AIMC Topic: Adult

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Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images.

PLoS biology
Deep neural networks (DNNs) for object classification have been argued to provide the most promising model of the visual system, accompanied by claims that they have attained or even surpassed human-level performance. Here, we evaluated whether DNNs ...

An early model to predict the risk of gestational diabetes mellitus in the absence of blood examination indexes: application in primary health care centres.

BMC pregnancy and childbirth
BACKGROUND: Gestational diabetes mellitus (GDM) is one of the critical causes of adverse perinatal outcomes. A reliable estimate of GDM in early pregnancy would facilitate intervention plans for maternal and infant health care to prevent the risk of ...

Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning.

BMC medical informatics and decision making
BACKGROUND: Alzheimer's disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to...

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning.

BMC medical imaging
PURPOSE: The objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms.

Convolutional Neural Network With Graphical Lasso to Extract Sparse Topological Features for Brain Disease Classification.

IEEE/ACM transactions on computational biology and bioinformatics
The functional connectivity provides new insights into the mechanisms of the human brain at network-level, which has been proved to be an effective biomarker for brain disease classification. Recently, machine learning methods have played an importan...

Blood Pressure Model Based on Hybrid Feature Convolution Neural Network in Promoting Rehabilitation of Patients with Hypertensive Intracerebral Hemorrhage.

Computational and mathematical methods in medicine
OBJECTIVE: Accurate prediction of the rise of blood pressure is essential for the hypertensive intracerebral hemorrhage. This study uses the hybrid feature convolution neural network to establish the blood pressure model instead of the traditional me...

A radiogenomics application for prognostic profiling of endometrial cancer.

Communications biology
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecul...

Robotic Transvaginal Natural Orifice Transluminal Endoscopic Surgery for Resection of Parametrial and Bowel Deeply Infiltrated Endometriosis.

Journal of minimally invasive gynecology
STUDY OBJECTIVE: To demonstrate stepwise techniques for the successful utilization of the Robotic-assisted transvaginal Natural Orifice Transluminal Endoscopy Surgery (NOTES) technique for safely surgically managing deeply infiltrated endometriosis (...

Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: The prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histologic criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and seve...

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.

Genes
OBJECTIVES: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) ...