Edwin Morley-Fletcher

Bio: Edwin Morley-Fletcher is an academic researcher. The author has contributed to research in topics: In silico clinical trials & In silico medicine. The author has an hindex of 2, co-authored 2 publications receiving 208 citations.

In silico clinical trials: how computer simulation will transform the biomedical industry

Abstract: The term ‘in silico clinical trials indicates the use of individualised computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention. This review article summarises the research and technological roadmap developed by the Avicenna Support Action during an 18 month consensus process that involved 577 international experts from academia, the biomedical industry, the simulation industry, the regulatory world, etc. The roadmap documents early examples of in silico clinical trials, identifies relevant use cases for in silico clinical trial technologies over the entire development and assessment cycle for both pharmaceuticals and medical devices, identifies open challenges and barriers to a wider adoption and puts forward 36 recommendations for all relevant stakeholders to consider .

A survey on digital twin : definitions, characteristics, applications, and design Implications

Abstract: When, in 1956, Artificial Intelligence (AI) was officially declared a research field, no one would have ever predicted the huge influence and impact its description, prediction, and prescription capabilities were going to have on our daily lives. In parallel to continuous advances in AI, the past decade has seen the spread of broadband and ubiquitous connectivity, (embedded) sensors collecting descriptive high dimensional data, and improvements in big data processing techniques and cloud computing. The joint usage of such technologies has led to the creation of digital twins, artificial intelligent virtual replicas of physical systems. Digital Twin (DT) technology is nowadays being developed and commercialized to optimize several manufacturing and aviation processes, while in the healthcare and medicine fields this technology is still at its early development stage. This paper presents the results of a study focused on the analysis of the state-of-the-art definitions of DT, the investigation of the main characteristics that a DT should possess, and the exploration of the domains in which DT applications are currently being developed. The design implications derived from the study are then presented: they focus on socio-technical design aspects and DT lifecycle. Open issues and challenges that require to be addressed in the future are finally discussed.

The 'Digital Twin' to enable the vision of precision cardiology.

Abstract: Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

Computational models in cardiology

Abstract: The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.