AIMC Topic: Brain Diseases
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Deep learning algorithms for brain disease detection with magnetic induction tomography.
Medical physics
Dec 18, 2020
PURPOSE: In order to improve the reconstruction accuracy of magnetic induction tomography (MIT) and achieve fast imaging especially in the detection of cerebral hemorrhage, artificial intelligence algorithms are proposed to improve the accuracy of MI...
Quantitative analysis of brain herniation from non-contrast CT images using deep learning.
Journal of neuroscience methods
Dec 13, 2020
BACKGROUND: Brain herniation is one of the fatal outcomes of increased intracranial pressure (ICP). It is caused due to the presence of hematoma or tumor mass in the brain. Ideal midline (iML) divides the healthy brain into two (right and left) nearl...
Gut microbiome-mediated epigenetic regulation of brain disorder and application of machine learning for multi-omics data analysis.
Genome
Oct 8, 2020
The gut-brain axis (GBA) is a biochemical link that connects the central nervous system (CNS) and enteric nervous system (ENS). Clinical and experimental evidence suggests gut microbiota as a key regulator of the GBA. Microbes living in the gut not o...
Diverse Applications of Artificial Intelligence in Neuroradiology.
Neuroimaging clinics of North America
Sep 17, 2020
Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of A...
Reliability and accuracy of EEG interpretation for estimating age in preterm infants.
Annals of clinical and translational neurology
Aug 7, 2020
OBJECTIVES: To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants.
Investigating the challenges and generalizability of deep learning brain conductivity mapping.
Physics in medicine and biology
Jun 26, 2020
To investigate deep learning electrical properties tomography (EPT) for application on different simulated and in-vivo datasets, including pathologies for brain conductivity reconstructions, 3D patch-based convolutional neural networks were trained t...
Machine-learning-based diagnostics of EEG pathology.
NeuroImage
Jun 10, 2020
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding ...
Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks.
International journal of computer assisted radiology and surgery
Jun 2, 2020
PURPOSE: Fetal brain abnormalities are some of the most common congenital malformations that may associated with syndromic and chromosomal malformations, and could lead to neurodevelopmental delay and mental retardation. Early prenatal detection of b...
Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI.
Radiology
Apr 7, 2020
Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for ...
Brain pathology identification using computer aided diagnostic tool: A systematic review.
Computer methods and programs in biomedicine
Nov 13, 2019
Computer aided diagnostic (CAD) has become a significant tool in expanding patient quality-of-life by reducing human errors in diagnosis. CAD can expedite decision-making on complex clinical data automatically. Since brain diseases can be fatal, rapi...
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