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
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