AIMC Topic: Machine Learning

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Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture.

Cell reports. Medicine
Federated learning (FL) is a distributed machine learning framework that is gaining traction in view of increasing health data privacy protection needs. By conducting a systematic review of FL applications in healthcare, we identify relevant articles...

Combining simulation models and machine learning in healthcare management: strategies and applications.

Progress in biomedical engineering (Bristol, England)
Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combinati...

Predicting DNA structure using a deep learning method.

Nature communications
Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA structure, also described as DNA shape, plays a key role in these mechanisms. In this study, we present a deep learning-based meth...

An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes.

Scientific reports
Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their clinically-estimated risk of rupture-an event that is the 13th leading cause of death in the US-exceeds the risk associated with repair. Yet the current clini...

Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review.

Caries research
INTRODUCTION: A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to those of certified dentists. This methodological systematic review aimed to evaluate the different ...

Machine learning-based prediction of hip joint moment in healthy subjects, patients and post-operative subjects.

Computer methods in biomechanics and biomedical engineering
The application of machine learning in the field of motion capture research is growing rapidly. The purpose of the study is to implement a long-short term memory (LSTM) model able to predict sagittal plane hip joint moment (HJM) across three distinct...

Automated detection of fatal cerebral haemorrhage in postmortem CT data.

International journal of legal medicine
During the last years, the detection of different causes of death based on postmortem imaging findings became more and more relevant. Especially postmortem computed tomography (PMCT) as a non-invasive, relatively cheap, and fast technique is progress...

Artificial intelligence-based prediction of the rheological properties of hydrocolloids for plant-based meat analogues.

Journal of the science of food and agriculture
BACKGROUND: Methylcellulose has been applied as a primary binding agent to control the quality attributes of plant-based meat analogues. H owever, a great deal of effort has been made to search for hydrocolloids to replace methylcellulose because of ...

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches.

Journal of applied toxicology : JAT
The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evalu...

Spatial relation categorization in infants and deep neural networks.

Cognition
Spatial relations, such as above, below, between, and containment, are important mediators in children's understanding of the world (Piaget, 1954). The development of these relational categories in infancy has been extensively studied (Quinn, 2003) y...