AIMC Topic: Retrospective Studies

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An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy.

Echocardiography (Mount Kisco, N.Y.)
AIM: To test the feasibility and accuracy of a new attention-based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference.

Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure.

Circulation. Heart failure
BACKGROUND: Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied.

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network.

Medicina (Kaunas, Lithuania)
BACKGROUND AND OBJECTIVES: This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images.

Leveraging machine learning to distinguish between bacterial and viral induced pharyngitis using hematological markers: a retrospective cohort study.

Scientific reports
Accurate differentiation between bacterial and viral-induced pharyngitis is recognized as essential for personalized treatment and judicious antibiotic use. From a cohort of 693 patients with pharyngitis, data from 197 individuals clearly diagnosed w...

External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study.

Critical care medicine
OBJECTIVES: The limitations of current early warning scores have prompted the development of deep learning-based systems, such as deep learning-based cardiac arrest risk management systems (DeepCARS). Unfortunately, in South Korea, only two instituti...

Non-invasive prediction of the chronic degree of lupus nephropathy based on ultrasound radiomics.

Lupus
OBJECTIVE: Through machine learning (ML) analysis of the radiomics features of ultrasound extracted from patients with lupus nephritis (LN), this attempt was made to non-invasively predict the chronicity index (CI)of LN.

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

European radiology
OBJECTIVES: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.

Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system.

Anesthesia management experience for pediatric day-case PDA ligation under thoracoscopy assisted by a robot: a retrospective study.

Journal of cardiothoracic surgery
BACKGROUND: To summarize the anesthesia management experience for pediatric day-case patent ductus arteriosus (PDA) ligation under robot-assisted thoracoscopy and explore the key points of anesthesia management for this procedure.