Otolaryngology

Diagnostic accuracy of deep learning-based algorithms in laryngoscopy: a systematic review and meta-analysis.

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 review and meta-analysis to investigate diagnostic utility of deep learning in laryngoscopy.

Development of Machine Learning Copilot to Assist Novices in Learning Flexible Laryngoscopy.

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 FFL skills.

Prediction of hearing recovery with deep learning algorithm in sudden sensorineural hearing loss.

This study aimed to establish a deep learning-based predictive model for the prognosis of idiopathic sudden sensorineural hearing loss (SSNHL). Data f...

Artificial neural network prediction of postoperative complications in papillary thyroid microcarcinoma based on preoperative ultrasonographic features.

OBJECTIVE: To predict post-thyroidectomy complications in papillary thyroid microcarcinoma (PTMC) patients using a deep learning model based on preope...

Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods.

BACKGROUND: Postoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged...

Machine Learning-Based Algorithm for the Early Prediction of Postoperative Hypocalcemia Risk After Thyroidectomy.

OBJECTIVE: We used machine learning to develop and validate a multivariable algorithm allowing the accurate and early prediction of postoperative hypo...

Merging Human and Artificial Intelligence to Improve Otolaryngology Care.

The Application of Artificial Intelligence to Acoustic Data in Otolaryngology.

Artificial intelligence (AI), particularly deep learning, has revolutionized various fields through its ability to model complex, noisy systems with h...

Artificial Intelligence and Pediatric Otolaryngology.

Artificial intelligence (AI) studies show how to program computers to simulate human intelligence and perform data interpretation, learning, and adapt...

Predicting Post-surgery Discharge Time in Pediatric Patients Using Machine Learning.

BACKGROUND: Prolonged hospital stays after pediatric surgeries, such as tonsillectomy and adenoidectomy, pose significant concerns regarding cost and ...

AI-generated text in otolaryngology publications: a comparative analysis before and after the release of ChatGPT.

PURPOSE: This study delves into the broader implications of artificial intelligence (AI) text generation technologies, including large language models...

Autonomous Robotic Systems in Otolaryngology-Head and Neck Surgery.

Robotic surgery is a growing field with increasing applications to patient care. With the rising use of artificial intelligence (AI), a new frontier e...

Computer Vision and Videomics in Otolaryngology-Head and Neck Surgery: Bridging the Gap Between Clinical Needs and the Promise of Artificial Intelligence.

This article discusses the role of computer vision in otolaryngology, particularly through endoscopy and surgery. It covers recent applications of art...

Patient-Centered Equitable and Safe Artificial Intelligence in Otolaryngology-Head and Neck Surgery.

The Ethics of Incorporating Artificial Intelligence Technologies in Prognostic Clinical Decision-Making in Otolaryngology.

Artificial Intelligence in Otolaryngology - Head and Neck Surgery in the Past Decade.

Artificial Intelligence in Head and Neck Surgery.

This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's po...

Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study.

Accurate prediction of difficult direct laryngoscopy (DDL) is essential to ensure optimal airway management and patient safety. The present study prop...

Variability of Guidelines and Disclosures for AI-Generated Content in Top Surgical Journals.

: When properly utilized, artificial intelligence generated content (AIGC) may improve virtually every aspect of research, from data gathering to synt...

Standardization, Collaboration, and Education in the Implementation of Artificial Intelligence in Otolaryngology: The Key to Scalable Impact.

The study delves into the crucial role of standardization, collaboration, and education in the integration of artificial intelligence (AI) in otolaryn...