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Larynx

Larynx

The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
Anatomy of the Larynx
The larynx consists of various components, including cartilage, muscles, and vocal cords. Its structure includes three large unpaired cartilages—the thyroid, cricoid, and epiglottis—and three smaller paired cartilages—the arytenoids,...

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

Ali Alabdalhussein¹, Mohammed Hasan Al-Khafaji¹, Rusul Al-Busairi²

¹Department of Otolaryngology, University Hospitals of Leicester, Leicester LE1 5WW, UK.

Current Oncology (Toronto, Ont.)

|June 25, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) aids in early laryngeal cancer detection. AI models show high accuracy, sensitivity, and specificity, improving patient outcomes and larynx preservation.

Keywords:

artificial intelligence (AI)laryngeal cancer laryngoscopy machine learning otolaryngology

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Early detection of laryngeal cancer is vital for patient outcomes and laryngeal function preservation.
Artificial intelligence (AI) can accelerate the triage of suspicious laryngeal lesions.
Reducing the time to clinical intervention is a key goal in laryngeal cancer management.

Purpose of the Study:

To systematically evaluate the diagnostic performance of AI in detecting laryngeal cancer.
To compare the efficacy of different AI models, including CNN-based approaches.

Main Methods:

A systematic literature search was conducted across five major databases up to February 2025.
Included 15 studies encompassing 17,559 patients.
Risk of bias was assessed using QUADAS-2, and data synthesis was performed using Meta Disc 1.4.

Main Results:

AI demonstrated high diagnostic accuracy for laryngeal cancer detection with 78% sensitivity and 86% specificity.
The pooled diagnostic odds ratio for AI was 53.77 (95% CI: 27.38 to 105.62).
Convolutional Neural Network (CNN)-based AI models outperformed non-CNN models in image analysis and lesion detection.

Conclusions:

AI possesses significant diagnostic accuracy, high sensitivity, and specificity for laryngeal cancer detection.
AI tools are suitable for real-world clinical applications in diagnosing laryngeal cancer.
AI facilitates earlier and more accurate identification of laryngeal lesions.