Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Department of Dermatology, 58884Hadassah Medical Center, Jerusalem, Israel.
Toxicol Pathol. 2021 Jul;49(5):1126-1133. doi: 10.1177/01926233211003866. Epub 2021 Mar 26.
In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate accurate evaluation of liver fibrosis in these models. In the present study, we compared the quantitative evaluation of collagen proportionate area in the carbon tetrachloride model of liver fibrosis in the mouse by a newly developed AI algorithm to the semiquantitative assessment of liver fibrosis performed by a board-certified toxicologic pathologist. We found an excellent correlation between the 2 methods of assessment, most evident in the higher magnification (×40) as compared to the lower magnification (×10). These findings strengthen the confidence of using digital tools in the toxicologic pathology field as an adjunct to an expert toxicologic pathologist.
在涉及肝纤维化动物模型的临床前研究中，准确量化纤维化程度至关重要。基于深度学习人工智能 (AI) 算法的数字图像分析可有助于在这些模型中准确评估肝纤维化。在本研究中，我们比较了新开发的 AI 算法对四氯化碳诱导的肝纤维化小鼠模型中胶原比例面积的定量评估与经认证的毒理病理学家进行的半定量肝纤维化评估，发现这两种评估方法具有极好的相关性，在高倍镜（×40）下比低倍镜（×10）下更为明显。这些发现增强了在毒理学病理学领域使用数字工具作为毒理病理学家辅助手段的信心。
