Abstract：
Liver fibrosis is a pathological condition characterized by the abnormal proliferation of liver tissue, subsequently able to progress to cirrhosis or possibly hepatocellular carcinoma. The development of artificial intelligence and deep learning have begun to play a significant role in fibrosis detection. This study aimed to develop SMART AI-PATHO, a fully automated assessment method combining quantification of histopathological architectural features, to analyze steatosis and fibrosis in nonalcoholic fatty liver disease (NAFLD) core biopsies and employ Metavir fibrosis staging as standard references and fat assessment grading measurement for comparison with the pathologist interpretations. There were 146 participants enrolled in our study. The correlation of Metavir scoring system interpretation between pathologists and SMART AI-PATHO was significantly correlated (Agreement = 68%, Kappa = 0.59, p-value <0.001), which subgroup analysis of significant fibrosis (Metavir score F2-F4) and nonsignificant fibrosis (Metavir score F0-F1) demonstrated substantial correlated results (agreement = 80%, kappa = 0.61, p-value <0.001), corresponding with the correlation of advanced fibrosis (Metavir score F3-F4) and nonadvanced fibrosis groups (Metavir score F0-F2), (agreement = 89%, kappa = 0.74, p-value <0.001). SMART AI-PATHO, the first pivotal artificially intelligent diagnostic tool for the color-based NAFLD hepatic tissue staging in Thailand, demonstrated satisfactory performance as a pathologist to provide liver fibrosis scoring and steatosis grading. In the future, developing AI algorithms and reliable testing on a larger scale may increase accuracy and contribute to telemedicine consultations for general pathologists in clinical practice.
摘要：
肝纤维化是以肝组织异常增殖为特征的病理状态，随后能够进展为肝硬化或可能的肝细胞癌。人工智能和深度学习的发展已经开始在纤维化检测中发挥重要作用。本研究旨在开发智能AI-PATHO，一种完全自动化的评估方法，结合了组织病理学结构特征的量化，分析非酒精性脂肪性肝病(NAFLD)核心活检中的脂肪变性和纤维化,并采用Metavir纤维化分期作为标准参考和脂肪评估分级测量,以便与病理学家的解释进行比较.有146名参与者参加了我们的研究。病理学家与SMARTAI-PATHO之间Metavir评分系统解释的相关性显着相关（协议=68％，Kappa=0.59，p值<0.001），显着纤维化（Metavir评分F2-F4）和非显着纤维化（Metavir评分F0-F1）的亚组分析显示出实质性相关结果（一致性=80％，κ=0.61，p值<0.001)，与晚期纤维化（Metavir评分F3-F4）和非晚期纤维化组（Metavir评分F0-F2）的相关性相对应，(协议=89%，κ=0.74，p值<0.001）。SMARTAI-PATHO,泰国第一个基于颜色的NAFLD肝组织分期的关键人工智能诊断工具，表现令人满意的表现，作为病理学家提供肝纤维化评分和脂肪变性分级。在未来,开发AI算法和更大规模的可靠测试可能会提高准确性，并有助于临床实践中普通病理学家的远程医疗咨询。
