Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review
The application of machine learning (ML) techniques to otolaryngology remains a topic of interest and prevalence in the literature, though no previous articles have summarized the current state of ML application to management and the diagnosis of lateral skull base (LSB) tumors. Subsequently, we pre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Journal of Otorhinolaryngology, Hearing and Balance Medicine |
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| Online Access: | https://www.mdpi.com/2504-463X/3/4/7 |
| _version_ | 1797455841219051520 |
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| author | Kotaro Tsutsumi Sina Soltanzadeh-Zarandi Pooya Khosravi Khodayar Goshtasbi Hamid R. Djalilian Mehdi Abouzari |
| author_facet | Kotaro Tsutsumi Sina Soltanzadeh-Zarandi Pooya Khosravi Khodayar Goshtasbi Hamid R. Djalilian Mehdi Abouzari |
| author_sort | Kotaro Tsutsumi |
| collection | DOAJ |
| description | The application of machine learning (ML) techniques to otolaryngology remains a topic of interest and prevalence in the literature, though no previous articles have summarized the current state of ML application to management and the diagnosis of lateral skull base (LSB) tumors. Subsequently, we present a systematic overview of previous applications of ML techniques to the management of LSB tumors. Independent searches were conducted on PubMed and Web of Science between August 2020 and February 2021 to identify the literature pertaining to the use of ML techniques in LSB tumor surgery written in the English language. All articles were assessed in regard to their application task, ML methodology, and their outcomes. A total of 32 articles were examined. The number of articles involving applications of ML techniques to LSB tumor surgeries has significantly increased since the first article relevant to this field was published in 1994. The most commonly employed ML category was tree-based algorithms. Most articles were included in the category of surgical management (13; 40.6%), followed by those in disease classification (8; 25%). Overall, the application of ML techniques to the management of LSB tumor has evolved rapidly over the past two decades, and the anticipated growth in the future could significantly augment the surgical outcomes and management of LSB tumors. |
| first_indexed | 2024-03-09T16:00:00Z |
| format | Article |
| id | doaj.art-1c5f1688518a4a9386c50045a97bb80a |
| institution | Directory Open Access Journal |
| issn | 2504-463X |
| language | English |
| last_indexed | 2024-03-09T16:00:00Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Otorhinolaryngology, Hearing and Balance Medicine |
| spelling | doaj.art-1c5f1688518a4a9386c50045a97bb80a2023-11-24T17:10:58ZengMDPI AGJournal of Otorhinolaryngology, Hearing and Balance Medicine2504-463X2022-09-0134710.3390/ohbm3040007Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic ReviewKotaro Tsutsumi0Sina Soltanzadeh-Zarandi1Pooya Khosravi2Khodayar Goshtasbi3Hamid R. Djalilian4Mehdi Abouzari5Department of Otolaryngology—Head and Neck Surgery, University of California Irvine, 333 City Blvd. West, Suite 525, Orange, CA 92868, USADepartment of Otolaryngology—Head and Neck Surgery, University of California Irvine, 333 City Blvd. West, Suite 525, Orange, CA 92868, USADepartment of Otolaryngology—Head and Neck Surgery, University of California Irvine, 333 City Blvd. West, Suite 525, Orange, CA 92868, USADepartment of Otolaryngology—Head and Neck Surgery, University of California Irvine, 333 City Blvd. West, Suite 525, Orange, CA 92868, USADepartment of Otolaryngology—Head and Neck Surgery, University of California Irvine, 333 City Blvd. West, Suite 525, Orange, CA 92868, USADepartment of Otolaryngology—Head and Neck Surgery, University of California Irvine, 333 City Blvd. West, Suite 525, Orange, CA 92868, USAThe application of machine learning (ML) techniques to otolaryngology remains a topic of interest and prevalence in the literature, though no previous articles have summarized the current state of ML application to management and the diagnosis of lateral skull base (LSB) tumors. Subsequently, we present a systematic overview of previous applications of ML techniques to the management of LSB tumors. Independent searches were conducted on PubMed and Web of Science between August 2020 and February 2021 to identify the literature pertaining to the use of ML techniques in LSB tumor surgery written in the English language. All articles were assessed in regard to their application task, ML methodology, and their outcomes. A total of 32 articles were examined. The number of articles involving applications of ML techniques to LSB tumor surgeries has significantly increased since the first article relevant to this field was published in 1994. The most commonly employed ML category was tree-based algorithms. Most articles were included in the category of surgical management (13; 40.6%), followed by those in disease classification (8; 25%). Overall, the application of ML techniques to the management of LSB tumor has evolved rapidly over the past two decades, and the anticipated growth in the future could significantly augment the surgical outcomes and management of LSB tumors.https://www.mdpi.com/2504-463X/3/4/7lateral skull base surgerymachine learningartificial intelligence |
| spellingShingle | Kotaro Tsutsumi Sina Soltanzadeh-Zarandi Pooya Khosravi Khodayar Goshtasbi Hamid R. Djalilian Mehdi Abouzari Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review Journal of Otorhinolaryngology, Hearing and Balance Medicine lateral skull base surgery machine learning artificial intelligence |
| title | Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review |
| title_full | Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review |
| title_fullStr | Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review |
| title_full_unstemmed | Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review |
| title_short | Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review |
| title_sort | machine learning in the management of lateral skull base tumors a systematic review |
| topic | lateral skull base surgery machine learning artificial intelligence |
| url | https://www.mdpi.com/2504-463X/3/4/7 |
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