The Faculty of Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel.
Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.
Toxicol Pathol. 2020 Jul;48(5):702-707. doi: 10.1177/0192623320926478. Epub 2020 Jun 8.
Quantification of fatty vacuoles in the liver, with differentiation from lumina of liver blood vessels and bile ducts, is an example where the traditional semiquantitative pathology assessment can be enhanced with artificial intelligence (AI) algorithms. Using glass slides of mice liver as a model for nonalcoholic fatty liver disease, a deep learning AI algorithm was developed. This algorithm uses a segmentation framework for vacuole quantification and can be deployed to analyze live histopathology fields during the microscope-based pathology assessment. We compared the manual semiquantitative microscope-based assessment with the quantitative output of the deep learning algorithm. The deep learning algorithm was able to recognize and quantify the percent of fatty vacuoles, exhibiting a strong and significant correlation ( = 0.87, < .001) between the semiquantitative and quantitative assessment methods. The use of deep learning algorithms for difficult quantifications within the microscope-based pathology assessment can help improve outputs of toxicologic pathology workflows.
肝脏脂肪空泡的定量分析，包括与肝血管和胆管管腔的区分，是人工智能 (AI) 算法可以增强传统半定量病理学评估的一个例子。使用非酒精性脂肪性肝病小鼠肝脏的载玻片作为模型，开发了一种深度学习 AI 算法。该算法使用了一种用于空泡定量的分割框架，并且可以部署到基于显微镜的病理学评估过程中分析实时组织病理学领域。我们将手动基于显微镜的半定量评估与深度学习算法的定量输出进行了比较。深度学习算法能够识别和量化脂肪空泡的百分比，在半定量和定量评估方法之间表现出很强的显著相关性 ( = 0.87， <.001)。在基于显微镜的病理学评估中，深度学习算法可用于对困难的定量分析，这有助于提高毒理学病理学工作流程的输出。
