BACKGROUND: The early diagnosis of laryngeal cancer (LCA) is crucial for prognosis, driving our search for an accurate, precise, and sensitive deep learning model to assist in LCA detection.
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...
OBJECTIVES: Here we describe the development and pilot testing of the first artificial intelligence (AI) software "copilot" to help train novices to competently perform flexible fiberoptic laryngoscopy (FFL) on a mannikin and improve their uptake of ...
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...
Current opinion in otolaryngology & head and neck surgery
Jul 24, 2024
PURPOSE OF REVIEW: The purpose of this review is to summarize the existing literature on artificial intelligence technology utilization in laryngology, highlighting recent advances and current barriers to implementation.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Jul 17, 2024
The current study trains, tests, and evaluates a deep learning algorithm to detect subglottic stenosis (SGS) on endoscopy. A retrospective review of patients undergoing microlaryngoscopy-bronchoscopy was performed. A pretrained image classifier (Resn...
Accurate prediction of difficult direct laryngoscopy (DDL) is essential to ensure optimal airway management and patient safety. The present study proposed an AI model that would accurately predict DDL using a small number of bedside pictures of the p...
OBJECTIVE: To extract texture features from vocal cord leukoplakia (VCL) images and establish a VCL risk stratification prediction model using machine learning (ML) techniques.
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