首页|Norwegian University of Science and Technology (NTNU) Reports Findings in Artificial Intelligence (Automatic assessment of left ventricular function for hemodynamic monitoring using artificial intelligence and transesophageal echocardiography)
Norwegian University of Science and Technology (NTNU) Reports Findings in Artificial Intelligence (Automatic assessment of left ventricular function for hemodynamic monitoring using artificial intelligence and transesophageal echocardiography)
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New research on Artificial Intelligence is the subject of a report. According to news originating from Trondheim, Norway, by NewsRx correspondents, research stated, “We have developed a method to automatically assess LV function by measuring mitral annular plane systolic excursion (MAPSE) using artificial intelligence and transesophageal echocardiography (autoMAPSE). Our aim was to evaluate autoMAPSE as an automatic tool for rapid and quantitative assessment of LV function in critical care patients.” Our news journalists obtained a quote from the research from the Norwegian University of Science and Technology (NTNU), “In this retrospective study, we studied 40 critical care patients immediately after cardiac surgery. First, we recorded a set of echocardiographic data, consisting of three consecutive beats of midesophageal two- and four-chamber views. We then altered the patient's hemodynamics by positioning them in anti-Trendelenburg and repeated the recordings. We measured MAPSE manually and used autoMAPSE in all available heartbeats and in four LV walls. To assess the agreement with manual measurements, we used a modified Bland-Altman analysis. To assess the precision of each method, we calculated the least significant change (LSC). Finally, to assess trending ability, we calculated the concordance rates using a four-quadrant plot. We found that autoMAPSE measured MAPSE in almost every set of two- and four-chamber views (feasibility 95%). It took less than a second to measure and average MAPSE over three heartbeats. AutoMAPSE had a low bias (0.4 mm) and acceptable limits of agreement (- 3.7 to 4.5 mm). AutoMAPSE was more precise than manual measurements if it averaged more heartbeats. AutoMAPSE had acceptable trending ability (concordance rate 81%) during hemodynamic alterations.”
TrondheimNorwayEuropeArtificial IntelligenceCardiologyCardiovascularCritical Care MedicineDiagnosisDiagnostic Techniques and ProceduresDoppler EchocardiographyEchocardiographyEmerging TechnologiesHealth and MedicineImaging TechnologyMachine LearningTechnologyTransesophageal Echocardiography
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