Literature DB >> 33290181

The Potential of a Digital Twin in Surgery.

Hanad Ahmed1, Laurence Devoto2.   

Abstract

Entities:  

Year:  2020        PMID: 33290181      PMCID: PMC8381595          DOI: 10.1177/1553350620975896

Source DB:  PubMed          Journal:  Surg Innov        ISSN: 1553-3506            Impact factor:   2.058


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Introduction

Digital twins are virtual replicas of physical entities that go beyond a still image and encompass the dynamic functionality of the real-life object.[1,2] They are widely used in industries such as construction and aviation. Their advent is said to mark the fourth industrial revolution for the innovation of new products and services.[1] The concept is increasingly entering the healthcare industry with the aim of creating molecular and phenotypic copies of human beings that can allow for trialling of different therapies to elucidate the most efficacious treatment for the real-life patient.[2] Although the literature is increasingly discussing the potential for medical specialities such as cardiology and oncology,[3,4] there are few articles discussing their potential in surgical practice.

Digital Twins in Surgical Practice

The ethos of a surgical digital twin is the idea that a patient model is created, and surgery can be planned in the multidisciplinary team meeting, practised beforehand in a simulator and referenced during the operation to verify anatomy and avoid inadvertent damage to structures. This real-time model of the patient could also give rise to clinical trials where new instruments, techniques or therapies are first tried on the digital twin, minimising risk to the patient. Digital twins combined with the increasingly developing virtual reality platforms can also enhance surgical training for residents, by allowing for simulated practice in the context of each patient’s specific anatomical and physiological variation, whilst providing a realistic account of performance with the ability to measure intraoperative metrics.[5]

Current Limitations

Current limitations are centred around tissue modelling in real time, with deformation and movement that resembles real life. This issue is mainly one of computer powers as the physics can be modelled using a number of open source programmes.

Conclusion and Future Practices

Digital twins have the potential to revolutionise surgical care, research and training. Despite the potentials, the healthcare industry is still in its infancy in being able to map the human body down to a dynamic real-time digital counterpart. Even if the technical challenges were to be overcome, there would still be ethical considerations surrounding the ability to download a detailed copy of a human being. Nonetheless, the potential to cure disease in both medicine and surgery is likely to evolve considerably in the coming decades with technology forming a key component of that evolution.
  5 in total

1.  Highly Accurate, Patient-Specific, 3-Dimensional Mixed-Reality Model Creation for Surgical Training and Decision-making.

Authors:  Laurence Devoto; Sam Muscroft; Manish Chand
Journal:  JAMA Surg       Date:  2019-10-01       Impact factor: 14.766

2.  Precision Oncology: Three Small Steps Forward.

Authors:  Hannah C Wise; David B Solit
Journal:  Cancer Cell       Date:  2019-06-10       Impact factor: 31.743

3.  Towards Digital Twin Implementation for Assessing Production Line Performance and Balancing.

Authors:  Marcello Fera; Alessandro Greco; Mario Caterino; Salvatore Gerbino; Francesco Caputo; Roberto Macchiaroli; Egidio D'Amato
Journal:  Sensors (Basel)       Date:  2019-12-23       Impact factor: 3.576

4.  Digital twins to personalize medicine.

Authors:  Bergthor Björnsson; Carl Borrebaeck; Nils Elander; Thomas Gasslander; Danuta R Gawel; Mika Gustafsson; Rebecka Jörnsten; Eun Jung Lee; Xinxiu Li; Sandra Lilja; David Martínez-Enguita; Andreas Matussek; Per Sandström; Samuel Schäfer; Margaretha Stenmarker; X F Sun; Oleg Sysoev; Huan Zhang; Mikael Benson
Journal:  Genome Med       Date:  2019-12-31       Impact factor: 11.117

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

Authors:  Jorge Corral-Acero; Francesca Margara; Maciej Marciniak; Cristobal Rodero; Filip Loncaric; Yingjing Feng; Andrew Gilbert; Joao F Fernandes; Hassaan A Bukhari; Ali Wajdan; Manuel Villegas Martinez; Mariana Sousa Santos; Mehrdad Shamohammdi; Hongxing Luo; Philip Westphal; Paul Leeson; Paolo DiAchille; Viatcheslav Gurev; Manuel Mayr; Liesbet Geris; Pras Pathmanathan; Tina Morrison; Richard Cornelussen; Frits Prinzen; Tammo Delhaas; Ada Doltra; Marta Sitges; Edward J Vigmond; Ernesto Zacur; Vicente Grau; Blanca Rodriguez; Espen W Remme; Steven Niederer; Peter Mortier; Kristin McLeod; Mark Potse; Esther Pueyo; Alfonso Bueno-Orovio; Pablo Lamata
Journal:  Eur Heart J       Date:  2020-12-21       Impact factor: 29.983

  5 in total
  4 in total

Review 1.  Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.

Authors:  Chengyue Wu; Guillermo Lorenzo; David A Hormuth; Ernesto A B F Lima; Kalina P Slavkova; Julie C DiCarlo; John Virostko; Caleb M Phillips; Debra Patt; Caroline Chung; Thomas E Yankeelov
Journal:  Biophys Rev (Melville)       Date:  2022-05-17

Review 2.  Selecting Privacy-Enhancing Technologies for Managing Health Data Use.

Authors:  Sara Jordan; Clara Fontaine; Rachele Hendricks-Sturrup
Journal:  Front Public Health       Date:  2022-03-16

Review 3.  Digital twins and the ethics of health decision-making concerning children.

Authors:  Matthias Braun; Jenny Krutzinna
Journal:  Patterns (N Y)       Date:  2022-04-08

Review 4.  Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them.

Authors:  Ulrich von Ulmenstein; Max Tretter; David B Ehrlich; Christina Lauppert von Peharnik
Journal:  Front Artif Intell       Date:  2022-08-01
  4 in total

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