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Computational Intelligence in Otorhinolaryngology

1 Department of ENT, Institute of Naval Medicine, INHS Asvini, Mumbai, Maharashtra, India
2 Department of Neurotology, Madras ENT Research Foundation, Chennai, Tamil Nadu, India
3 Family Clinic, SHO, Mumbai, Maharashtra, India

Correspondence Address:
Sunil Mathews,
Institute of Naval Medicine, INHS Asvini, Mumbai, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmms.jmms_159_22

There have been major advancements in the field of artificial intelligence (AI) in the last few decades and its use in otorhinolaryngology has seen promising results. In machine learning, which is a subset of AI, computers learn from historical data to gather insights and they make diagnoses about new input data, based on the information it has learned. The objective of this study was to provide a comprehensive review of current applications, future possibilities, and limitations of AI, with respect to the specialty of otorhinolaryngology. A search of the literature was performed using PubMed and Medline search engines. Search terms related to AI or machine learning in otorhinolaryngology were identified and queried to select recent and relevant articles. AI has implications in various areas of otorhinolaryngology such as automatically diagnosing hearing loss, improving performance of hearing aids, restoring speech in paralyzed individuals, predicting speech and language outcomes in cochlear implant candidates, diagnosing various otology conditions using otoscopic images, training in otological surgeries using virtual reality simulator, classifying and quantifying opacification in computed tomography images of paranasal sinuses, distinguishing various laryngeal pathologies based on laryngoscopic images, automatically segmenting anatomical structures to accelerate radiotherapy planning, and assisting pathologist in reporting of thyroid cytopathology. The results of various studies show that machine learning might be used by general practitioners, in remote areas where specialist care is not readily available and as a supportive diagnostic tool in otorhinolaryngology setups, for better diagnosis and faster decision-making.

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