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Padte S, Samala Venkata V, Mehta P, Tawfeeq S, Kashyap R, Surani S. 21st century critical care medicine: An overview. World J Crit Care Med 2024; 13:90176. [PMID: 38633477 PMCID: PMC11019625 DOI: 10.5492/wjccm.v13.i1.90176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/28/2023] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units (ICUs). This abstract provides a concise summary of the latest developments in critical care, highlighting key areas of innovation. Recent advancements in critical care include Precision Medicine: Tailoring treatments based on individual patient characteristics, genomics, and biomarkers to enhance the effectiveness of therapies. The objective is to describe the recent advancements in Critical Care Medicine. Telemedicine: The integration of telehealth technologies for remote patient monitoring and consultation, facilitating timely interventions. Artificial intelligence (AI): AI-driven tools for early disease detection, predictive analytics, and treatment optimization, enhancing clinical decision-making. Organ Support: Advanced life support systems, such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support. Infection Control: Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections. Ventilation Strategies: Precision ventilation modes and lung-protective strategies to minimize ventilator-induced lung injury. Sepsis Management: Early recognition and aggressive management of sepsis with tailored interventions. Patient-Centered Care: A shift towards patient-centered care focusing on psychological and emotional well-being in addition to medical needs. We conducted a thorough literature search on PubMed, EMBASE, and Scopus using our tailored strategy, incorporating keywords such as critical care, telemedicine, and sepsis management. A total of 125 articles meeting our criteria were included for qualitative synthesis. To ensure reliability, we focused only on articles published in the English language within the last two decades, excluding animal studies, in vitro/molecular studies, and non-original data like editorials, letters, protocols, and conference abstracts. These advancements reflect a dynamic landscape in critical care medicine, where technology, research, and patient-centered approaches converge to improve the quality of care and save lives in ICUs. The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.
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Affiliation(s)
- Smitesh Padte
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
| | | | - Priyal Mehta
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
| | - Sawsan Tawfeeq
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
| | - Rahul Kashyap
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
- Department of Research, WellSpan Health, York, PA 17403, United States
- Department of Pulmonary & Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Salim Surani
- Department of Pulmonary & Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
- Department of Medicine & Pharmacology, Texas A&M University, College Station, TX 77843, United States
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Rovati L, Gary PJ, Cubro E, Dong Y, Kilickaya O, Schulte PJ, Zhong X, Wörster M, Kelm DJ, Gajic O, Niven AS, Lal A. Development and usability testing of a patient digital twin for critical care education: a mixed methods study. Front Med (Lausanne) 2024; 10:1336897. [PMID: 38274456 PMCID: PMC10808677 DOI: 10.3389/fmed.2023.1336897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
Background Digital twins are computerized patient replicas that allow clinical interventions testing in silico to minimize preventable patient harm. Our group has developed a novel application software utilizing a digital twin patient model based on electronic health record (EHR) variables to simulate clinical trajectories during the initial 6 h of critical illness. This study aimed to assess the usability, workload, and acceptance of the digital twin application as an educational tool in critical care. Methods A mixed methods study was conducted during seven user testing sessions of the digital twin application with thirty-five first-year internal medicine residents. Qualitative data were collected using a think-aloud and semi-structured interview format, while quantitative measurements included the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and a short survey. Results Median SUS scores and NASA-TLX were 70 (IQR 62.5-82.5) and 29.2 (IQR 22.5-34.2), consistent with good software usability and low to moderate workload, respectively. Residents expressed interest in using the digital twin application for ICU rotations and identified five themes for software improvement: clinical fidelity, interface organization, learning experience, serious gaming, and implementation strategies. Conclusion A digital twin application based on EHR clinical variables showed good usability and high acceptance for critical care education.
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Affiliation(s)
- Lucrezia Rovati
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Phillip J. Gary
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Edin Cubro
- Department of Information Technology, Mayo Clinic, Rochester, MN, United States
| | - Yue Dong
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States
| | - Oguz Kilickaya
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Phillip J. Schulte
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Xiang Zhong
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
| | - Malin Wörster
- Center for Anesthesiology and Intensive Care Medicine, Department of Anesthesiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Diana J. Kelm
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Ognjen Gajic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Alexander S. Niven
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
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Montgomery AJ, Litell J, Dang J, Flurin L, Gajic O, Lal A. Gaining consensus on expert rule statements for acute respiratory failure digital twin patient model in intensive care unit using a Delphi method. Biomol Biomed 2023; 23:1108-1117. [PMID: 37431943 PMCID: PMC10655890 DOI: 10.17305/bb.2023.9344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/29/2023] [Accepted: 06/29/2023] [Indexed: 07/12/2023]
Abstract
Digital twin technology is a virtual depiction of a physical product and has been utilized in many fields. Digital twin patient model in healthcare is a virtual patient that provides opportunities to test the outcomes of various interventions virtually without subjecting an actual patient to possible harm. This can serve as a decision aid in the complex environment of the intensive care unit (ICU). Our objective is to develop consensus among a multidisciplinary expert panel on statements regarding respiratory pathophysiology contributing to respiratory failure in the medical ICU. We convened a panel of 34 international critical care experts. Our group modeled elements of respiratory failure pathophysiology using directed acyclic graphs (DAGs) and derived expert statements describing associated ICU clinical practices. The experts participated in three rounds of modified Delphi to gauge agreement on 78 final questions (13 statements with 6 substatements for each) using a Likert scale. A modified Delphi process achieved agreement for 62 of the final expert rule statements. Statements with the highest degree of agreement included the physiology, and management of airway obstruction decreasing alveolar ventilation and ventilation-perfusion matching. The lowest agreement statements involved the relationship between shock and hypoxemic respiratory failure due to heightened oxygen consumption and dead space. Our study proves the utility of a modified Delphi method to generate consensus to create expert rule statements for further development of a digital twin-patient model with acute respiratory failure. A substantial majority of expert rule statements used in the digital twin design align with expert knowledge of respiratory failure in critically ill patients.
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Affiliation(s)
| | - John Litell
- Department of Emergency Critical Care, Abbott Northwestern, Minneapolis, USA
| | - Johnny Dang
- Department of Neurology, Cleveland Clinic, Cleveland, USA
| | - Laure Flurin
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, USA
| | - Ognjen Gajic
- Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, USA
| | - Amos Lal
- Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, USA
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Garcia-Mendez JP, Lal A, Herasevich S, Tekin A, Pinevich Y, Lipatov K, Wang HY, Qamar S, Ayala IN, Khapov I, Gerberi DJ, Diedrich D, Pickering BW, Herasevich V. Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review. Bioengineering (Basel) 2023; 10:1155. [PMID: 37892885 PMCID: PMC10604310 DOI: 10.3390/bioengineering10101155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/15/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.
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Affiliation(s)
- Juan P. Garcia-Mendez
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Svetlana Herasevich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Aysun Tekin
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Yuliya Pinevich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
- Department of Cardiac Anesthesiology and Intensive Care, Republican Clinical Medical Center, 223052 Minsk, Belarus
| | - Kirill Lipatov
- Division of Pulmonary Medicine, Mayo Clinic Health Systems, Essentia Health, Duluth, MN 55805, USA
| | - Hsin-Yi Wang
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
- Department of Anesthesiology, Taipei Veterans General Hospital, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320317, Taiwan
| | - Shahraz Qamar
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Ivan N. Ayala
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Ivan Khapov
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | | | - Daniel Diedrich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Brian W. Pickering
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
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Dang J, Lal A, Montgomery A, Flurin L, Litell J, Gajic O, Rabinstein A. Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit. BMC Neurol 2023; 23:161. [PMID: 37085850 PMCID: PMC10121414 DOI: 10.1186/s12883-023-03192-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/30/2023] [Indexed: 04/23/2023] Open
Abstract
INTRODUCTION Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create a model of the Earth for climate research. While digital twins hold much promise for the neurocritical care unit, the question remains on how to best establish the rules that govern these models. This model will expand on our group's existing digital twin model for the treatment of sepsis. METHODS The authors of this project collaborated to create a Direct Acyclic Graph (DAG) and an initial series of 20 DELPHI statements, each with six accompanying sub-statements that captured the pathophysiology surrounding the management of acute ischemic strokes in the practice of Neurocritical Care (NCC). Agreement from a panel of 18 experts in the field of NCC was collected through a 7-point Likert scale with consensus defined a-priori by ≥ 80% selection of a 6 ("agree") or 7 ("strongly agree"). The endpoint of the study was defined as the completion of three separate rounds of DELPHI consensus. DELPHI statements that had met consensus would not be included in subsequent rounds of DELPHI consensus. The authors refined DELPHI statements that did not reach consensus with the guidance of de-identified expert comments for subsequent rounds of DELPHI. All DELPHI statements that reached consensus by the end of three rounds of DELPHI consensus would go on to be used to inform the construction of the digital twin model. RESULTS After the completion of three rounds of DELPHI, 93 (77.5%) statements reached consensus, 11 (9.2%) statements were excluded, and 16 (13.3%) statements did not reach a consensus of the original 120 DELPHI statements. CONCLUSION This descriptive study demonstrates the use of the DELPHI process to generate consensus among experts and establish a set of rules for the development of a digital twin model for use in the neurologic ICU. Compared to associative models of AI, which develop rules based on finding associations in datasets, digital twin AI created by the DELPHI process are easily interpretable models based on a current understanding of underlying physiology.
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Affiliation(s)
- Johnny Dang
- Department of Neurology, Cleveland Clinic, Cleveland, USA
| | - Amos Lal
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, USA.
| | | | - Laure Flurin
- Infectious Diseases Research Laboratory, Mayo Clinic, Rochester, USA
- Department of Critical Care, University Hospital of Guadeloupe, Guadeloupe, France
| | - John Litell
- Abbott Northwestern Emergency Critical Care, Minneapolis, USA
| | - Ognjen Gajic
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, USA
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Gary PJ, Lal A, Simonetto DA, Gajic O, Gallo de Moraes A. Acute on chronic liver failure: prognostic models and artificial intelligence applications. Hepatol Commun 2023; 7:02009842-202304010-00015. [PMID: 36972378 PMCID: PMC10043584 DOI: 10.1097/hc9.0000000000000095] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 01/04/2023] [Indexed: 03/29/2023] Open
Abstract
Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.
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Affiliation(s)
- Phillip J Gary
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Amos Lal
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Ognjen Gajic
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Alice Gallo de Moraes
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
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Coorey G, Figtree GA, Fletcher DF, Snelson VJ, Vernon ST, Winlaw D, Grieve SM, McEwan A, Yang JYH, Qian P, O'Brien K, Orchard J, Kim J, Patel S, Redfern J. The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field. NPJ Digit Med 2022; 5:126. [PMID: 36028526 PMCID: PMC9418270 DOI: 10.1038/s41746-022-00640-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.
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Affiliation(s)
- Genevieve Coorey
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia. .,The George Institute for Global Health, Sydney, NSW, Australia.
| | - Gemma A Figtree
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David F Fletcher
- University of Sydney, School of Chemical and Biomolecular Engineering, Sydney, NSW, Australia
| | - Victoria J Snelson
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Stephen Thomas Vernon
- Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia.,Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David Winlaw
- Cincinnati Children's Hospital Medical Cente, Cincinnati, OH, USA
| | - Stuart M Grieve
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Alistair McEwan
- The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia
| | - Jean Yee Hwa Yang
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Pierre Qian
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Westmead Applied Research Centre, Westmead Hospital, Sydney, NSW, Australia
| | - Kieran O'Brien
- Siemens Healthcare Pty Ltd; and Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Jessica Orchard
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Jinman Kim
- University of Sydney, School of Computer Science, Sydney, NSW, Australia
| | - Sanjay Patel
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Royal Prince Alfred Hospital, Sydney, NSW, Australia.,Heart Research Institute, Sydney, NSW, Australia
| | - Julie Redfern
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia
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Vyas S, Shabaz M, Pandit P, Parvathy LR, Ofori I, Al-farga A. Integration of Artificial Intelligence and Blockchain Technology in Healthcare and Agriculture. J FOOD QUALITY 2022; 2022:1-11. [DOI: 10.1155/2022/4228448] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Over the last decade, the healthcare sector has accelerated its digitization and electronic health records (EHRs). As information technology progresses, the notion of intelligent health also gathers popularity. By combining technologies such as the internet of things (IoT) and artificial intelligence (AI), innovative healthcare modifies and enhances traditional medical systems in terms of efficiency, service, and personalization. On the other side, intelligent healthcare systems are incredibly vulnerable to data breaches and other malicious assaults. Recently, blockchain technology has emerged as a potentially transformative option for enhancing data management, access control, and integrity inside healthcare systems. Integrating these advanced approaches in agriculture is critical for managing food supply chains, drug supply chains, quality maintenance, and intelligent prediction. This study reviews the literature, formulates a research topic, and analyzes the applicability of blockchain to the agriculture/food industry and healthcare, with a particular emphasis on AI and IoT. This article summarizes research on the newest blockchain solutions paired with AI technologies for strengthening and inventing new technological standards for the healthcare ecosystems and food industry.
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Zhang Z, Kashyap R, Liu N, Su L, Meng Q. Editorial: Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume II. Front Med (Lausanne) 2022; 9:910163. [PMID: 35602491 PMCID: PMC9121731 DOI: 10.3389/fmed.2022.910163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Zhongheng Zhang
| | - Rahul Kashyap
- Critical Care Independent Multidisciplinary Program, Mayo Clinic, Rochester, MN, United States
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Longxiang Su
- State Key Laboratory of Complex Severe and Rare Diseases, Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Peking Union Medical College, Beijing, China
| | - Qinghe Meng
- Department of Surgery, SUNY Upstate Medical University, Syracuse, NY, United States
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Abstract
CONTEXT Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children. OBJECTIVE We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care. DATA SOURCE PubMed, IEEE Xplore, Cochrane, and Web of Science databases. STUDY SELECTION We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality). DATA EXTRACTION Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report. RESULTS Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization. CONCLUSIONS Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.
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Affiliation(s)
- Claudette O Adegboro
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Avishek Choudhury
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Onur Asan
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Michelle M Kelly
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
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Dang J, Lal A, Flurin L, James A, Gajic O, Rabinstein AA. Predictive modeling in neurocritical care using causal artificial intelligence. World J Crit Care Med 2021; 10:112-119. [PMID: 34316446 PMCID: PMC8291004 DOI: 10.5492/wjccm.v10.i4.112] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/17/2021] [Accepted: 07/02/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.
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Affiliation(s)
- Johnny Dang
- Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Laure Flurin
- Division of Clinical Microbiology, Mayo Clinic, Rochester, MN 55905, United States
| | - Amy James
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Ognjen Gajic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Alejandro A Rabinstein
- Department of Medicine, Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN 55905, United States
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Lal A, Herasevich V, Gajic O. Utility of AI models in critical care: union of man and the machine. Crit Care 2021; 25:46. [PMID: 33531063 PMCID: PMC7852115 DOI: 10.1186/s13054-021-03478-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 01/25/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Amos Lal
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
| | - Vitaly Herasevich
- Division of Critical Care, Department of Anesthesiology and Perioperative Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Ognjen Gajic
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
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Zhang Z, Liu N, Meng Q, Su L. Editorial: Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume I. Front Med (Lausanne) 2021; 8:809478. [PMID: 34938754 PMCID: PMC8685312 DOI: 10.3389/fmed.2021.809478] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/22/2021] [Indexed: 02/05/2023] Open
Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Zhongheng Zhang
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Qinghe Meng
- Department of Surgery, State University of New York Upstate Medical University, Syracuse, NY, United States
| | - Longxiang Su
- State Key Laboratory of Complex Severe and Rare Diseases, Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
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Lal A, Li G, Cubro E, Chalmers S, Li H, Herasevich V, Dong Y, Pickering BW, Kilickaya O, Gajic O. Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response During the First 24 Hours of Sepsis. Crit Care Explor 2020; 2:e0249. [PMID: 33225302 PMCID: PMC7671877 DOI: 10.1097/cce.0000000000000249] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
To develop and verify a digital twin model of critically ill patient using the causal artificial intelligence approach to predict the response to specific treatment during the first 24 hours of sepsis. DESIGN Directed acyclic graphs were used to define explicitly the causal relationship among organ systems and specific treatments used. A hybrid approach of agent-based modeling, discrete-event simulation, and Bayesian network was used to simulate treatment effect across multiple stages and interactions of major organ systems (cardiovascular, neurologic, renal, respiratory, gastrointestinal, inflammatory, and hematology). Organ systems were visualized using relevant clinical markers. The application was iteratively revised and debugged by clinical experts and engineers. Agreement statistics was used to test the performance of the model by comparing the observed patient response versus the expected response (primary and secondary) predicted by digital twin. SETTING Medical ICU of a large quaternary- care academic medical center in the United States. PATIENTS OR SUBJECTS Adult (> 18 year yr old), medical ICU patients were included in the study. INTERVENTIONS No additional interventions were made beyond the standard of care for this study. MEASUREMENTS AND MAIN RESULTS During the verification phase, model performance was prospectively tested on 145 observations in a convenience sample of 29 patients. Median age was 60 years (54-66 d) with a median Sequential Organ Failure Assessment score of 9.5 (interquartile range, 5.0-14.0). The most common source of sepsis was pneumonia, followed by hepatobiliary. The observations were made during the first 24 hours of the ICU admission with one-step interventions, comparing the output in the digital twin with the real patient response. The agreement between the observed versus and the expected response ranged from fair (kappa coefficient of 0.41) for primary response to good (kappa coefficient of 0.65) for secondary response to the intervention. The most common error detected was coding error in 50 observations (35%), followed by expert rule error in 29 observations (20%) and timing error in seven observations (5%). CONCLUSIONS We confirmed the feasibility of development and prospective testing of causal artificial intelligence model to predict the response to treatment in early stages of critical illness. The availability of qualitative and quantitative data and a relatively short turnaround time makes the ICU an ideal environment for development and testing of digital twin patient models. An accurate digital twin model will allow the effect of an intervention to be tested in a virtual environment prior to use on real patients.
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Affiliation(s)
- Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN
| | - Guangxi Li
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN
| | - Edin Cubro
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN
| | - Sarah Chalmers
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN
| | - Heyi Li
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN
| | - Yue Dong
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN
| | - Brian W Pickering
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN
| | - Oguz Kilickaya
- Department of Anesthesiology and Critical Care, Altinbas University, Bahcelievler Medical Park Hospital, Istanbul, Turkey
| | - Ognjen Gajic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN
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Jin HY, Zhang M, Hu B. Techniques to integrate artificial intelligence systems with medical information in gastroenterology. Artif Intell Gastrointest Endosc 2020; 1:19-27. [DOI: 10.37126/aige.v1.i1.19] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 07/07/2020] [Accepted: 07/14/2020] [Indexed: 02/06/2023] Open
Abstract
Gastrointestinal (GI) endoscopy is the central element in contemporary gastroenterology as it provides direct evidence to guide targeted therapy. To increase the accuracy of GI endoscopy and to reduce human-related errors, artificial intelligence (AI) has been applied in GI endoscopy, which has been proved to be effective in diagnosing and treating numerous diseases. Therefore, we review current research on the efficacy of AI-assisted GI endoscopy in order to assess its functions, advantages and how the design can be improved.
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Affiliation(s)
- Hong-Yu Jin
- Department of Liver Surgery, Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Man Zhang
- Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, Endoscopy Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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