English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent work has shown the potential of using audio data (eg, cough,breathing, and voice) in the screening for COVID-19. However, these approachesonly focus on one-off detection and detect the infection given the currentaudio sample, but do not monitor disease progression in COVID-19. Limitedexploration has been put forward to continuously monitor COVID-19 progression,especially recovery, through longitudinal audio data. Tracking diseaseprogression characteristics could lead to more timely treatment.  The primary objective of this study is to explore the potential oflongitudinal audio samples over time for COVID-19 progression prediction and,especially, recovery trend prediction using sequential deep learningtechniques.  Crowdsourced respiratory audio data, including breathing, cough, and voicesamples, from 212 individuals over 5-385 days were analyzed. We developed adeep learning-enabled tracking tool using gated recurrent units (GRUs) todetect COVID-19 progression by exploring the audio dynamics of the individuals'historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19detection in terms of positive and negative (healthy) tests, and (2)longitudinal disease progression prediction over time in terms of probabilityof positive tests.  The strong performance for COVID-19 detection, yielding an AUROC of 0.79, asensitivity of 0.75, and a specificity of 0.71 supported the effectiveness ofthe approach compared to methods that do not leverage longitudinal dynamics. Wefurther examined the predicted disease progression trajectory, displaying highconsistency with test results with a correlation of 0.75 in the test cohort and0.86 in a subset of the test cohort who reported recovery. Our findings suggestthat monitoring COVID-19 evolution via longitudinal audio data has potential inthe tracking of individuals' disease progression and recovery.

jmir. journal of medical internet research/journal of medical internet research

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19  Progression Prediction via Sequential Deep Learning: Model Development and  Validation