology are varied and evolving and include applications in hearing aids, imaging technologies, robotic surgeries, anomaly determination, and pathology detection. Clinical integration of AI and otorhinolaryngology has immense potential to revolutionize the healthcare system.
STUDY DESIGN: Literature search/narrative review
SETTING: Otolaryngology-Head and Neck Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, India
SYNOPSIS: Researchers mined electronic databases for terms involving AI and its subfields—machine learning, deep learning, and neural networks—in combination with “otology” and “rhinology.” Their report included the following: In otology, the application of AI in vestibular manifestations and vertigo ranges from diagnosis and classification to the current application of virtual reality rehabilitation. Available tools include a diagnostic platform that can offer multiple diagnoses per patient, a virtual reality rehabilitation program, and convolutional neural networks (CNNs) to aid imaging. In rhinology, CNNs have been used to form a detection model for nasopharyngeal carcinoma, and deep learning has been applied to detect sinonasal malignancies. Unsupervised learning has been used in studies to identify patient clusters and categorize them into classes of chronic rhinosinusitis. Image guidance surgery shows evidence of better surgical outcomes, and robotic surgeries have progressed in other otolaryngological fields. In laryngology, an artificial neural network compares the texture and hue between laryngoscope images of non-laryngopharyngeal reflux and laryngopharyngeal reflux disease, and speech analysis systems for voice pathology have utilized machine learning algorithms. Authors say future applications of AI will include 3D virtual reality in rehabilitating vestibular disorder and vertigo, and real-time face mapping and navigation during surgery.
CITATION: Ghosh Moulic A, et al. Artificial intelligence in otology, rhinology, and laryngology: a narrative review of its current and evolving picture. Cureus. 2024;16(8):e66036. doi: 10.7759/cureus.66036.