摘要
一位新闻记者兼机器人与机器学习每日新闻编辑每日新闻-肝脏疾病和疾病的新研究-肝纤维化是一篇报道的主题。根据NewsRx记者从中国江苏发回的新闻报道，Rese Arch说，“日本血吸虫病是南亚重要的公共卫生问题，迫切需要优化现有的血吸虫病诊断技术。”新闻记者引用了第一人民医院的研究，“本研究旨在利用超声放射组学和机器扫描技术建立血吸虫感染肝纤维化不同阶段的模型，我们回顾性收集了江西省都昌市第二人民医院2018-2022年1531例患者和5671例B超图像。”将(LFSI)血吸虫感染肝纤维化分为0级、1级、2级和3级4个阶段。例如第1组(0级vs. 1级)和第2组(0级vs. 2级)。使用Pyradiomics、Mann-Whitney U检验和最小绝对收缩和选择算子(LASSO)提取关键的放射学特征。使用支持向量机(SVM)构建机器学习模型。应用Shapley additive Explanations(SHAP)描述了不同特征在模型中的分布。本研究最终包括1388例患者及其相应的图像。每个二值分类问题共提取了851个放射组学特征。各组保留18至76个特征。验证队列的受试者工作特征曲线(AUC)下面积：LFSI 0级与LFSI 1级分别为0.834（95%CI：0.77 9-0.885）、LFSI 1级与LFSI 2级分别为0.771（95%CI：0.713-0.835）、LFSI 2级与LFSI 3级分别为0.830（95%CI：0.762
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Liver Diseases and Con ditions - Liver Fibrosis is the subject of a report. According to news reporting originating in Jiangsu, People’s Republic of China, by NewsRx journalists, rese arch stated, “Schistosomiasis japonica represents a significant public health co ncern in South Asia. There is an urgent need to optimize existing schistosomiasi s diagnostic techniques.” The news reporters obtained a quote from the research from First People’s Hospit al, “This study aims to develop models for the different stages of liver fibrosi s caused by Schistosoma infection utilizing ultrasound radiomics and machine lea rning techniques. From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People’s Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on incl usion and exclusion criteria suitable for radiomics models. Liver fibrosis due t o Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification pr oblems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test , and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learn ing models were constructed using Support Vector Machine (SVM), and the contribu tion of different features in the model was described by applying Shapley Additi ve Explanations (SHAP). This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features w ere retained from each groups. The area under the receiver operating characteris tic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.77 9-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.7 13-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0. 762-0.885) for LFSI grade 2 vs. LFSI grade 3.”
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