Clin Exp Otorhinolaryngol.  2020 Nov;13(4):326-339. 10.21053/ceo.2020.00654.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

• Tama BA
• Kim DH
• Kim G
• Kim SW
• Lee S

• KMID: 2508510
• DOI: http://doi.org/10.21053/ceo.2020.00654

Abstract

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.

Keyword

Artificial Intelligence; Machine Learning; Deep Learning; Otorhinolaryngology

Figure

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  Fig. 1. Flowchart of the literature search and study selection.

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  Fig. 2. Interconnections between artificial intelligence, machine learning, and deep learning.

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  Fig. 3. Artificial intelligence (AI) techniques used for medical image-based analysis.

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  Fig. 4. Artificial intelligence (AI) techniques used for voice-based analysis.

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  Fig. 5. Artificial intelligence (AI) analyses of biosignals detected from medical devices. SVM, support vector machine.

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  Fig. 6. Artificial intelligence (AI) techniques used for clinical diagnoses and treatments. EMR, electronic medical record.

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