AIMC Topic: Deep Learning

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Deep learning algorithm for predicting preterm birth in the case of threatened preterm labor admissions using transvaginal ultrasound.

Journal of medical ultrasonics (2001)
PURPOSE: Preterm birth presents a major challenge in perinatal care, and predicting preterm birth remains a major challenge. If preterm birth cases can be accurately predicted during pregnancy, preventive interventions and more intensive prenatal mon...

Protocol for assessing neighborhood physical disorder using the YOLOv8 deep learning model.

STAR protocols
Neighborhood physical disorder (PD), characterized by disruptions and irregularities in spatial elements, is associated with negative economic, social, and public health outcomes. Here, we present a protocol to quantitatively assess PD utilizing a ra...

Hessian Regularized -Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.

Interdisciplinary sciences, computational life sciences
Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate...

Artificial intelligence in glaucoma detection using color fundus photographs.

Indian journal of ophthalmology
PURPOSE: To explore the potential of artificial intelligence (AI) for glaucoma detection using deep learning algorithm and evaluate its accuracy for image classification of glaucomatous optic neuropathy (GON) from color fundus photographs.

Regulated Behavior in Living Cells with Highly Aligned Configurations on Nanowrinkled Graphene Oxide Substrates: Deep Learning Based on Interplay of Cellular Contact Guidance.

ACS nano
Micro-/nanotopographical cues have emerged as a practical and promising strategy for controlling cell fate and reprogramming, which play a key role as biophysical regulators in diverse cellular processes and behaviors. Extracellular biophysical facto...

A deep-learning approach for identifying prospective chemical hazards.

Toxicology
With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identifi...

Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

NeuroImage
Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the under...

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data.

Journal of visualized experiments : JoVE
Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the analysis of multi-omics datasets, including proteomics, metabolomics, and clinical data. This workf...

SEResUTer: a deep learning approach for accurate ECG signal delineation and atrial fibrillation detection.

Physiological measurement
Accurate detection of electrocardiogram (ECG) waveforms is crucial for computer-aided diagnosis of cardiac abnormalities. This study introduces SEResUTer, an enhanced deep learning model designed for ECG delineation and atrial fibrillation (AF) detec...

An overview of machine learning and deep learning techniques for predicting epileptic seizures.

Journal of integrative bioinformatics
Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has ...