首页|First People’s Hospital Reports Findings in Liver Fibrosis (Feasibility of ultra sound radiomics based models for classification of liver fibrosis due to Schisto soma japonicum infection)
First People’s Hospital Reports Findings in Liver Fibrosis (Feasibility of ultra sound radiomics based models for classification of liver fibrosis due to Schisto soma japonicum infection)
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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.”
JiangsuPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesGastroenterologyHealth and MedicineLiver CirrhosisLiver Diseases and ConditionsLiver FibrosisMachine Learning