Validation of assisted automated scoring versus manual scoring of polysmnography (PSG) studies Source: International Congress 2017 – Diagnostic approaches to obstructive sleep apnoea Year: 2017
Comparison of visual and automatic methods for scoring leg movements in polygraphic sleep recordings. Validity of a modified automatic method Source: Annual Congress 2008 - Diagnostic aspects of sleep apnoea Year: 2008
A comparison of automated and manual sleep staging and respiratory event recognition in a portable sleep diagnostic device with in-lab sleep study Source: Virtual Congress 2020 – Screening methods and diagnostic tools for obstructive sleep apnoea Year: 2020
Deep learning for scoring sleep based on cardiorespiratory signals as compared to auto and multiple manual sleep scorings based on neurological signals Source: International Congress 2018 – New diagnostic tools for sleep and breathing and healthcare provision options Year: 2018
Validation of automated versus manual scoring of cardio-respiratory sleep studies: A clinical audit Source: International Congress 2016 – Man versus machine: waves, frequency, and more in lung function Year: 2016
Comparison of accuracy of VBN (virtual bronchoscopic navigation) software (VINCENT vs. LungPoint) for peripheral lung lesions and trial of prediction of diagnostic yield from VBN findings Source: International Congress 2019 – Diagnostic procedures and biology of lung cancer Year: 2019
Technical and digital polysomnography specifications for the new AASM sleep scoring rules Source: Annual Congress 2008 - Diagnostic aspects of sleep apnoea Year: 2008
Comparison of Manual and Automatic Scoring of limited channel sleep studies:Noxturnal Software correlates well with manual scoring in severe OSA. Source: International Congress 2017 – Diagnostic approaches to obstructive sleep apnoea Year: 2017
Diagnostic accuracy of COPD severity grading using machine learning features and lung sounds. Source: International Congress 2019 – Innovations in primary care assessment and management Year: 2019
Comparison of the visually scored AHI determined by a new recording system compared to conventional polysomnography Source: Eur Respir J 2006; 28: Suppl. 50, 194s Year: 2006
Automatic lung sound analysis accuracy: a validation study Source: Virtual Congress 2021 – Digital health interventions in respiratory medicine Year: 2021
A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis Source: Eur Respir J, 56 (2) 2000775; 10.1183/13993003.00775-2020 Year: 2020
ECG based detection of sleep apnea - a systematic comparison of 12 algorithms Source: Eur Respir J 2002; 20: Suppl. 38, 292s Year: 2002
Workstation 3: Scoring sleep using AASM guidelines. A brief introduction to scoring respiratory events, arousals and limb movements. Source: International Congress 2015 – EW25 Hands-on polysomnography Year: 2015
Workstation 3: Scoring sleep using AASM guidelines. A brief introduction to scoring respiratory events, arousals and limb movements. Source: International Congress 2015 – EW27 Hands-on polysomnography Year: 2015
ciliaFA: A free research tool for accurate, automated, high-throughput measurement of ciliary beat frequency (CBF) Source: Annual Congress 2011 - Lung cell biology Year: 2011
Knowledge-based automatic sleep stage recognition for the diagnosis of sleep disorders Source: Eur Respir J 2003; 22: Suppl. 45, 271s Year: 2003
Diagnostic accuracy of asthma severity grading using machine learning features and lung sounds Source: International Congress 2018 – Innovations in equipment and their application Year: 2018
Comparative study between automatic and manual scoring in the diagnosis of sleep apnea by home respiratory polygraphy Source: Virtual Congress 2021 – From phenotyping to different diagnostic tools for sleep breathing disorders Year: 2021
Validation of manual and automated wheezing detection from audio recordings Source: Virtual Congress 2021 – New insights into lung function testing Year: 2021