Literature DB >> 32065113

Artificial intelligence in sleep medicine: background and implications for clinicians.

Cathy A Goldstein1, Richard B Berry2, David T Kent3, David A Kristo4, Azizi A Seixas5, Susan Redline6, M Brandon Westover7.   

Abstract

None: Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools. Combined with other sources of health data, AI is expected to provide new insights to inform the clinical care of sleep disorders and advance our understanding of the integral role sleep plays in human health. Additionally, AI has the potential to streamline day-to-day operations and therefore optimize direct patient care by the sleep disorders team. However, clinicians, scientists, and other stakeholders must develop best practices to integrate this rapidly evolving technology into our daily work while maintaining the highest degree of quality and transparency in health care and research. Ultimately, when harnessed appropriately in conjunction with human expertise, AI will improve the practice of sleep medicine and further sleep science for the health and well-being of our patients.
© 2020 American Academy of Sleep Medicine.

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Year:  2020        PMID: 32065113      PMCID: PMC7161463          DOI: 10.5664/jcsm.8388

Source DB:  PubMed          Journal:  J Clin Sleep Med        ISSN: 1550-9389            Impact factor:   4.062


  81 in total

1.  Sleep health: can we define it? Does it matter?

Authors:  Daniel J Buysse
Journal:  Sleep       Date:  2014-01-01       Impact factor: 5.849

2.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Authors:  Varun Gulshan; Lily Peng; Marc Coram; Martin C Stumpe; Derek Wu; Arunachalam Narayanaswamy; Subhashini Venugopalan; Kasumi Widner; Tom Madams; Jorge Cuadros; Ramasamy Kim; Rajiv Raman; Philip C Nelson; Jessica L Mega; Dale R Webster
Journal:  JAMA       Date:  2016-12-13       Impact factor: 56.272

3.  An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24 x 7 utilizing the Siesta database.

Authors:  Peter Anderer; Georg Gruber; Silvia Parapatics; Michael Woertz; Tatiana Miazhynskaia; Gerhard Klosch; Bernd Saletu; Josef Zeitlhofer; Manuel J Barbanoj; Heidi Danker-Hopfe; Sari-Leena Himanen; Bob Kemp; Thomas Penzel; Michael Grozinger; Dieter Kunz; Peter Rappelsberger; Alois Schlogl; Georg Dorffner
Journal:  Neuropsychobiology       Date:  2005-04-18       Impact factor: 2.328

4.  Eszopiclone increases the respiratory arousal threshold and lowers the apnoea/hypopnoea index in obstructive sleep apnoea patients with a low arousal threshold.

Authors:  Danny J Eckert; Robert L Owens; Geoffrey B Kehlmann; Andrew Wellman; Shilpa Rahangdale; Susie Yim-Yeh; David P White; Atul Malhotra
Journal:  Clin Sci (Lond)       Date:  2011-06       Impact factor: 6.124

5.  A quantitative statistical analysis of the submentalis muscle EMG amplitude during sleep in normal controls and patients with REM sleep behavior disorder.

Authors:  Raffaele Ferri; Mauro Manconi; Giuseppe Plazzi; Oliviero Bruni; Stefano Vandi; Pasquale Montagna; Luigi Ferini-Strambi; Marco Zucconi
Journal:  J Sleep Res       Date:  2008-03       Impact factor: 3.981

6.  Delayed emergence of a parkinsonian disorder or dementia in 81% of older men initially diagnosed with idiopathic rapid eye movement sleep behavior disorder: a 16-year update on a previously reported series.

Authors:  Carlos H Schenck; Bradley F Boeve; Mark W Mahowald
Journal:  Sleep Med       Date:  2013-01-22       Impact factor: 3.492

7.  Daytime sleepiness and polysomnography in obstructive sleep apnea patients.

Authors:  Nuria Roure; Silvia Gomez; Olga Mediano; Joaquin Duran; Monica de la Peña; Francisco Capote; Joaquin Teran; Juan Fernando Masa; Maria Luz Alonso; Jaime Corral; Angeles Sánchez-Armengod; Cristina Martinez; Antonia Barceló; David Gozal; Jose Maria Marín; Ferran Barbé
Journal:  Sleep Med       Date:  2008-05-15       Impact factor: 3.492

8.  Test-retest reliability of the multiple sleep latency test in narcolepsy without cataplexy and idiopathic hypersomnia.

Authors:  Lynn Marie Trotti; Beth A Staab; David B Rye
Journal:  J Clin Sleep Med       Date:  2013-08-15       Impact factor: 4.062

9.  Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

Authors:  Jens B Stephansen; Alexander N Olesen; Mads Olsen; Aditya Ambati; Eileen B Leary; Hyatt E Moore; Oscar Carrillo; Ling Lin; Fang Han; Han Yan; Yun L Sun; Yves Dauvilliers; Sabine Scholz; Lucie Barateau; Birgit Hogl; Ambra Stefani; Seung Chul Hong; Tae Won Kim; Fabio Pizza; Giuseppe Plazzi; Stefano Vandi; Elena Antelmi; Dimitri Perrin; Samuel T Kuna; Paula K Schweitzer; Clete Kushida; Paul E Peppard; Helge B D Sorensen; Poul Jennum; Emmanuel Mignot
Journal:  Nat Commun       Date:  2018-12-06       Impact factor: 14.919

10.  Advances in Glucose Monitoring and Automated Insulin Delivery: Supplement to Endocrine Society Clinical Practice Guidelines.

Authors:  Anne L Peters; Andrew J Ahmann; Irl B Hirsch; Jennifer K Raymond
Journal:  J Endocr Soc       Date:  2018-10-05
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  9 in total

1.  Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring.

Authors:  Jung Kyung Hong; Taeyoung Lee; Roben Deocampo Delos Reyes; Joonki Hong; Hai Hong Tran; Dongheon Lee; Jinhwan Jung; In-Young Yoon
Journal:  Nat Sci Sleep       Date:  2021-12-24

2.  Contact-free screening for obstructive sleep apnea: comfort, especially in a physically distanced brave new world.

Authors:  Bahman Chavoshan; George Dungan; Peter Y Liu
Journal:  J Clin Sleep Med       Date:  2021-05-01       Impact factor: 4.062

3.  Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning.

Authors:  Ipek Ensari; Billy A Caceres; Kasey B Jackman; Niurka Suero-Tejeda; Ari Shechter; Michelle L Odlum; Suzanne Bakken
Journal:  Sleep Med       Date:  2021-07-19       Impact factor: 4.842

4.  Sleep and Big Data: harnessing data, technology, and analytics for monitoring sleep and improving diagnostics, prediction, and interventions-an era for Sleep-Omics?

Authors:  Susan Redline; Shaun M Purcell
Journal:  Sleep       Date:  2021-06-11       Impact factor: 6.313

5.  Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm.

Authors:  Matteo Cesari; Ambra Stefani; Thomas Penzel; Abubaker Ibrahim; Heinz Hackner; Anna Heidbreder; András Szentkirályi; Beate Stubbe; Henry Völzke; Klaus Berger; Birgit Högl
Journal:  J Clin Sleep Med       Date:  2021-06-01       Impact factor: 4.324

Review 6.  Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.

Authors:  Anuja Bandyopadhyay; Cathy Goldstein
Journal:  Sleep Breath       Date:  2022-03-09       Impact factor: 2.816

7.  Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care.

Authors:  Junjun Chen; Hong Pu; Dianrong Wang
Journal:  J Healthc Eng       Date:  2021-07-02       Impact factor: 2.682

8.  Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study.

Authors:  Satoru Tsuiki; Takuya Nagaoka; Tatsuya Fukuda; Yuki Sakamoto; Fernanda R Almeida; Hideaki Nakayama; Yuichi Inoue; Hiroki Enno
Journal:  Sleep Breath       Date:  2021-02-08       Impact factor: 2.816

9.  A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study.

Authors:  Jeonghwan Hwang; Taeheon Lee; Honggu Lee; Seonjeong Byun
Journal:  J Med Internet Res       Date:  2022-01-19       Impact factor: 5.428

  9 in total

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