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Full-Text Articles in Medicine and Health Sciences
The Overestimation Of Concentric Hypertrophy In Patients With Hfpef As Determined By 2d-Echocardiography, Mohammad F. Mathbout, Hussam Al Hennawi, Anwar Khedr, Gaurang N. Vaidya, Marcus Stoddard
The Overestimation Of Concentric Hypertrophy In Patients With Hfpef As Determined By 2d-Echocardiography, Mohammad F. Mathbout, Hussam Al Hennawi, Anwar Khedr, Gaurang N. Vaidya, Marcus Stoddard
Division of Internal Medicine Faculty Papers & Presentations
Background: Heart failure with preserved ejection fraction continues to pose multiple challenges in terms of accurate diagnosis, treatment, and associated morbidity. Accurate left ventricular (LV) mass calculation yields essential prognostic information relating to structural heart disease. Two-dimensional (2D) echocardiography-based calculations are solely limited to LV geometric assumptions of symmetry, whereas three-dimensional (3D) echocardiography could overcome these limitations. This study aims to compare the performance of 2D and 3D LV mass calculations. Methods: A prospective review of echocardiography findings at the University of Louisville, Kentucky, was conducted and assessed. Normal ejection fraction (EF) was defined as >=52% in males and >=54% …
The Effect Of Scan And Patient Parameters On The Diagnostic Performance Of Ai For Detecting Coronary Stenosis On Coronary Ct Angiography, Rebecca Jonas, Emil Barkovich, Andrew D Choi, William F Griffin, Joanna Riess, Hugo Marques, Hyuk-Jae Chang, Jung Hyun Choi, Joon-Hyung Doh, Ae-Young Her, Bon-Kwon Koo, Chang-Wook Nam, Hyung-Bok Park, Sang-Hoon Shin, Jason Cole, Alessia Gimelli, Muhammad Akram Khan, Bin Lu, Yang Gao, Faisal Nabi, Ryo Nakazato, U Joseph Schoepf, Roel S Driessen, Michiel J Bom, Randall C Thompson, James J Jang, Michael Ridner, Chris Rowan, Erick Avelar, Philippe Généreux, Paul Knaapen, Guus A De Waard, Gianluca Pontone, Daniele Andreini, Marco Guglielmo, Mouaz H Al-Mallah, Robert S Jennings, Tami R Crabtree, James P Earls
The Effect Of Scan And Patient Parameters On The Diagnostic Performance Of Ai For Detecting Coronary Stenosis On Coronary Ct Angiography, Rebecca Jonas, Emil Barkovich, Andrew D Choi, William F Griffin, Joanna Riess, Hugo Marques, Hyuk-Jae Chang, Jung Hyun Choi, Joon-Hyung Doh, Ae-Young Her, Bon-Kwon Koo, Chang-Wook Nam, Hyung-Bok Park, Sang-Hoon Shin, Jason Cole, Alessia Gimelli, Muhammad Akram Khan, Bin Lu, Yang Gao, Faisal Nabi, Ryo Nakazato, U Joseph Schoepf, Roel S Driessen, Michiel J Bom, Randall C Thompson, James J Jang, Michael Ridner, Chris Rowan, Erick Avelar, Philippe Généreux, Paul Knaapen, Guus A De Waard, Gianluca Pontone, Daniele Andreini, Marco Guglielmo, Mouaz H Al-Mallah, Robert S Jennings, Tami R Crabtree, James P Earls
Division of Internal Medicine Faculty Papers & Presentations
Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis.
Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters.
Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± …