Segmentation of Vestibular Schwannomas on Postoperative Gadolinium-Enhanced T1-Weighted and Noncontrast T2-Weighted Magnetic Resonance Imaging Using Deep Learning.

Objective: Surveillance of postoperative vestibular schwannomas currently relies on manual segmentation and measurement of the tumor by content experts, which is both labor intensive and time consuming. We aimed to develop and validate deep learning models for automatic segmentation of postoperative vestibular schwannomas on gadolinium-enhanced T1-weighted magnetic resonance imaging (GdT1WI) and noncontrast high-resolution T2-weighted magnetic resonance imaging (HRT2WI).

Study Design: A supervised machine learning approach using a U-Net model was applied to segment magnetic resonance imaging images into pixels representing vestibular schwannoma and background pixels.

Impact of COVID-19 on Otolaryngology Literature.

Objectives/hypothesis: To understand the effect of the COVID-19 pandemic on the volume, quality, and impact of otolaryngology publications.

Study Design: Retrospective analysis.

Methods: Fifteen of the top peer-reviewed otolaryngology journals were queried on PubMed for COVID and non-COVID-related articles from April 1, 2020 to March 31, 2021 (pandemic period) and pre-COVID articles from the year prior.