Adopting artificial intelligence in cardiovascular medicine: A scoping review

H Makimoto, T Kohro - Hypertension Research, 2024 - nature.com
Recent years have witnessed significant transformations in cardiovascular medicine, driven
by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to …

Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached?

G Raileanu, JSSG de Jong - Current Problems in Cardiology, 2024 - Elsevier
Artificial intelligence (AI) is already widely used in different fields of medicine, making
possible the integration of the paraclinical exams with the clinical findings in patients, for a …

[HTML][HTML] Deep learning in computed tomography pulmonary angiography imaging: A dual-pronged approach for pulmonary embolism detection

F Bushra, MEH Chowdhury, R Sarmun, S Kabir… - Expert Systems with …, 2024 - Elsevier
The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for
Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved …

Multimodal fusion models for pulmonary embolism mortality prediction

N Cahan, E Klang, EM Marom, S Soffer, Y Barash… - Scientific Reports, 2023 - nature.com
Pulmonary embolism (PE) is a common, life threatening cardiovascular emergency. Risk
stratification is one of the core principles of acute PE management and determines the …

[HTML][HTML] Screening for RV Dysfunction Using Smartphone ECG Analysis App: Validation Study with Acute Pulmonary Embolism Patients

YJ Choi, MJ Park, Y Cho, J Kim, E Lee, D Son… - Journal of Clinical …, 2024 - mdpi.com
Background: Acute pulmonary embolism (PE) is a critical condition where the timely and
accurate assessment of right ventricular (RV) dysfunction is important for patient …

From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management

P Chrysafi, B Lam, S Carton, R Patell - Hämostaseologie, 2024 - thieme-connect.com
The high incidence of venous thromboembolism (VTE) globally and the morbidity and
mortality burden associated with the disease make it a pressing issue. Machine learning …

Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level

SV Kalmady, A Salimi, W Sun, N Sepehrvand… - NPJ Digital …, 2024 - nature.com
Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence
for the early detection of cardiovascular (CV) conditions, including those not traditionally …

Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review

B Ose, Z Sattar, A Gupta, C Toquica, C Harvey… - Current Cardiology …, 2024 - Springer
Many AI models in the domain of cardiac monitors and smart watches have received Food
and Drug Administration (FDA) clearance for rhythm classification, while others for …

Machine learning-based predictive models for patients with venous thromboembolism: A Systematic Review

V Danilatou, D Dimopoulos… - Thrombosis and …, 2024 - thieme-connect.com
Background: Venous thromboembolism (VTE) is a chronic disorder with a significant health
and economic burden. Several VTE-specific Clinical Prediction Models (CPMs) have been …

[HTML][HTML] Electrocardiogram Signal Analysis With a Machine Learning Model Predicts the Presence of Pulmonary Embolism With Accuracy Dependent on Embolism …

WE Wysokinski, RA Meverden, F Lopez-Jimenez… - Mayo Clinic …, 2024 - Elsevier
Objective To develop an artificial intelligence deep neural network (AI-DNN) algorithm to
analyze 12-lead electrocardiogram (ECG) for detection of acute PE and PE categories …