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
  • [41] Educatio all Advances in Artificial intelligence
    Brown, Laura E.
    Kauchak, David
    AI MAGAZINE, 2013, 34 (04) : 127 - 127
  • [42] Advances in Computer Science and Artificial Intelligence
    Zacarias Flores, Fernando
    Arrazola Ramirez, Jose
    Estrada Estrada, Oscar
    ENGINEERING LETTERS, 2007, 15 (02)
  • [43] Avenues in IoT with advances in Artificial Intelligence
    Mullick, Ankan
    Gupta, Mukur
    2024 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS AND OTHER AFFILIATED EVENTS, PERCOM WORKSHOPS, 2024, : 314 - 319
  • [44] Advances in artificial intelligence research in health
    Khanna, Sankalp
    Sattar, Abdul
    Hansen, David
    AUSTRALASIAN MEDICAL JOURNAL, 2012, 5 (09): : 475 - 477
  • [45] Advances in Weighted Logics for Artificial Intelligence
    Finger, Marcelo
    Godo, Lluis
    Prade, Henri
    Qi, Guilin
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2017, 88 : 385 - 386
  • [46] Advances in Artificial Intelligence for the Underwater Domain
    Gratton, Michael B.
    MARINE TECHNOLOGY SOCIETY JOURNAL, 2019, 53 (05) : 68 - 74
  • [47] Advances of Artificial Intelligence in Mechanical Engineering
    Talatahari, Siamak
    Chen, Shengyong
    Gandomi, Amir H.
    Alavi, Amir H.
    ADVANCES IN MECHANICAL ENGINEERING, 2014,
  • [48] Advances in neuroradiology II: Artificial intelligence
    Morales, Humberto
    SEMINARS IN ULTRASOUND CT AND MRI, 2022, 43 (02) : 131 - 132
  • [49] Recent Advances in Artificial Intelligence Sensors
    Zhang, Zixuan
    Wang, Luwei
    Lee, Chengkuo
    ADVANCED SENSOR RESEARCH, 2023, 2 (08):
  • [50] Advances in Theory and Applications of Artificial Intelligence
    Fujita, Hamido
    Fournier-Viger, Philippe
    Sasaki, Jun
    Ali, Moonis
    AI MAGAZINE, 2021, 42 (01) : 86 - 87