AI Medical Compendium Journal:
The Laryngoscope

Showing 31 to 40 of 64 articles

Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility.

The Laryngoscope
OBJECTIVE: (1) To compare maximum glottic opening angle (anterior glottic angle, AGA) in patients with bilateral vocal fold immobility (BVFI), unilateral vocal fold immobility (UVFI) and normal larynges (NL), and (2) to correlate maximum AGA with pat...

Single-Port Transaxillary Robotic Modified Radical Neck Dissection (STAR-RND): Initial Experiences.

The Laryngoscope
OBJECTIVES: This study aimed to demonstrate the usefulness of single-port transaxillary robotic modified radical neck dissection (STAR-RND) for metastatic thyroid cancer, and its potential to make small and invisible surgical wounds possible compared...

An Artificial Intelligence-Based Cosmesis Evaluation for Temporomandibular Joint Reconstruction.

The Laryngoscope
OBJECTIVE: Management of the temporomandibular joint (TMJ) following condylar resection remains challenging in the field of mandibular reconstruction. A simple reconstruction of the TMJ with a contoured end of a fibular graft placed into the joint sp...

A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences.

The Laryngoscope
OBJECTIVE: To design a deep learning model based on multimodal magnetic resonance image (MRI) sequences for automatic parotid neoplasm classification, and to improve the diagnostic decision-making in clinical settings.

Evaluation of a 3D-Printed Transoral Robotic Surgery Simulator Utilizing Artificial Tissue.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: Transoral robotic surgery (TORS) poses challenges for operators in training, with limited robot access on a platform requiring distinct surgical skills. Few simulators exist, and current virtual reality training modules exclude...

Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection.

The Laryngoscope
OBJECTIVES: To assess a new application of artificial intelligence for real-time detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow-band imaging (NBI) videolaryngoscopies based on the You-Only-Look-Once (YOLO) d...

Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasoph...

Stratifying the Risk of Cardiovascular Disease in Obstructive Sleep Apnea Using Machine Learning.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: Obstructive sleep apnea (OSA) is associated with higher risk of morbidity and mortality related to cardiovascular disease (CVD). Due to overlapping clinical risk factors, identifying high-risk patients with OSA who are likely t...

Diagnostic Accuracies of Laryngeal Diseases Using a Convolutional Neural Network-Based Image Classification System.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: There may be an interobserver variation in the diagnosis of laryngeal disease based on laryngoscopic images according to clinical experience. Therefore, this study is aimed to perform computer-assisted diagnosis for common lary...