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Artificial Intelligence Advances in Transplant Pathology

Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intellige...

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Autores principales: Rahman, Md Arafatur, Yilmaz, Ibrahim, Albadri, Sam T., Salem, Fadi E., Dangott, Bryan J., Taner, C. Burcin, Nassar, Aziza, Akkus, Zeynettin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525684/
https://www.ncbi.nlm.nih.gov/pubmed/37760142
http://dx.doi.org/10.3390/bioengineering10091041
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author Rahman, Md Arafatur
Yilmaz, Ibrahim
Albadri, Sam T.
Salem, Fadi E.
Dangott, Bryan J.
Taner, C. Burcin
Nassar, Aziza
Akkus, Zeynettin
author_facet Rahman, Md Arafatur
Yilmaz, Ibrahim
Albadri, Sam T.
Salem, Fadi E.
Dangott, Bryan J.
Taner, C. Burcin
Nassar, Aziza
Akkus, Zeynettin
author_sort Rahman, Md Arafatur
collection PubMed
description Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians’ decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.
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spelling pubmed-105256842023-09-28 Artificial Intelligence Advances in Transplant Pathology Rahman, Md Arafatur Yilmaz, Ibrahim Albadri, Sam T. Salem, Fadi E. Dangott, Bryan J. Taner, C. Burcin Nassar, Aziza Akkus, Zeynettin Bioengineering (Basel) Systematic Review Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians’ decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field. MDPI 2023-09-04 /pmc/articles/PMC10525684/ /pubmed/37760142 http://dx.doi.org/10.3390/bioengineering10091041 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Systematic Review
Rahman, Md Arafatur
Yilmaz, Ibrahim
Albadri, Sam T.
Salem, Fadi E.
Dangott, Bryan J.
Taner, C. Burcin
Nassar, Aziza
Akkus, Zeynettin
Artificial Intelligence Advances in Transplant Pathology
title Artificial Intelligence Advances in Transplant Pathology
title_full Artificial Intelligence Advances in Transplant Pathology
title_fullStr Artificial Intelligence Advances in Transplant Pathology
title_full_unstemmed Artificial Intelligence Advances in Transplant Pathology
title_short Artificial Intelligence Advances in Transplant Pathology
title_sort artificial intelligence advances in transplant pathology
topic Systematic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525684/
https://www.ncbi.nlm.nih.gov/pubmed/37760142
http://dx.doi.org/10.3390/bioengineering10091041
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