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Watson NF, Fernandez CR. Artificial intelligence and sleep: Advancing sleep medicine. Sleep Med Rev 2021; 59:101512. [PMID: 34166990 DOI: 10.1016/j.smrv.2021.101512] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) allows analysis of "big data" combining clinical, environmental and laboratory based objective measures to allow a deeper understanding of sleep and sleep disorders. This development has the potential to transform sleep medicine in coming years to the betterment of patient care and our collective understanding of human sleep. This review addresses the current state of the field starting with a broad definition of the various components and analytic methods deployed in AI. We review examples of AI use in screening, endotyping, diagnosing, and treating sleep disorders and place this in the context of precision/personalized sleep medicine. We explore the opportunities for AI to both facilitate and extend providers' clinical impact and present ethical considerations regarding AI derived prognostic information. We cover early adopting specialties of AI in the clinical realm, such as radiology and pathology, to provide a road map for the challenges sleep medicine is likely to face when deploying this technology. Finally, we discuss pitfalls to ensure clinical AI implementation proceeds in the safest and most effective manner possible.
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Affiliation(s)
- Nathaniel F Watson
- Department of Neurology, University of Washington (UW) School of Medicine, USA; UW Medicine Sleep Center, USA.
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Tschuggnall M, Grote V, Pirchl M, Holzner B, Rumpold G, Fischer MJ. Machine learning approaches to predict rehabilitation success based on clinical and patient-reported outcome measures. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100598] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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MacGregor RM, Guo A, Masood MF, Cupps BP, Ewald GA, Pasque MK, Foraker R. Machine Learning Outcome Prediction in Dilated Cardiomyopathy Using Regional Left Ventricular Multiparametric Strain. Ann Biomed Eng 2020; 49:922-932. [PMID: 33006006 DOI: 10.1007/s10439-020-02639-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/24/2020] [Indexed: 01/17/2023]
Abstract
The clinical presentation of idiopathic dilated cardiomyopathy (IDCM) heart failure (HF) patients who will respond to medical therapy (responders) and those who will not (non-responders) is often similar. A machine learning (ML)-based clinical tool to identify responders would prevent unnecessary surgery, while targeting non-responders for early intervention. We used regional left ventricular (LV) contractile injury patterns in ML models to identify IDCM HF non-responders. MRI-based multiparametric strain analysis was performed in 178 test subjects (140 normal subjects and 38 IDCM patients), calculating longitudinal, circumferential, and radial strain over 18 LV sub-regions for inclusion in ML analyses. Patients were identified as responders based upon symptomatic and contractile improvement on medical therapy. We tested the predictive accuracy of support vector machines (SVM), logistic regression (LR), random forest (RF), and deep neural networks (DNN). The DNN model outperformed other models, predicting response to medical therapy with an area under the receiver operating characteristic curve (AUC) of 0.94. The top features were longitudinal strain in (1) basal: anterior, posterolateral and (2) mid: posterior, anterolateral, and anteroseptal sub-regions. Regional contractile injury patterns predict response to medical therapy in IDCM HF patients, and have potential application in ML-based HF patient care.
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Affiliation(s)
- Robert M MacGregor
- Department of Surgery, Division of Cardiothoracic Surgery, Barnes-Jewish Hospital, Washington University School of Medicine, Campus Box 8234, 660 S. Euclid Ave., St. Louis, MO, 63110, USA
| | - Aixia Guo
- Institute for Informatics, Division of General Medical Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Muhammad F Masood
- Department of Surgery, Division of Cardiothoracic Surgery, Barnes-Jewish Hospital, Washington University School of Medicine, Campus Box 8234, 660 S. Euclid Ave., St. Louis, MO, 63110, USA
| | - Brian P Cupps
- Department of Surgery, Division of Cardiothoracic Surgery, Barnes-Jewish Hospital, Washington University School of Medicine, Campus Box 8234, 660 S. Euclid Ave., St. Louis, MO, 63110, USA
| | - Gregory A Ewald
- John T. Milliken Department of Internal Medicine, Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
| | - Michael K Pasque
- Department of Surgery, Division of Cardiothoracic Surgery, Barnes-Jewish Hospital, Washington University School of Medicine, Campus Box 8234, 660 S. Euclid Ave., St. Louis, MO, 63110, USA.
| | - Randi Foraker
- Institute for Informatics, Division of General Medical Sciences, Washington University School of Medicine, St. Louis, MO, USA
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Conrad K, Shoenfeld Y, Fritzler MJ. Precision health: A pragmatic approach to understanding and addressing key factors in autoimmune diseases. Autoimmun Rev 2020; 19:102508. [PMID: 32173518 DOI: 10.1016/j.autrev.2020.102508] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 11/06/2019] [Indexed: 02/07/2023]
Abstract
The past decade has witnessed a significant paradigm shift in the clinical approach to autoimmune diseases, lead primarily by initiatives in precision medicine, precision health and precision public health initiatives. An understanding and pragmatic implementation of these approaches require an understanding of the drivers, gaps and limitations of precision medicine. Gaining the trust of the public and patients is paramount but understanding that technologies such as artificial intelligences and machine learning still require context that can only be provided by human input or what is called augmented machine learning. The role of genomics, the microbiome and proteomics, such as autoantibody testing, requires continuing refinement through research and pragmatic approaches to their use in applied precision medicine.
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Affiliation(s)
- Karsten Conrad
- Institute of Immunology, Medical Faculty "Carl Gustav Carus", Technical University of Dresden, Dresden, Germany
| | - Yehuda Shoenfeld
- Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel; Department of Medicine, Sheba Medical Center, Tel Hashomer, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Marvin J Fritzler
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
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Charlton PH, Mariscal Harana J, Vennin S, Li Y, Chowienczyk P, Alastruey J. Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes. Am J Physiol Heart Circ Physiol 2019; 317:H1062-H1085. [PMID: 31442381 PMCID: PMC6879924 DOI: 10.1152/ajpheart.00218.2019] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/09/2019] [Accepted: 07/28/2019] [Indexed: 11/22/2022]
Abstract
The arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health. It is widely measured by both consumer and clinical devices. However, the physical determinants of the PW are not yet fully understood, and the development of PW analysis algorithms is limited by a lack of PW data sets containing reference CV measurements. Our aim was to create a database of PWs simulated by a computer to span a range of CV conditions, representative of a sample of healthy adults. The typical CV properties of 25-75 yr olds were identified through a literature review. These were used as inputs to a computational model to simulate PWs for subjects of each age decade. Pressure, flow velocity, luminal area, and photoplethysmographic PWs were simulated at common measurement sites, and PW indexes were extracted. The database, containing PWs from 4,374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data. Good agreement was observed, with well-reproduced age-related changes in hemodynamic parameters and PW morphology. The utility of the database was demonstrated through case studies providing novel hemodynamic insights, in silico assessment of PW algorithms, and pilot data to inform the design of clinical PW algorithm assessments. In conclusion, the publicly available PW database is a valuable resource for understanding CV determinants of PWs and for the development and preclinical assessment of PW analysis algorithms. It is particularly useful because the exact CV properties that generated each PW are known.NEW & NOTEWORTHY First, a comprehensive literature review of changes in cardiovascular properties with age was performed. Second, an approach for simulating pulse waves (PWs) at different ages was designed and verified against in vivo data. Third, a PW database was created, and its utility was illustrated through three case studies investigating the determinants of PW indexes. Fourth, the database and tools for creating the database, analyzing PWs, and replicating the case studies are freely available.
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Affiliation(s)
- Peter H Charlton
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Jorge Mariscal Harana
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Samuel Vennin
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
- Department of Clinical Pharmacology, King's College London, King's Health Partners, London, United Kingdom
| | - Ye Li
- Department of Clinical Pharmacology, King's College London, King's Health Partners, London, United Kingdom
| | - Phil Chowienczyk
- Department of Clinical Pharmacology, King's College London, King's Health Partners, London, United Kingdom
| | - Jordi Alastruey
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
- Institute of Personalized Medicine, Sechenov University, Moscow, Russia
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Nag N, Jain R. A Navigational Approach to Health: Actionable Guidance for Improved Quality of Life. COMPUTER 2019; 52:12-20. [PMID: 31213725 PMCID: PMC6581511 DOI: 10.1109/mc.2018.2883280] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Health and well-being are shaped by how lifestyle and the environment interact with biological machines. A navigational paradigm can help users reach a specific health goal by using constantly captured measurements to estimate how their health is continuously changing and provide actionable guidance.
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Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25:44-56. [PMID: 30617339 DOI: 10.1038/s41591-018-0300-7] [Citation(s) in RCA: 2121] [Impact Index Per Article: 424.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 11/12/2018] [Indexed: 11/08/2022]
Abstract
The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
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Affiliation(s)
- Eric J Topol
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA.
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