There is a growing need for precise diagnosis and personalized treatment of disease in recent years. Providing treatment tailored to each patient and maximizing efficacy and efficiency are broad goals of the healthcare system. As an engineering concept that connects the physical entity and digital space, the digital twin (DT) entered our lives at the beginning of Industry 4.0. It is evaluated as a revolution in many industrial fields and has shown the potential to be widely used in the field of medicine. This technology can offer innovative solutions for precise diagnosis and personalized treatment processes. Although there are difficulties in data collection, data fusion, and accurate simulation at this stage, we speculated that the DT may have an increasing use in the future and will become a new platform for personal health management and healthcare services. We introduced the DT technology and discussed the advantages and limitations of its applications in the medical field. This article aims to provide a perspective that combining Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technology; the DT will help establish high-resolution models of patients to achieve precise diagnosis and personalized treatment.

/pdf/the-digital-twin-in-medicine-a-key-to-the-future-of-s2lmezdt.pdf

The Digital Twin in Medicine: A Key to the Future of Healthcare?

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2022 | https://dx.doi.org/10.21037/atm-22-4203 The goal of precision medicine is to provide tailored therapy to each patient with considering therapeutic benefits and risks. Strides toward this goal have been made by harnessing the benefits of big data, the development of mathematical and data-based computational models, and the use of artificial intelligence (AI) and machine learning (ML) algorithms (1-3). The currently available mathematical models lack fidelity, are unable to represent changes in realtime, and are far from being the optimal tools to provide robust predictive enrichment that can be of use in clinical medicine. In their article, Liu et al. (4) performed an exhaustive bibliometric analysis of the currently available applications, specifically in the arena of individualized diagnosis and treatment of the critically ill patients. There has been an increasing number of articles published over the past decade, however, the clinical relevance and real-world application remains debatable. To circumvent these issues, the construction of digital twin models based on research data and physiological properties has been proposed.

/pdf/regulatory-oversight-and-ethical-concerns-surrounding-1jikdv2i.pdf

Regulatory oversight and ethical concerns surrounding software as medical device (SaMD) and digital twin technology in healthcare

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. 

/pdf/developing-delphi-expert-consensus-rules-for-a-digital-twin-1cnhwew8.pdf

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit

Background Artificial intelligence (AI) has been extensively applied in the individualized diagnosis and treatment of critical illness, and numerous studies have been published on this topic. Therefore, a bibliometric analysis of these publications should be performed to provide a direction of hot topics and future research trends. Methods A bibliometric analysis was performed on the research articles to identify the hot topics and any unsolved issues regarding the use of AI in individualized diagnosis and treatment of critical illness. Articles published from January 2011 to December 2021 were retrieved from the Web of Science (WOS) core collection database for bibliometric analysis, and a cross-sectional analysis of the relevant studies that had been registered at ClinicalTrials.gov was also conducted. Results The number of articles published showed an annually increasing trend, with a worldwide geographic distribution over the past decade. Ultimately, 427 research articles were included in the bibliometric analysis. The relevant articles were divided into four separate clusters that focused on AI application aspects, prediction model establishment, coronavirus disease 2019 (COVID-19) treatment and outcome assessments, respectively. “Machine learning” was the most frequent keyword (147 occurrences, 165 links, and 395 total link strengths) followed by “risk”, “models”, and “mortality”. With 205 articles, the United States of America (USA) had interacted the most with other countries (20 links, and 94 total link strength), while the domestic research institutes in China had infrequently collaborated with others. Approximately 130 trials focusing on the application of AI in the intensive care unit (ICU) and emergency department (ED) had been registered at ClinicalTrial.gov, and most of them (n=71, 54.6%) were interventional. The main research objectives of these trials were to provide decision making assistance and establish prediction models. However, only 3.8% (5 trials) of them had reached exact conclusions which favored the application of AI. Conclusions The application of AI has raised great interest in critical illness and has mainly been focused on decision making assistance and prediction model establishment. Cooperation between agencies engaged in AI research needs to be strengthened. An increasing number of trials have been registered at ClinicalTrial.gov, and the results of them are promising. Keywords Bibliometric analysis; artificial intelligence (AI); individualized diagnosis; critical care medicine; emergency department (ED)

/pdf/a-bibliometric-analysis-of-the-application-of-artificial-3by1jros.pdf

A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness


 UNSTRUCTURED
 With the development of the world economy and improvements in living standards, people are paying more attention to their healthcare. As simulation is playing an increasingly important role in the field of medicine, providing the individual patient with a customised diagnosis and treatment is envisage as part of future precision medicine. Such customisation will become possible through the emergence of digital twin (DT) technology. As an engineering concept that reflects the true mirror image of a physical entity, the DT is increasingly being used in the field of precision health and personalised medicine. This review summarises the progress of prominent research on DT technology in the medical field and forecasts potential applications and future opportunities and discusses several challenges remaining in digital healthcare. The author hopes that the review encourages additional efforts towards the development and acceptance of DT techniques.


Digital-twin Healthcare: A Gateway to Future Medicine (Preprint)

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.

Amy James

Papers

Predictive modeling in neurocritical care using causal artificial intelligence