AIMC Topic: Laryngeal Neoplasms
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Diagnostic accuracy of deep learning-based algorithms in laryngoscopy: a systematic review and meta-analysis.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Oct 24, 2024
PURPOSE: Laryngoscopy is routinely used for suspicious vocal cord lesions with limited performance. Accumulated studies have demonstrated the bright prospect of deep learning in processing medical imaging. In this study, we perform a systematic revie...
Towards laryngeal cancer diagnosis using Dandelion Optimizer Algorithm with ensemble learning on biomedical throat region images.
Scientific reports
Aug 24, 2024
Laryngeal cancer exhibits a notable global health burden, with later-stage detection contributing to a low mortality rate. Laryngeal cancer diagnosis on throat region images is a pivotal application of computer vision (CV) and medical image diagnoses...
A lightweight intelligent laryngeal cancer detection system for rural areas.
American journal of otolaryngology
Aug 8, 2024
OBJECTIVE: Early diagnosis of laryngeal cancer (LC) is crucial, particularly in rural areas. Despite existing studies on deep learning models for LC identification, challenges remain in selecting suitable models for rural areas with shortages of lary...
AI Detection of Glottic Neoplasm Using Voice Signals, Demographics, and Structured Medical Records.
The Laryngoscope
Jun 12, 2024
OBJECTIVE: This study investigated whether artificial intelligence (AI) models combining voice signals, demographics, and structured medical records can detect glottic neoplasm from benign voice disorders.
Laryngeal Cancer Screening During Flexible Video Laryngoscopy Using Large Computer Vision Models.
The Annals of otology, rhinology, and laryngology
May 16, 2024
OBJECTIVE: Develop an artificial intelligence assisted computer vision model to screen for laryngeal cancer during flexible laryngoscopy.
Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study.
American journal of otolaryngology
Apr 30, 2024
OBJECTIVE: To develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL).
To trust or not to trust: evaluating the reliability and safety of AI responses to laryngeal cancer queries.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Apr 23, 2024
PURPOSE: As online health information-seeking surges, concerns mount over the quality and safety of accessible content, potentially leading to patient harm through misinformation. On one hand, the emergence of Artificial Intelligence (AI) in healthca...
Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis.
Scientific reports
Apr 23, 2024
Voice change is often the first sign of laryngeal cancer, leading to diagnosis through hospital laryngoscopy. Screening for laryngeal cancer solely based on voice could enhance early detection. However, identifying voice indicators specific to laryng...
Transoral robotic vertical partial laryngectomy (hemilaryngectomy) extended to the hypopharynx.
Head & neck
Jan 14, 2024
Locally advanced laryngeal cancers treatment often involves total laryngectomy, which some patients are unwilling to undergo, even if this choice reduces their survival probability. Therefore, the objective of laryngeal oncologic surgery is not only ...
Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.
European radiology
Jan 11, 2024
OBJECTIVES: To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in t...
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