Computational Intelligence in Otorhinolaryngology
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 n...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2023-01-01
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| Series: | Journal of Marine Medical Society |
| Subjects: | |
| Online Access: | http://www.marinemedicalsociety.in/article.asp?issn=0975-3605;year=2023;volume=25;issue=3;spage=3;epage=10;aulast=Mathews |
| _version_ | 1797738629794103296 |
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| author | Sunil Mathews Ruchima Dham Angshuman Dutta Asha Treesa Jose |
| author_facet | Sunil Mathews Ruchima Dham Angshuman Dutta Asha Treesa Jose |
| author_sort | Sunil Mathews |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T13:45:34Z |
| format | Article |
| id | doaj.art-f0d961dd9be549dfa5bc2a92b1c6365d |
| institution | Directory Open Access Journal |
| issn | 0975-3605 |
| language | English |
| last_indexed | 2024-03-12T13:45:34Z |
| publishDate | 2023-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Marine Medical Society |
| spelling | doaj.art-f0d961dd9be549dfa5bc2a92b1c6365d2023-08-23T09:26:41ZengWolters Kluwer Medknow PublicationsJournal of Marine Medical Society0975-36052023-01-0125331010.4103/jmms.jmms_159_22Computational Intelligence in OtorhinolaryngologySunil MathewsRuchima DhamAngshuman DuttaAsha Treesa JoseThere 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.http://www.marinemedicalsociety.in/article.asp?issn=0975-3605;year=2023;volume=25;issue=3;spage=3;epage=10;aulast=Mathewsartificial intelligenceconvolutional neural networkdeep learninghearinglaryngeal pathologymachine learningthyroid cytopathology |
| spellingShingle | Sunil Mathews Ruchima Dham Angshuman Dutta Asha Treesa Jose Computational Intelligence in Otorhinolaryngology Journal of Marine Medical Society artificial intelligence convolutional neural network deep learning hearing laryngeal pathology machine learning thyroid cytopathology |
| title | Computational Intelligence in Otorhinolaryngology |
| title_full | Computational Intelligence in Otorhinolaryngology |
| title_fullStr | Computational Intelligence in Otorhinolaryngology |
| title_full_unstemmed | Computational Intelligence in Otorhinolaryngology |
| title_short | Computational Intelligence in Otorhinolaryngology |
| title_sort | computational intelligence in otorhinolaryngology |
| topic | artificial intelligence convolutional neural network deep learning hearing laryngeal pathology machine learning thyroid cytopathology |
| url | http://www.marinemedicalsociety.in/article.asp?issn=0975-3605;year=2023;volume=25;issue=3;spage=3;epage=10;aulast=Mathews |
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