AIMC Topic: Machine Learning

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Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery.

Seminars in arthritis and rheumatism
The emergence of powerful machine learning methodology together with an increasing amount of data collected during clinical routine have fostered a growing role of artificial intelligence (AI) in medicine. Algorithms have become part of clinical care...

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes.

Proceedings of the National Academy of Sciences of the United States of America
Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and functio...

Discovery of potential RIPK1 inhibitors by machine learning and molecular dynamics simulations.

Physical chemistry chemical physics : PCCP
Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) plays a crucial role in inflammation and cell death, so it is a promising candidate for the treatment of autoimmune, inflammatory, neurodegenerative, and ischemic diseases. So far, there ...

Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review.

Minerva surgery
INTRODUCTION: Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortalit...

Machine learning-based nonlinear regression-adjusted real-time quality control modeling: a multi-center study.

Clinical chemistry and laboratory medicine
OBJECTIVES: Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring the performance of the testing process, has gained increasing attention in recent years. It has been questioned for its generalizability among analytes, in...

A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran.

Environmental science and pollution research international
Detecting and mapping landslides are crucial for effective risk management and planning. With the great progress achieved in applying optimized and hybrid methods, it is necessary to use them to increase the accuracy of landslide susceptibility maps....

Speech emotion analysis using convolutional neural network (CNN) and gamma classifier-based error correcting output codes (ECOC).

Scientific reports
Speech emotion analysis is one of the most basic requirements for the evolution of Artificial Intelligence (AI) in the field of human-machine interaction. Accurate emotion recognition in speech can be effective in applications such as online support,...

Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review.

Environmental monitoring and assessment
Environmental contamination especially air pollution is an exponentially growing menace requiring immediate attention, as it lingers on with the associated risks of health, economic and ecological crisis. The special focus of this study is on the adv...

Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography.

European radiology
OBJECTIVES: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)-based machine le...

Machine learning and single-cell transcriptome profiling reveal regulation of fibroblast activation through THBS2/TGFβ1/P-Smad2/3 signalling pathway in hypertrophic scar.

International wound journal
Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA-seq data from samples of normal sk...