AIMC Topic:
Magnetic Resonance Imaging
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Diagnostic Utility of Artificial Intelligence-assisted Transperineal Biopsy Planning in Prostate Cancer Suspected Men: A Prospective Cohort Study.
European urology focus
Apr 29, 2024
BACKGROUND AND OBJECTIVE: Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improv...
Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.
Interdisciplinary sciences, computational life sciences
Apr 29, 2024
Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting th...
Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.
Journal of neurotrauma
Apr 29, 2024
Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinatio...
Motion correction and super-resolution for multi-slice cardiac magnetic resonance imaging via an end-to-end deep learning approach.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 29, 2024
Accurate reconstruction of a high-resolution 3D volume of the heart is critical for comprehensive cardiac assessments. However, cardiac magnetic resonance (CMR) data is usually acquired as a stack of 2D short-axis (SAX) slices, which suffers from the...
Detection of Marchiafava Bignami disease using distinct deep learning techniques in medical diagnostics.
BMC medical imaging
Apr 29, 2024
PURPOSE: To detect the Marchiafava Bignami Disease (MBD) using a distinct deep learning technique.
Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.
Journal of cancer research and clinical oncology
Apr 29, 2024
PURPOSE: The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-yea...
LST-AI: A deep learning ensemble for accurate MS lesion segmentation.
NeuroImage. Clinical
Apr 29, 2024
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segment...
Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks.
Parkinsonism & related disorders
Apr 28, 2024
BACKGROUND: Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain...
Deep Learning Model for Grading and Localization of Lumbar Disc Herniation on Magnetic Resonance Imaging.
Journal of magnetic resonance imaging : JMRI
Apr 27, 2024
BACKGROUND: Methods for grading and localization of lumbar disc herniation (LDH) on MRI are complex, time-consuming, and subjective. Utilizing deep learning (DL) models as assistance would mitigate such complexities.
Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Apr 27, 2024
Despite its superior soft tissue contrast and non-invasive nature, MRI requires long scan times due to its intrinsic signal acquisition principles, a main drawback which technological advancements in MRI have been focused on. In particular, scan time...
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