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Review
. 2025 Jun 9;32(6):338.
doi: 10.3390/curroncol32060338.

Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis

Ali Alabdalhussein  1 , Mohammed Hasan Al-Khafaji  1 , Rusul Al-Busairi  2 , Shahad Al-Dabbagh  2 , Waleed Khan  1 , Fahid Anwar  3 , Taghreed Sami Raheem  2 , Mohammed Elkrim  1 , Raguwinder Bindy Sahota  4 , Manish Mair  3
Affiliations
Review

Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis

Ali Alabdalhussein et al. Curr Oncol. .

Abstract

(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.

Keywords: artificial intelligence (AI); laryngeal cancer; laryngoscopy; machine learning; otolaryngology.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA flow diagram.
Figure 2
Figure 2
(A). Risk of bias and applicability concern summary [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25] and (B). risk of bias and applicability concerns graphs.
Figure 2
Figure 2
(A). Risk of bias and applicability concern summary [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25] and (B). risk of bias and applicability concerns graphs.
Figure 3
Figure 3
The (A)—sensitivity, (B)—specificity, (C)—SROC, and (D)—diagnostic accuracy of included studies.
Figure 3
Figure 3
The (A)—sensitivity, (B)—specificity, (C)—SROC, and (D)—diagnostic accuracy of included studies.
Figure 4
Figure 4
The (A)—sensitivity, (B)—specificity, (C)—SROC, and (D)—diagnostic accuracy of non-CNN-based models.
Figure 4
Figure 4
The (A)—sensitivity, (B)—specificity, (C)—SROC, and (D)—diagnostic accuracy of non-CNN-based models.
Figure 5
Figure 5
The (A)—sensitivity, (B)—specificity, (C)—SROC, and (D)—diagnostic accuracy of CNN-based models.
Figure 5
Figure 5
The (A)—sensitivity, (B)—specificity, (C)—SROC, and (D)—diagnostic accuracy of CNN-based models.
Figure 5
Figure 5
The (A)—sensitivity, (B)—specificity, (C)—SROC, and (D)—diagnostic accuracy of CNN-based models.
Figure 5
Figure 5
The (A)—sensitivity, (B)—specificity, (C)—SROC, and (D)—diagnostic accuracy of CNN-based models.
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