首页|Data on Machine Learning Reported by Zhubo Jiang and Colleagues (An Automated Ma chine Learning-Based Quantitative Multiparametric Approach for Mitral Regurgitat ion Severity Grading)
Data on Machine Learning Reported by Zhubo Jiang and Colleagues (An Automated Ma chine Learning-Based Quantitative Multiparametric Approach for Mitral Regurgitat ion Severity Grading)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to news reportingfrom Singapore, Singapore, by NewsRx journalists, research stated, “Considering the high prevalence ofmitral regurgitation (MR) and the highly subjective, variable MR severity reporting, a n automated tool thatcould screen patients for clinically significant MR ( mode rate) would streamline the diagnostic/therapeuticpathways and ultimately improv e patient outcomes. The authors aimed to develop and validate a fullyautomated machine learning (ML)-based echocardiography workflow for grading MR severity.”
SingaporeSingaporeAsiaCardiologyCardiovascularCyborgsDiagnosisDiagnostic Techniques and ProceduresDopple r EchocardiographyEchocardiographyEmerging TechnologiesHealth and MedicineMachine Learning
NETL
NSTL
