AIMC Topic: Deep Learning

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Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances.

Talanta
Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depe...

Prospective Comparison of Free-Breathing Accelerated Cine Deep Learning Reconstruction Versus Standard Breath-Hold Cardiac MRI Sequences in Patients With Ischemic Heart Disease.

AJR. American journal of roentgenology
Cine cardiac MRI sequences require repeated breath-holds, which can be difficult for patients with ischemic heart disease (IHD). The purpose of the study was to compare a free-breathing accelerated cine sequence using deep learning (DL) reconstruct...

Revolutionizing protein-protein interaction prediction with deep learning.

Current opinion in structural biology
Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning...

MCPNET: Development of an interpretable deep learning model based on multiple conformations of the compound for predicting developmental toxicity.

Computers in biology and medicine
The development of deep learning models for predicting toxicological endpoints has shown great promise, but one of the challenges in the field is the accuracy and interpretability of these models. The bioactive conformation of a compound plays a crit...

Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques.

Sensors (Basel, Switzerland)
Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthc...

Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data.

BMC genomics
BACKGROUND: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore r...

Applied deep learning in neurosurgery: identifying cerebrospinal fluid (CSF) shunt systems in hydrocephalus patients.

Acta neurochirurgica
BACKGROUND: Over the recent decades, the number of different manufacturers and models of cerebrospinal fluid shunt valves constantly increased. Proper identification of shunt valves on X-ray images is crucial to neurosurgeons and radiologists to deri...

Empowering PET: harnessing deep learning for improved clinical insight.

European radiology experimental
This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough explor...

Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing.

JACC. Clinical electrophysiology
BACKGROUND: Continuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer ...

Correspondence-based Generative Bayesian Deep Learning for semi-supervised volumetric medical image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Automated medical image segmentation plays a crucial role in diverse clinical applications. The high annotation costs of fully-supervised medical segmentation methods have spurred a growing interest in semi-supervised methods. Existing semi-supervise...