Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis
(1) Background: The early detection of laryngeal cancer is crucial for achieving superior patient outcomes and preserving laryngeal function. Artificial intelligence (AI) methodologies can expedite the triage of suspicious laryngeal lesions, thereby diminishing the critical timeframe required for cl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Current Oncology |
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| Online Access: | https://www.mdpi.com/1718-7729/32/6/338 |
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| author | Ali Alabdalhussein Mohammed Hasan Al-Khafaji Rusul Al-Busairi Shahad Al-Dabbagh Waleed Khan Fahid Anwar Taghreed Sami Raheem Mohammed Elkrim Raguwinder Bindy Sahota Manish Mair |
| author_facet | Ali Alabdalhussein Mohammed Hasan Al-Khafaji Rusul Al-Busairi Shahad Al-Dabbagh Waleed Khan Fahid Anwar Taghreed Sami Raheem Mohammed Elkrim Raguwinder Bindy Sahota Manish Mair |
| author_sort | Ali Alabdalhussein |
| collection | DOAJ |
| description | (1) Background: The early detection of laryngeal cancer is crucial for achieving superior patient outcomes and preserving laryngeal function. Artificial intelligence (AI) methodologies can expedite the triage of suspicious laryngeal lesions, thereby diminishing the critical timeframe required for clinical intervention. (2) Methods: We included all studies published up to February 2025. We conducted a systematic search across five major databases: MEDLINE, EMCARE, EMBASE, PubMed, and the Cochrane Library. We included 15 studies, with a total of 17,559 patients. A risk of bias assessment was performed using the QUADAS-2 tool. We conducted data synthesis using the Meta Disc 1.4 program. (3) Results: A meta-analysis revealed that AI demonstrated high sensitivity (78%) and specificity (86%), with a Pooled Diagnostic Odds Ratio of 53.77 (95% CI: 27.38 to 105.62) in detecting laryngeal cancer. The subset analysis revealed that CNN-based AI models are superior to non-CNN-based models in image analysis and lesion detection. (4) Conclusions: AI can be used in real-world settings due to its diagnostic accuracy, high sensitivity, and specificity. |
| format | Article |
| id | doaj-art-92f4f6c8f66a4a49b33a366b5d7c26c6 |
| institution | Matheson Library |
| issn | 1198-0052 1718-7729 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Current Oncology |
| spelling | doaj-art-92f4f6c8f66a4a49b33a366b5d7c26c62025-06-25T13:41:33ZengMDPI AGCurrent Oncology1198-00521718-77292025-06-0132633810.3390/curroncol32060338Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-AnalysisAli Alabdalhussein0Mohammed Hasan Al-Khafaji1Rusul Al-Busairi2Shahad Al-Dabbagh3Waleed Khan4Fahid Anwar5Taghreed Sami Raheem6Mohammed Elkrim7Raguwinder Bindy Sahota8Manish Mair9Department of Otolaryngology, University Hospitals of Leicester, Leicester LE1 5WW, UKDepartment of Otolaryngology, University Hospitals of Leicester, Leicester LE1 5WW, UKIndependent Researcher, Leicester LE2 2AD, UKIndependent Researcher, Leicester LE2 2AD, UKDepartment of Otolaryngology, University Hospitals of Leicester, Leicester LE1 5WW, UKDepartment of Maxillofacial Surgery, University Hospitals of Leicester, Leicester LE1 5WW, UKIndependent Researcher, Leicester LE2 2AD, UKDepartment of Otolaryngology, University Hospitals of Leicester, Leicester LE1 5WW, UKDepartment of Otolaryngology, University Hospitals of Derby and Burton, Derby DE22 3NE, UKDepartment of Maxillofacial Surgery, University Hospitals of Leicester, Leicester LE1 5WW, UK(1) Background: The early detection of laryngeal cancer is crucial for achieving superior patient outcomes and preserving laryngeal function. Artificial intelligence (AI) methodologies can expedite the triage of suspicious laryngeal lesions, thereby diminishing the critical timeframe required for clinical intervention. (2) Methods: We included all studies published up to February 2025. We conducted a systematic search across five major databases: MEDLINE, EMCARE, EMBASE, PubMed, and the Cochrane Library. We included 15 studies, with a total of 17,559 patients. A risk of bias assessment was performed using the QUADAS-2 tool. We conducted data synthesis using the Meta Disc 1.4 program. (3) Results: A meta-analysis revealed that AI demonstrated high sensitivity (78%) and specificity (86%), with a Pooled Diagnostic Odds Ratio of 53.77 (95% CI: 27.38 to 105.62) in detecting laryngeal cancer. The subset analysis revealed that CNN-based AI models are superior to non-CNN-based models in image analysis and lesion detection. (4) Conclusions: AI can be used in real-world settings due to its diagnostic accuracy, high sensitivity, and specificity.https://www.mdpi.com/1718-7729/32/6/338artificial intelligence (AI)machine learninglaryngeal cancerlaryngoscopyotolaryngology |
| spellingShingle | Ali Alabdalhussein Mohammed Hasan Al-Khafaji Rusul Al-Busairi Shahad Al-Dabbagh Waleed Khan Fahid Anwar Taghreed Sami Raheem Mohammed Elkrim Raguwinder Bindy Sahota Manish Mair Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis Current Oncology artificial intelligence (AI) machine learning laryngeal cancer laryngoscopy otolaryngology |
| title | Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis |
| title_full | Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis |
| title_fullStr | Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis |
| title_full_unstemmed | Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis |
| title_short | Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis |
| title_sort | artificial intelligence in laryngeal cancer detection a systematic review and meta analysis |
| topic | artificial intelligence (AI) machine learning laryngeal cancer laryngoscopy otolaryngology |
| url | https://www.mdpi.com/1718-7729/32/6/338 |
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