Objectives/Hypothesis To create a new strategy for monitoring pediatric otitis media (OM),
we developed a brief, reliable, and objective method for automated classification using …

Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of
ear diseases extracted from the knowledge of otolaryngologists is limited. Improved …

Artificial intelligence (AI) using deep learning approaches the capabilities of human experts
in medical image diagnosis. However, due to liability issues in medical decisions, AI is often …

Background Accurate assessment of the percentage total body surface area (% TBSA) of
burn wounds is crucial in the management of burn patients. The resuscitation fluid and …

Purpose To present the utility of a smartphone-enabled otoscope as a teaching adjunct in
pre-clinical otoscopy training. Methods 60 pre-clinical medical students were randomized …

Background Deep learning (DL)–based artificial intelligence may have different diagnostic
characteristics than human experts in medical diagnosis. As a data-driven knowledge …

Otitis media (OM) is a common disease of the middle ear, affecting 80% of children before
the age of three. The otoscope, a simple illuminated magnifier, is the standard clinical …

Purpose Competent otoscopy is a key otolaryngology skill for a broad range of medical
careers, yet undergraduate's confidence to perform otoscopy is reported as low. Smartphone …

Background Acute otitis media (AOM) is the most frequent indication for antibiotic treatment
of children in the United States. Its diagnosis relies on visualization of the tympanic …

ObjectiveThe ai/m of this study was to compare the self-reported confidence of novices in
using a smartphone-enabled video otoscope, a microscope and loupes for ear examination …