首页|Department of Radiology Reports Findings in Artificial Intelligence (Automatic d etection of cognitive impairment in patients with white matter hyperintensity an d causal analysis of related factors using artificial intelligence of MRI)
Department of Radiology Reports Findings in Artificial Intelligence (Automatic d etection of cognitive impairment in patients with white matter hyperintensity an d causal analysis of related factors using artificial intelligence of MRI)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Chongqing, Peop le's Republic of China, by NewsRx editors, research stated, "White matter hyperi ntensity (WMH) is a common feature of brain aging, often linked with cognitive d ecline and dementia. This study aimed to employ deep learning and radiomics to d evelop models for detecting cognitive impairment in WMH patients and to analyze the causal relationships among cognitive impairment and related factors." Our news journalists obtained a quote from the research from the Department of R adiology, "A total of 79 WMH patients from hospital 1 were randomly divided into a training set (62 patients) and a testing set (17 patients). Additionally, 29 patients from hospital 2 were included as an independent testing set. All partic ipants underwent formal neuropsychological assessments to determine cognitive st atus. Automated identification and segmentation of WMH were conducted using VB-n et, with extraction of radiomics features from cortex, white matter, and nuclei. Four machine learning classifiers were trained on the training set and validate d on the testing set to detect cognitive impairment. Model performances were eva luated and compared. Causal analyses were conducted among cortex, white matter, nuclei alterations, and cognitive impairment. Among the models, the logistic reg ression (LR) model based on white matter features demonstrated the highest perfo rmance, achieving an AUC of 0.819 in the external test dataset. Causal analyses indicated that age, education level, alterations in cortex, white matter, and nu clei were causal factors of cognitive impairment. The LR model based on white ma tter features exhibited high accuracy in detecting cognitive impairment in WMH p atients."
ChongqingPeople's Republic of ChinaA siaArtificial IntelligenceEmerging TechnologiesHospitalsMachine Learning
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