Harnessing machine learning in diagnosing complex hoarseness cases.

Purpose: Traditional vocal fold pathology recognition typically requires expertise of laryngologists and advanced instruments, primarily through direct visualization. This study aims to augment this conventional paradigm by introducing a parallel diagnostic procedure. Our objective is to harness a machine-learning algorithm designed to discern intricate patterns within patients' voice recordings to distinguish not only between healthy and hoarse voices but also among various specific disorders.

Higher theta-beta ratio during screen-based vs. printed paper is related to lower attention in children: An EEG study.

Reading is considered a non-intuitive, cognitively demanding ability requiring synchronization between several neural networks supporting visual, language processing and higher-order abilities. With the involvement of technology in our everyday life, reading from a screen has become widely used. Several studies point to challenges in processing written materials from the screen due to changes in attention allocation when reading from a screen compared to reading from a printed paper.

Guiding Principles for a Pediatric Neurology ICU (neuroPICU) Bedside Multimodal Monitor: Findings from an International Working Group.

Background: Physicians caring for children with serious acute neurologic disease must process overwhelming amounts of physiological and medical information. Strategies to optimize real time display of this information are understudied.

Objectives: Our goal was to engage clinical and engineering experts to develop guiding principles for creating a pediatric neurology intensive care unit (neuroPICU) monitor that integrates and displays data from multiple sources in an intuitive and informative manner.