首页|Capital Medical University Reports Findings in Artificial Intelligence (Identify ing IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: M anual recognition followed by artificial intelligence recognition)
Capital Medical University Reports Findings in Artificial Intelligence (Identify ing IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: M anual recognition followed by artificial intelligence recognition)
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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 originating from Beijing, People 's Republic of China, by NewsRx correspondents, research stated, "The T2-FLAIR m ismatch sign (T2FM) has nearly 100% specificity for predicting IDH -mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2% -56.0% of astrocytomas demonstrate a positive T2FM." Funders for this research include National Natural Science Foundation of China, National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation. Our news journalists obtained a quote from the research from Capital Medical Uni versity, "Methods must be considered for distinguishing astrocytomas from negati ve T2FM gliomas. In this study, positive T2FM gliomas were manually distinguishe d from nonenhancing gliomas, and then a support vector machine (SVM) classificat ion model was used to distinguish astrocytomas from negative T2FM gliomas. Nonen hancing gliomas (regardless of pathological type or grade) diagnosed between Jan uary 2022 and October 2022 ( = 300) and November 2022 and March 2023 ( = 196) wi ll comprise the training and validation sets, respectively. Our method for disti nguishing astrocytomas from nonenhancing gliomas was examined and validated usin g the training set and validation set. The specificity of T2FM for predicting as trocytomas was 100% in both the training and validation sets, whil e the sensitivity was 42.75% and 67.22%, respectively . Using a classification model of SVM based on radiomics features, among negativ e T2FM gliomas, the accuracy was above 85% when the prediction sco re was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas."
BeijingPeople's Republic of ChinaAsi aArtificial IntelligenceAstrocytomasCancerEmerging TechnologiesGliomasHealth and MedicineMachine LearningOncologySupport Vector Machines
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