AIMC Topic: Magnetic Resonance Imaging

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[Evaluation of brain age changes in patients with liver cirrhosis and hepatic encephalopathy with deep learning models based on structural magnetic resonance imaging].

Zhonghua yi xue za zhi
To investigate the brain aging in patients with cirrhosis and hepatic encephalopathy(HE), constructed a prediction model of brain age based on deep learning and T high-resolution MRI, and try to reveal the specific regions where cirrhosis and HE acc...

Development of the lyrics-based deep learning algorithm for identifying alcohol-related words (LYDIA).

Alcohol and alcoholism (Oxford, Oxfordshire)
BACKGROUND: Music is an integral part of our lives and is often played in public places like restaurants. People exposed to music that contained alcohol-related lyrics in a bar scenario consumed significantly more alcohol than those exposed to music ...

A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images.

Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement.

Journal of the American College of Cardiology
Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical...

Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Machine learning (ML) algorithms play a crucial role in the early and accurate diagnosis of Alzheimer's Disease (AD), which is essential for effective treatment planning. However, existing methods are not well-suited for identifying Mild Cognitive Im...

[A CASE OF TESTICULAR TUMOR UNDER CONSIDERATION FOR PARTIAL ORCHIECTOMY THROUGH RAPID INTRAOPERATIVE DIAGNOSIS].

Nihon Hinyokika Gakkai zasshi. The japanese journal of urology
A 35-year-old man visited a local doctor for continuing analysis of his infertility. Semen analysis revealed azoospermia while an ultrasonography detected a right testicular tumor with a diameter of 10 mm. A blood test was negative for tumor markers....

Prediction of T Stage of Rectal Cancer After Neoadjuvant Therapy by Multi-Parameter Magnetic Resonance Radiomics Based on Machine Learning Algorithms.

Technology in cancer research & treatment
INTRODUCTION: Since the response of patients with rectal cancer (RC) to neoadjuvant therapy is highly variable, there is an urgent need to develop accurate methods to predict the post-treatment T (pT) stage. The purpose of this study was to evaluate ...

Towards Explainable Detection of Alzheimer's Disease: A Fusion of Deep Convolutional Neural Network and Enhanced Weighted Fuzzy C-Mean.

Current medical imaging
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, posing a significant challenge for individuals and society. Early detection and treatment are essential for effective disease managem...

Untrained Network for Super-resolution for Non-contrast-enhanced Wholeheart MRI Acquired using Cardiac-triggered REACT (SRNN-REACT).

Current medical imaging
BACKGROUND: Three-dimensional (3D) whole-heart magnetic resonance imaging (MRI) is an excellent tool to check the heart anatomy of patients with congenital and acquired heart disease. However, most 3D whole-heart MRI acquisitions take a long time to ...

Classification of Artifacts in Multimodal Fused Images using Transfer Learning with Convolutional Neural Networks.

Current medical imaging
INTRODUCTION: Multimodal medical image fusion techniques play an important role in clinical diagnosis and treatment planning. The process of combining multimodal images involves several challenges depending on the type of modality, transformation tec...