AIMC Topic: Magnetic Resonance Imaging

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Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study.

International journal of environmental research and public health
Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial int...

Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke.

Topics in stroke rehabilitation
In recent years, robotic training has been utilized for recovery of motor control in patients with motor deficits. Along with clinical assessment, electrical patterns in the brain have emerged as a marker for studying changes in the brain associated...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model.

Journal of visualized experiments : JoVE
Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy exceeding its constituents. This protocol describes the development of an ML algorithm to predic...

MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas.

Computers in biology and medicine
Twelve to 66% of patients with clinically non-functioning pituitary adenoma (NFPA) experience tumor recurrence 1-5 years after the first surgery. Nevertheless, there is still no recurrence prediction factor concisely established and reproduced in the...

Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic res...

Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor d...

Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation.

Human brain mapping
Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique t...

Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions.

AJR. American journal of roentgenology
The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. ML currently has a very low barrier to entry into gene...

Passive, yet not inactive: robotic exoskeleton walking increases cortical activation dependent on task.

Journal of neuroengineering and rehabilitation
BACKGROUND: Experimental designs using surrogate gait-like movements, such as in functional magnetic resonance imaging (MRI), cannot fully capture the cortical activation associated with overground gait. Overground gait in a robotic exoskeleton may b...