Latest AI and machine learning research in otolaryngology for healthcare professionals.
OBJECTIVE: To characterize non-neural post-thyroidectomy dysphonia (PTD) by analyzing long-term voice outcomes in patients with no evidence of nerve injury on postoperative laryngoscopy and electromyography (EMG). STUDY DESIGN: Retrospective cohort study. SETTING: Tertiary referral center. METHODS: We reviewed 527 post-thyroidectomy patients with intact vocal fold mobility and normal EMG results a...
As a critical factor in diagnostic work-up and treatment decision-making process of sleep-related breathing disorders, accurate localization of obstructive sites in the upper airway is in dire need. Snoring, as a dynamic acoustic signal, carries informative information relating to the sites and degree of obstruction in the upper airway, offering a non-invasive, cost-effective solution for obstruct...
BACKGROUND/OBJECTIVES: Patient messaging portals are widely used in clinical practice and are linked to improved patient outcomes, but they are also a...
OBJECTIVE: Artificial intelligence (AI) is poised to transform surgical education, particularly in providing objective, scalable feedback. This study ...
BACKGROUND: At present, the early warning of difficult airway remains fraught with challenges. Previous ultrasonic quantitative parameters have demons...
BACKGROUND: The 2025 American Thyroid Association guidelines recommend total thyroidectomy for all T3b differentiated thyroid carcinoma (DTC). However...
OBJECTIVE: To develop and externally validate a computer-aided diagnosis (CADx) model using artificial intelligence (AI) for classifying laryngeal les...
BACKGROUND: Intraoperative preservation of parathyroid glands (PGs) remained a significant challenge in thyroidectomy. Recently, deep learning has dem...
The discovery of new organic photocatalysts (PCs) for energy transfer (EnT) catalysis remains a significant challenge, largely due to the vast and und...
OBJECTIVE: Improved operating room (OR) efficiency provides greater patient throughput, reduced costs, and maximal patient care. The aim of this study...
IMPORTANCE: Large language models (LLMs), a rapidly advancing domain of artificial intelligence (AI), are poised to transform administrative, clinical...
BACKGROUND: Aspiration causes or aggravates a variety of respiratory diseases. Subjective bedside evaluations of aspiration are limited by poor inter-...
OBJECTIVE: To develop and evaluate a deep learning object detection system for identifying vocal fold polyps in stroboscopic video frames using You On...
BACKGROUND: Rhinoplasty is a complex operation that warrants careful consideration of both functional and aesthetic principles. Despite its prevalence...
OBJECTIVES: To develop and validate a multimodal deep learning model integrating clinical data, contrast-enhanced CT, and laryngoscopic images for dif...
OBJECTIVE: To compare the diagnostic accuracy, linguistic clarity, and user satisfaction of three large language models (ChatGPT-4.0, Claude 3.7 Sonet...
Deep learning (DL) applications in healthcare are expanding beyond proof-of-concept studies. Yet, the extent of its real-world implementation and impa...
PURPOSE: Artificial intelligence systems known as large language models are being evaluated for clinical decision support, yet their role in emergency...
Operative ear, nose and throat (ENT) medicine in Germany is facing a profound structural change, which is significantly influenced by the ongoing hosp...