Analysis of Prevalence and Predictive Factors of Long-Lasting Olfactory and Gustatory Dysfunction in COVID-19 Patients.

Background: Although smell and taste disorders are highly prevalent symptoms of COVID-19 infection, the predictive factors leading to long-lasting chemosensory dysfunction are still poorly understood.

Methods: 102 out of 421 (24.2%) mildly symptomatic COVID-19 patients completed a second questionnaire about the evolution of their symptoms one year after the infection using visual analog scales (VAS).

Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach.

The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection.

Virtual surgery for patients with nasal obstruction: Use of computational fluid dynamics (MeComLand, Digbody & Noseland) to document objective flow parameters and optimise surgical results.

Introduction: Computational fluid dynamics (CFD) is a mathematical tool to analyse airflow. We present a novel CFD software package to improve results following nasal surgery for obstruction.

Methods: A group of engineers in collaboration with otolaryngologists have developed a very intuitive CFD software package called MeComLand®, which uses the patient's cross-sectional (tomographic) images, thus showing in detail results originated by CFD such as airflow distributions, velocity profiles, pressure, or wall shear stress.