New Paradigms in Transplantation: Artificial Intelligence, Diagnostic Imaging and Telemedicine

A special issue of Transplantology (ISSN 2673-3943).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 8842

Special Issue Editors


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Guest Editor
Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
Interests: kidney transplantation; lifestyle and cardiovascular disease; inflammation and oxidative stress; heavy metals; kidney fibrosis; vascular calcification

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Guest Editor
1. Department of Surgery, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
2. Department of Surgery, Maasstad Hospital, PO Box 9100, 3007 AC Rotterdam, The Netherlands
Interests: surgery; solid organ transplantation; kidney transplantation; pancreas transplantation; diagnostic imaging; positron emission tomography; computed tomography; ultrasonography; vascular calcification; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

Improving long-term outcomes remains a major challenge in transplantation. Alongside, a rapidly evolving state-of-the-art in computer sciences and medical informatics has the potential of transforming research and eventually reshaping standards of transplant care in the near future. To tackle the current obstacles, the interdisciplinary collaboration of transplant professionals with big data engineers, diagnostic imaging specialists, and Telemedicine pioneers is pivotal.

In this Special Issue, we encourage the transplant community to contribute with clinical reports, concept papers, and cutting-edge reviews focusing on Artificial Intelligence (AI), Diagnostic Imaging, and Telemedicine. We particularly seek out integrative studies incorporating multi-modality data (i.e. hybrid clinical and imaging studies) to align with most advanced efforts to improve predictive models’ robustness and accuracy. Also, we invite authors to submit articles discussing current obstacles for introducing AI in the kidney transplant field, relevant general regulatory issues, and emerging clinical concerns. Finally, we make a call for studies reporting disruptive innovations in connected health, and recent advances on the applicability and broad clinical impact of telemedicine for the care of transplant recipients.

Dr. Camilo G. Sotomayor
Dr. Stan Benjamens
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Transplantology is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Outcomes
  • Graft failure
  • Risk factors
  • Prediction
  • Prognosis
  • Artificial intelligence
  • Machine learning
  • Deep learning
  • Systematic review
  • Meta-analyses
  • Telemedicine
  • Diagnostic imaging
  • Positron emission tomography
  • Computed tomography
  • Ultrasonography

Published Papers (3 papers)

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Editorial

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5 pages, 197 KiB  
Editorial
Dietary Assessment and Self-Management Using Information Technology in Order to Improve Outcomes in Kidney Transplant Recipients
by Fernanda G. Rodrigues, Martin H. de Borst and Ita P. Heilberg
Transplantology 2020, 1(2), 97-101; https://doi.org/10.3390/transplantology1020009 - 1 Nov 2020
Viewed by 2162
Abstract
Big data and artificial intelligence (AI) will transform the way research in nephrology is carried out and consequently improve the performance of clinical practice in nephrology and transplantation. Managing long-term health outcomes in kidney transplant recipients (KTR) includes the improvement of modifiable factors, [...] Read more.
Big data and artificial intelligence (AI) will transform the way research in nephrology is carried out and consequently improve the performance of clinical practice in nephrology and transplantation. Managing long-term health outcomes in kidney transplant recipients (KTR) includes the improvement of modifiable factors, such as diet. Self-management using information technology (IT) aims to facilitate lifestyle changes, manage symptoms and treatment in the course of chronic kidney disease (CKD) or any chronic condition. The advantages of health mobile applications further include the capacity of data compilation and yielding responses to numerous research questions in nephrology and transplantation. However, studies investigating the employment of such applications in KTR and its impact in kidney transplant outcomes are still lacking. The specific advantages of dietary assessment and self-management using IT in order to improve outcomes in KTR are presently discussed. This Special Issue features a great set of articles regarding IT approaches to improve kidney allograft survival and posttransplant outcomes in all areas. Full article

Review

Jump to: Editorial

11 pages, 354 KiB  
Review
Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence
by Raúl Castillo-Astorga and Camilo G. Sotomayor
Transplantology 2021, 2(2), 118-128; https://doi.org/10.3390/transplantology2020012 - 6 Apr 2021
Cited by 3 | Viewed by 2722
Abstract
After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical [...] Read more.
After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical and research challenges. The current burst of studies and other innovative initiatives using artificial intelligence (AI) for a wide range of analytical and practical applications in biomedical areas seems to correlate with the same trend observed in publications in the kidney transplantation field, and points toward the potential of such novel approaches to address the aforementioned aim of improving long-term outcomes of kidney transplant recipients (KTR). However, at the same time, this trend underscores now more than ever the old methodological challenges and potential threats that the research and clinical community needs to be aware of and actively look after with regard to AI-driven evidence. The purpose of this narrative mini-review is to explore challenges for obtaining applicable and adequate kidney transplant data for analyses using AI techniques to develop prediction models, and to propose next steps in the field. We make a call to act toward establishing the strong collaborations needed to bring innovative synergies further augmented by AI, which have the potential to impact the long-term care of KTR. We encourage researchers and clinicians to submit their invaluable research, including original clinical and imaging studies, database studies from registries, meta-analyses, and AI research in the kidney transplantation field. Full article
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12 pages, 264 KiB  
Review
Living Kidney Donation: Practical Considerations on Setting Up a Program
by Maria Irene Bellini, Vito Cantisani, Augusto Lauro and Vito D’Andrea
Transplantology 2021, 2(1), 75-86; https://doi.org/10.3390/transplantology2010008 - 10 Mar 2021
Cited by 3 | Viewed by 3252
Abstract
Living kidney donation represents the best treatment for end stage renal disease patients, with the potentiality to pre-emptively address kidney failure and significantly expand the organ pool. Unfortunately, there is still limited knowledge about this underutilized resource. The present review aims to describe [...] Read more.
Living kidney donation represents the best treatment for end stage renal disease patients, with the potentiality to pre-emptively address kidney failure and significantly expand the organ pool. Unfortunately, there is still limited knowledge about this underutilized resource. The present review aims to describe the general principles for the establishment, organization, and oversight of a successful living kidney transplantation program, highlighting recommendation for good practice and the work up of donor selection, in view of potential short- and long-terms risks, as well as the additional value of kidney paired exchange programs. The need for donor registries is also discussed, as well as the importance of lifelong follow up. Full article
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