Klinkachorn Mac, Tsoi-A-Sue Christian, Narayan Raja R, Kadri Haaris, Tam Taylor, Melcher Marc L
Department of Engineering, Stanford University, Stanford, CA, United States.
Department of Surgery, Stanford University, Stanford, CA, United States.
Front Transplant. 2023 Jun 23;2:1206085. doi: 10.3389/frtra.2023.1206085. eCollection 2023.
An accurate estimation of liver fat content is necessary to predict how a donated liver will function after transplantation. Currently, a pathologist needs to be available at all hours of the day, even at remote hospitals, when an organ donor is procured. Even among expert pathologists, the estimation of liver fat content is operator-dependent. Here we describe the development of a low-cost, end-to-end artificial intelligence platform to evaluate liver fat content on a donor liver biopsy slide in real-time. The hardware includes a high-resolution camera, display, and GPU to acquire and process donor liver biopsy slides. A deep learning model was trained to label and quantify fat globules in liver tissue. The algorithm was deployed on the device to enable real-time quantification and characterization of fat content for transplant decision-making. This information is displayed on the device and can also be sent to a cloud platform for further analysis.
准确估计肝脏脂肪含量对于预测捐赠肝脏在移植后的功能至关重要。目前，即使在偏远医院获取器官捐赠者时，一天中的任何时候都需要有病理学家在场。即使在专家病理学家中，肝脏脂肪含量的估计也依赖于操作人员。在此，我们描述了一种低成本的端到端人工智能平台的开发，用于实时评估供体肝脏活检切片上的肝脏脂肪含量。硬件包括高分辨率相机、显示器和GPU，用于获取和处理供体肝脏活检切片。训练了一个深度学习模型来标记和量化肝组织中的脂肪球。该算法部署在设备上，以便实时量化和表征脂肪含量，用于移植决策。此信息显示在设备上，也可以发送到云平台进行进一步分析。
