Artificial Intelligence in Sleep Medicine: The Dawn of a New Era

References

• Khosla S, Deak MC, Gault D, et al. Consumer sleep technology: an American academy of sleep medicine position statement. J Clin Sleep Med. 2018;14(5):877–880. doi:10.5664/jcsm.7128
   PubMed Web of Science ®Google Scholar
• BaHammam AS, Pandi-Perumal SR, Hunasikatti M. Wearable Technologies/Consumer Sleep Technologies in Relation to Sleep Disorders Developments in the Last Decade. In: BaHammam AS, Hunasikatti M, editors. Sleep Apnea Frontiers: Pathophysiology, Diagnosis, and Treatment Strategies. Singapore: Springer Nature Singapore; 2023:145–160.
   Google Scholar
• Goldstein CA, Berry RB, Kent DT, et al. Artificial intelligence in sleep medicine: an American academy of sleep medicine position statement. J Clin Sleep Med. 2020;16(4):605–607. doi:10.5664/jcsm.8288
   PubMed Web of Science ®Google Scholar
• Bechny M, Monachino G, Fiorillo L, et al. Bridging AI and clinical practice: integrating automated sleep scoring algorithm with uncertainty-guided physician review. Nat Sci Sleep. 2024;2024:1.
   Google Scholar
• Alattar M, Govind A, Mainali S. Artificial intelligence models for the automation of standard diagnostics in sleep medicine—a systematic review. Bioengineering. 2024;11(3):206. doi:10.3390/bioengineering11030206
   PubMedGoogle Scholar
• Choo BP, Mok Y, Oh HC, et al. Benchmarking performance of an automatic polysomnography scoring system in a population with suspected sleep disorders. Front Neurol. 2023;14:1123935. doi:10.3389/fneur.2023.1123935
   PubMed Web of Science ®Google Scholar
• American Academy of Sleep Medicine. American academy of sleep medicine. AASM autoscoring certification: sleep stage pilot program. Darien, IL: American Academy of Sleep Medicine; 2023. Available from: https://aasm.org/about/industry-programs/autoscoring-certification/#:~:text=The%20AASM%20is%20excited%20to,the%20performance%20of%20autoscoring%20systems. Accessed April 15, 2024.
   Google Scholar
• Ferreira-Santos D, Amorim P, Silva Martins T, Monteiro-Soares M, Pereira Rodrigues P. Enabling early obstructive sleep apnea diagnosis with machine learning: systematic review. J Med Internet Res. 2022;24(9):e39452. doi:10.2196/39452
   PubMed Web of Science ®Google Scholar
• Serrano Alarcón Á, Martínez madrid N, Seepold R. A minimum set of physiological parameters to diagnose obstructive sleep apnea syndrome using non-invasive portable monitors. a systematic review. Life. 2021;11(11). doi:10.3390/life11111249
   PubMedGoogle Scholar
• Zhao R, Xue J, Zhang X, et al. Comparison of ring pulse oximetry using reflective photoplethysmography and PSG in the Detection of OSA in Chinese adults: a pilot study. Nat Sci Sleep. 2022;14:1427–1436. doi:10.2147/NSS.S367400
   PubMed Web of Science ®Google Scholar
• Almarshad MA, Al-Ahmadi S, Islam MS, BaHammam AS, Soudani A. Adoption of transformer neural network to improve the diagnostic performance of oximetry for obstructive sleep apnea. Sensors. 2023;23(18):7924. doi:10.3390/s23187924
   PubMed Web of Science ®Google Scholar
• Yang H, Lu S, Yang L. Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review. Syst Rev. 2024;13(1):38. doi:10.1186/s13643-024-02449-9
   PubMed Web of Science ®Google Scholar
• Kuan YC, Hong CT, Chen PC, Liu WT, Chung CC. Logistic regression and artificial neural network-based simple predicting models for obstructive sleep apnea by age, sex, and body mass index. Math Biosci Eng. 2022;19(11):11409–11421. doi:10.3934/mbe.2022532
   PubMed Web of Science ®Google Scholar
• Shen N, Luo T, Chen C, et al. Towards an automatic narcolepsy detection on ambiguous sleep staging and sleep transition dynamics joint model. J Neural Eng. 2022;19(5):056009. doi:10.1088/1741-2552/ac8c6b
   Web of Science ®Google Scholar
• Cesari M, Egger K, Stefani A, et al. Differentiation of central disorders of hypersomnolence with manual and artificial-intelligence-derived polysomnographic measures. Sleep. 2023;46(2). doi:10.1093/sleep/zsac288
   Web of Science ®Google Scholar
• BaHammam A. Personalizing obstructive sleep apnea therapy using machine learning: insights from the ISAACC trial. Ann Am Thorac Soc. 2024. doi:10.1513/AnnalsATS.202403-308ED
   Google Scholar
• Nemati S, Orr J, Malhotra A. Data-driven phenotyping: graphical models for model-based phenotyping of sleep apnea. IEEE Pulse. 2014;5(5):45–48. doi:10.1109/MPUL.2014.2339402
   PubMedGoogle Scholar
• Brennan HL, Kirby SD. The role of artificial intelligence in the treatment of obstructive sleep apnea. J Otolaryngol. 2023;52(1):7. doi:10.1186/s40463-023-00621-0
   Google Scholar
• Dutta R, Delaney G, Toson B, et al. A novel model to estimate key obstructive sleep apnea endotypes from standard polysomnography and clinical data and their contribution to obstructive sleep apnea severity. Ann Am Thorac Soc. 2021;18(4):656–667. doi:10.1513/AnnalsATS.202001-064OC
   PubMed Web of Science ®Google Scholar
• Dutta R, Tong BK, Eckert DJ. Development of a physiological-based model that uses standard polysomnography and clinical data to predict oral appliance treatment outcomes in obstructive sleep apnea. J Clin Sleep Med. 2022;18(3):861–870. doi:10.5664/jcsm.9742
   PubMed Web of Science ®Google Scholar
• Eguchi K, Yabuuchi T, Nambu M, et al. Investigation on factors related to poor CPAP adherence using machine learning: a pilot study. Sci Rep. 2022;12(1):19563. doi:10.1038/s41598-022-21932-8
   PubMed Web of Science ®Google Scholar
• Scioscia G, Tondo P, Foschino Barbaro MP, et al. Machine learning-based prediction of adherence to continuous positive airway pressure (CPAP) in obstructive sleep apnea (OSA). Inform Health Soc Care. 2022;47(3):274–282. doi:10.1080/17538157.2021.1990300
   PubMed Web of Science ®Google Scholar
• Qasrawi SO, BaHammam AS. Role of Sleep and Sleep Disorders in Cardiometabolic Risk: a Review and Update. Current Sleep Med Rep. 2024;10(1):34–50. doi:10.1007/s40675-024-00276-x
   Google Scholar
• Cohen O, Sánchez-de-la-Torre M, Al-Taie Z, et al. Heterogeneous effects of CPAP in Non-Sleepy OSA on CVD outcomes: post-hoc machine learning analysis of the ISAACC Trial (ECSACT Study). Ann Am Thorac Soc. 2024;21:1.
   PubMedGoogle Scholar
• Parekh A, Kam K, Wickramaratne S, et al. Ventilatory burden as a measure of obstructive sleep apnea severity is predictive of cardiovascular and all-cause mortality. Am J Respir Crit Care Med. 2023;208(11):1216–1226. doi:10.1164/rccm.202301-0109OC
   PubMed Web of Science ®Google Scholar
• Wickramaratne S, Kam K, Tolbert TM, et al. Combination of ventilatory, hypoxic, and arousal burden predicts short- and long-term consequences of OSA better than the apnea-hypopnea index [abstract]. Am J Respir Crit Care Med. 2023;207:A5968.
   Web of Science ®Google Scholar
• Hesse J, Martinelli J, Aboumanify O, Ballesta A, Relogio A. A mathematical model of the circadian clock and drug pharmacology to optimize irinotecan administration timing in colorectal cancer. Comput Struct Biotechnol J. 2021;19:5170–5183. doi:10.1016/j.csbj.2021.08.051
   PubMed Web of Science ®Google Scholar
• Braun R, Kath WL, Iwanaszko M, et al. Universal method for robust detection of circadian state from gene expression. Proc Natl Acad Sci U S A. 2018;115(39):E9247–e56. doi:10.1073/pnas.1800314115
   PubMed Web of Science ®Google Scholar
• Zaki NFW, Yousif M, BaHammam AS, et al. Chronotherapeutics: recognizing the importance of timing factors in the treatment of disease and sleep disorders. Clin Neuropharmacol. 2019;42(3):80–87. doi:10.1097/WNF.0000000000000341
   PubMed Web of Science ®Google Scholar
• Leppanen T, Varon C, de Zambotti M, Myllymaa S. Machine learning and wearable technology in sleep medicine. Front Digit Health. 2022;4:845879. doi:10.3389/fdgth.2022.845879
   PubMedGoogle Scholar
• Göktepe-Kavis P, Aellen FM, Alnes SL, Tzovara A. Sleep research in the era of AI. Clin Transl Neurosci. 2024;8(1):13. doi:10.3390/ctn8010013
   Google Scholar
• Millard K, Richardson M. On the importance of training data sample selection in random forest image classification: a case study in peatland ecosystem mapping. Remote Sensing. 2015;7(7):8489–8515. doi:10.3390/rs70708489
   Web of Science ®Google Scholar
• Roenneberg T. The human sleep project. Nature. 2013;498:427–8. doi:10.1038/498427a
   PubMed Web of Science ®Google Scholar
• Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective. Sleep Breath. 2023;27(1):39–55. doi:10.1007/s11325-022-02592-4
   PubMed Web of Science ®Google Scholar
• Jeong E, Shin YW, Byun JI, et al. EEG-based machine learning models for the prediction of phenoconversion time and subtype in iRBD. Sleep. 2024. doi:10.1093/sleep/zsae031
   Google Scholar
• Cesari M, Christensen JAE, Muntean ML, et al. A data-driven system to identify REM sleep behavior disorder and to predict its progression from the prodromal stage in Parkinson’s disease. Sleep Med. 2021;77:238–248. doi:10.1016/j.sleep.2020.04.010
   PubMed Web of Science ®Google Scholar
• Adra N, Sun H, Ganglberger W, et al. Optimal spindle detection parameters for predicting cognitive performance. Sleep. 2022;45(4). doi:10.1093/sleep/zsac001
   PubMed Web of Science ®Google Scholar
• Klement W, El Emam K. Consolidated reporting guidelines for prognostic and diagnostic machine learning modeling studies: development and validation. J Med Internet Res. 2023;25:e48763.
   PubMed Web of Science ®Google Scholar