Artificial Intelligence Advances in Transplant Pathology

被引:8
作者
Rahman, Md Arafatur [1 ,2 ]
Yilmaz, Ibrahim [1 ,3 ]
Albadri, Sam T. [1 ]
Salem, Fadi E. [1 ]
Dangott, Bryan J. [1 ,3 ]
Taner, C. Burcin [4 ]
Nassar, Aziza [1 ]
Akkus, Zeynettin [1 ,3 ]
Alper, Cuneyt M.
机构
[1] Mayo Clin, Dept Lab Med & Pathol, Jacksonville, FL 32224 USA
[2] Florida State Univ, Dept Math, Tallahassee, FL 32306 USA
[3] Mayo Clin, Computat Pathol & Artificial Intelligence, Jacksonville, FL 32224 USA
[4] Mayo Clin, Dept Transplantat Surg, Jacksonville, FL 32224 USA
来源
BIOENGINEERING-BASEL | 2023年 / 10卷 / 09期
关键词
transplant pathology; artificial intelligence; kidney transplant; heart transplant; liver transplant; lung transplant; digital pathology;
D O I
10.3390/bioengineering10091041
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology
    Yoshida, Hiroshi
    Kiyuna, Tomoharu
    WORLD JOURNAL OF GASTROENTEROLOGY, 2021, 27 (21) : 2818 - 2833
  • [22] Implementation of Digital Pathology and Artificial fi cial Intelligence in Routine Pathology Practice
    Zhang, David Y.
    Venkat, Arsha
    Khasawneh, Hamdi
    Sali, Rasoul
    Zhang, Valerio
    Pei, Zhiheng
    LABORATORY INVESTIGATION, 2024, 104 (09)
  • [23] Artificial intelligence applied to breast pathology
    Mustafa Yousif
    Paul J. van Diest
    Arvydas Laurinavicius
    David Rimm
    Jeroen van der Laak
    Anant Madabhushi
    Stuart Schnitt
    Liron Pantanowitz
    Virchows Archiv, 2022, 480 : 191 - 209
  • [24] Artificial intelligence applied to breast pathology
    Yousif, Mustafa
    van Diest, Paul J.
    Laurinavicius, Arvydas
    Rimm, David
    van der Laak, Jeroen
    Madabhushi, Anant
    Schnitt, Stuart
    Pantanowitz, Liron
    VIRCHOWS ARCHIV, 2022, 480 (01) : 191 - 209
  • [25] Application of Artificial Intelligence in Shoulder Pathology
    Cheng, Cong
    Liang, Xinzhi
    Guo, Dong
    Xie, Denghui
    DIAGNOSTICS, 2024, 14 (11)
  • [26] Artificial Intelligence in the Pathology of Gastric Cancer
    Choi, Sangjoon
    Kim, Seokhwi
    JOURNAL OF GASTRIC CANCER, 2023, 23 (03) : 410 - 427
  • [27] Systematic Analysis of Artificial Intelligence in Pathology
    Uppala, Divya
    Ogirala, Smyrna
    Gadam, Leela Lavanya
    Kumar, Tompala Vinod
    ORAL & MAXILLOFACIAL PATHOLOGY JOURNAL, 2023, 14 (01) : 142 - 144
  • [28] Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology
    Palazzi, Xavier
    Barale-Thomas, Erio
    Bawa, Bhupinder
    Carter, Jonathan
    Janardhan, Kyathanahalli
    Marxfeld, Heike
    Nyska, Abraham
    Saravanan, Chandrassegar
    Schaudien, Dirk
    Schumacher, Vanessa L.
    Spaet, Robert H.
    Tangermann, Simone
    Turner, Oliver C.
    Vezzali, Enrico
    TOXICOLOGIC PATHOLOGY, 2023, 51 (04) : 216 - 224
  • [29] The ethical challenges of artificial intelligence-driven digital pathology
    McKay, Francis
    Williams, Bethany J.
    Prestwich, Graham
    Bansal, Daljeet
    Hallowell, Nina
    Treanor, Darren
    JOURNAL OF PATHOLOGY CLINICAL RESEARCH, 2022, 8 (03) : 209 - 216
  • [30] Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology
    Lancellotti, Cesare
    Cancian, Pierandrea
    Savevski, Victor
    Kotha, Soumya Rupa Reddy
    Fraggetta, Filippo
    Graziano, Paolo
    Di Tommaso, Luca
    CELLS, 2021, 10 (04)