AI Medical Compendium Topic:
Predictive Value of Tests

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Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Circulation. Arrhythmia and electrophysiology
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are...

Ischemia and outcome prediction by cardiac CT based machine learning.

The international journal of cardiovascular imaging
Cardiac CT using non-enhanced coronary artery calcium scoring (CACS) and coronary CT angiography (cCTA) has been proven to provide excellent evaluation of coronary artery disease (CAD) combining anatomical and morphological assessment of CAD for card...

Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints.

Toxicology letters
The human ether-a-go-go-related gene (hERG) encodes a tetrameric potassium channel called Kv11.1. This channel can be blocked by certain drugs, which leads to long QT syndrome, causing cardiotoxicity. This is a significant problem during drug develop...

Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.

Scientific reports
We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model. In a 17-center prospective study of infants (aged < 1 year) hospitali...

From CT to artificial intelligence for complex assessment of plaque-associated risk.

The international journal of cardiovascular imaging
The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust inform...

Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer.

Cancer research and treatment
PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and...

Machine learning-based prediction of transfusion.

Transfusion
BACKGROUND: The ability to predict transfusions arising during hospital admission might enable economized blood supply management and might furthermore increase patient safety by ensuring a sufficient stock of red blood cells (RBCs) for a specific pa...

Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma.

The British journal of ophthalmology
BACKGROUND/AIM: To train and validate the prediction performance of the deep learning (DL) model to predict visual field (VF) in central 10° from spectral domain optical coherence tomography (SD-OCT).

Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.

The journal of pathology. Clinical research
The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we p...

Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.

European journal of radiology
PURPOSE: Adenocarcinoma (ADC) is the most common histological subtype of lung cancers in non-small cell lung cancer (NSCLC) in which ground glass opacifications (GGOs) found on computed tomography (CT) scans are the most common lesions. However, the ...