Program of Excellence 2014-16-Siemens Program for Greece, Greece.
Stud Health Technol Inform. 2024 Aug 22;316:884-888. doi: 10.3233/SHTI240554.
To extend reliability of the integrated Tele-Radiological (TRE) and Tele-Pathological (TPE) evaluation of the Renal Graft (RG) of Prometheus Digital Medical Device (pn 2003016) via integration with Machine Organ Perfusion and Tele-Robotics (Stamoulis Rb) in Organ Transplantation.
A sensitivity-specificity analysis by a simulation of the TRE of RG on 15 MR abdominal images by a radiologist and of the TPE of RG by 26 specialists based on 130 human RG images assessing damages and lesions.
The integrated analysis of TRE and TPE of RG showed: Sensitivity=96.7%, Specificity=100% and Accuracy=97.6%. Integration of Machine Organ Perfusion based results pattern recognition and AI programming offers deep learning and improves morbidity-mortality and organ viability prognosis.
The TRE integrated with TPE and AI programming of RG machine organ perfusion based results pattern recognition by AI programming and Deep Learning supported virtual benching is feasible and seems more reliable for instant morbidity-mortality and organ viability prognosis in renal transplant decision support and operational planning.
通过与机器器官灌注和远程机器人技术(Stamoulis Rb)在器官移植中的整合,扩展 Prometheus 数字医疗设备(pn 2003016)的肾脏移植物(RG)的综合远程放射学(TRE)和远程病理学(TPE)评估的可靠性。
通过对 15 个磁共振腹部图像进行 RG 的 TRE 模拟,以及 26 名专家对 130 个人类 RG 图像的 RG 的 TPE 进行的敏感性特异性分析,评估损伤和病变。
对 RG 的 TRE 和 TPE 的综合分析显示:敏感性=96.7%,特异性=100%,准确性=97.6%。基于机器器官灌注的综合分析,基于结果模式识别和人工智能编程的整合提供了深度学习,并改善了发病率-死亡率和器官活力预后。
基于机器器官灌注的 RG 的 TRE 与 TPE 以及 AI 编程的集成,通过人工智能编程和深度学习支持的虚拟 Bench 实现了基于结果模式识别的人工智能编程,在肾移植决策支持和运营规划中的即时发病率-死亡率和器官活力预后方面似乎更可靠。