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Artificial intelligence in sleep medicine: background and implications for clinicians

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Abstract

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.

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Goldstein CA, Berry RB, Kent DT, et al. Artificial intelligence in sleep medicine: background and implications for clinicians. J Clin Sleep Med. 2020;16(4):609–618.

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Abbreviations

AHI:

apnea-hypopnea index

AI:

artificial intelligence

CST:

consumer sleep technology

EEG:

electroencephalogram

FDA:

US Food and Drug Administration

ML:

machine learning

MSLT:

Multiple Sleep Latency Test

OSA:

obstructive sleep apnea

PAP:

positive airway pressure

PSG:

polysomnography

RBD:

REM sleep behavior disorder

REM:

rapid eye movement

RSWA:

REM sleep without atonia

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ACKNOWLEDGMENTS

The authors thank the AASM staff members who assisted with the development of this article. The authors also are grateful for the feedback provided by the new members of the AASM Artificial Intelligence in Sleep Medicine Committee: Arun Badi, MD, PhD; Hao Cheng, MD; Daniel V. Fabbri, PhD; Thomas Gustafson, MD; and Octavian Ioachimescu, MD, PhD, MBA.

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Authors and Affiliations

  1. Sleep Disorders Center, Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA

    Cathy A. Goldstein MD

  2. Division of Pulmonary, Critical Care and Sleep Medicine, University of Florida, Gainesville, Florida, USA

    Richard B. Berry MD

  3. Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA

    David T. Kent MD

  4. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    David A. Kristo MD

  5. Department of Population Health, Department of Psychiatry, NYU Langone Health, New York, New York, USA

    Azizi A. Seixas PhD

  6. Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA

    Susan Redline MD

  7. Neurology Department, Massachusetts General Hospital, Boston, Massachusetts, USA

    M. Brandon Westover MD, PhD

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  1. Cathy A. Goldstein MD
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Corresponding author

Correspondence to Cathy A. Goldstein MD.

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Address correspondence to: Cathy A. Goldstein MD, University of Michigan Sleep Disorders Center, C728 Med Inn Building, 1500 E. Medical Center Drive, SPC5845, Ann Arbor, Michigan, 48109-5845; Email: cathygo@med.umich.edu

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Goldstein, C., Berry, R., Kent, D. et al. Artificial intelligence in sleep medicine: background and implications for clinicians. J Clin Sleep Med 16, 609–618 (2020). https://doi.org/10.5664/jcsm.8388

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  • DOI: https://doi.org/10.5664/jcsm.8388

Profiles

  1. Azizi A. Seixas View author profile
  2. M. Brandon Westover View author profile