Smart transplant+: A HyCARE hybrid AI–cloud framework for intelligent donor–recipient matching, workflow automation, and post-transplant optimization

IF 1.6 4区医学 Q4 IMMUNOLOGY

Transplant immunology Pub Date : 2026-02-01 Epub Date: 2025-11-27 DOI:10.1016/j.trim.2025.102332

Winner Pulakhandam , Archana Chaluvadi , Visrutatma Rao Vallu , R. Padmavathy

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引用次数: 0

Abstract

Organ transplantation is a life-saving medical intervention to reverse end-stage organ failure. Despite its life-saving potential, organ transplantation faces inefficiencies like organ shortages, long wait times, and rejection risks due to manual, clinically limited donor-recipient matching. The rapid growth of AI and cloud computing offers new opportunities to enhance organ transplantation. This study proposes Smart Transplant+, a HyCARE system enabling intelligent matching, decision-making, and process automation. The architecture leverages a huge Organ Transplant Dataset and the most advanced methods such as Feedforward Neural Networks and Genetic Algorithms to maximize donor-recipient matching. Gated Recurrent Units are utilized in pre-transplant risk prediction, and post-transplant care is augmented with real-time tracking by IoT-based wearable sensors. The system has been programmed using Python, along with software tools like TensorFlow for machine learning and AES encryption for secure data storage and transmission. The Smart Transplant+ system provides 95–98 % accuracy which is higher than existing methods in identifying suitable donors and recipients and the potential for successful transplantation, and greatly enhances organ transplant efficiency and success rate. This book illustrates the revolutionary potential of synergizing IoT, cloud technology, and AI to optimize transplant care and improve outcomes.

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智能移植+:HyCARE混合人工智能云框架，用于智能供体-受体匹配、工作流程自动化和移植后优化。

器官移植是一种挽救生命的医疗干预措施，以逆转终末期器官衰竭。尽管器官移植具有挽救生命的潜力，但由于人工、临床有限的供体-受体匹配，器官移植面临着器官短缺、等待时间长和排斥风险等低效率问题。人工智能和云计算的快速发展为加强器官移植提供了新的机遇。本研究提出了智能移植+，一个HyCARE系统，实现智能匹配，决策和过程自动化。该架构利用巨大的器官移植数据集和最先进的方法，如前馈神经网络和遗传算法，以最大限度地实现供体-受体匹配。门控复发单元用于移植前风险预测，移植后护理通过基于物联网的可穿戴传感器进行实时跟踪。该系统使用Python编程，以及用于机器学习的TensorFlow和用于安全数据存储和传输的AES加密等软件工具。智能移植+系统在识别合适的供体和受体以及移植成功的可能性方面提供95-98 %的准确率，大大提高了器官移植的效率和成功率。这本书说明了协同物联网、云技术和人工智能优化移植护理和改善结果的革命性潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Transplant immunology 医学-免疫学

CiteScore

2.10

自引率

13.30%

发文量

198

审稿时长

48 days

期刊介绍： Transplant Immunology will publish up-to-date information on all aspects of the broad field it encompasses. The journal will be directed at (basic) scientists, tissue typers, transplant physicians and surgeons, and research and data on all immunological aspects of organ-, tissue- and (haematopoietic) stem cell transplantation are of potential interest to the readers of Transplant Immunology. Original papers, Review articles and Hypotheses will be considered for publication and submitted manuscripts will be rapidly peer-reviewed and published. They will be judged on the basis of scientific merit, originality, timeliness and quality.