AIMC Topic: Laryngeal Neoplasms

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Laryngeal Cancer Screening During Flexible Video Laryngoscopy Using Large Computer Vision Models.

The Annals of otology, rhinology, and laryngology
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
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
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...

Transoral robotic vertical partial laryngectomy (hemilaryngectomy) extended to the hypopharynx.

Head & neck
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
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...

Real-Time Laryngeal Cancer Boundaries Delineation on White Light and Narrow-Band Imaging Laryngoscopy with Deep Learning.

The Laryngoscope
OBJECTIVE: To investigate the potential of deep learning for automatically delineating (segmenting) laryngeal cancer superficial extent on endoscopic images and videos.

Essentially unedited deep-learning-based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning.

Journal of applied clinical medical physics
Quality of organ at risk (OAR) autosegmentation is often judged by concordance metrics against the human-generated gold standard. However, the ultimate goal is the ability to use unedited autosegmented OARs in treatment planning, while maintaining th...

Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images.

Head & neck
BACKGROUND: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the...

Transoral Laser-Assisted Infrahyoid Supraglottic Laryngectomy for Selected Patients With Supraglottic Cancer.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Supraglottic laryngectomy has evolved from open to transoral endoscopic approaches with advancements in surgical techniques and instruments such as lasers, endoscopes, ultrasonic devices, and robotics. Transoral laser-assisted microsurgery has emerge...