AIMC Topic: Magnetic Resonance Imaging

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Where, why, and how is bias learned in medical image analysis models? A study of bias encoding within convolutional networks using synthetic data.

EBioMedicine
BACKGROUND: Understanding the mechanisms of algorithmic bias is highly challenging due to the complexity and uncertainty of how various unknown sources of bias impact deep learning models trained with medical images. This study aims to bridge this kn...

ResNet-Vision Transformer based MRI-endoscopy fusion model for predicting treatment response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: A multicenter study.

Chinese medical journal
BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer ba...

The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.

European radiology
AIM: Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA imaging...

Enhancing thin slice 3D T2-weighted prostate MRI with super-resolution deep learning reconstruction: Impact on image quality and PI-RADS assessment.

Magnetic resonance imaging
PURPOSES: This study aimed to assess the effectiveness of Super-Resolution Deep Learning Reconstruction (SR-DLR) -a deep learning-based technique that enhances image resolution and quality during MRI reconstruction- in improving the image quality of ...

Automated Neuroprognostication Via Machine Learning in Neonates with Hypoxic-Ischemic Encephalopathy.

Annals of neurology
OBJECTIVES: Neonatal hypoxic-ischemic encephalopathy is a serious neurologic condition associated with death or neurodevelopmental impairments. Magnetic resonance imaging (MRI) is routinely used for neuroprognostication, but there is substantial subj...

A neural network to create super-resolution MR from multiple 2D brain scans of pediatric patients.

Medical physics
BACKGROUND: High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatment in childhood cancer survivors, such as morphological changes in brain structures. However, these...

MRI classification of progressive supranuclear palsy, Parkinson disease and controls using deep learning and machine learning algorithms for the identification of regions and tracts of interest as potential biomarkers.

Computers in biology and medicine
BACKGROUND: Quantitative magnetic resonance imaging (MRI) analysis has shown promise in differentiating neurodegenerative Parkinsonian syndromes and has significantly advanced our understanding of diseases like progressive supranuclear palsy (PSP) in...

Advancing brain tumor classification: A robust framework using EfficientNetV2 transfer learning and statistical analysis.

Computers in biology and medicine
Brain tumors are a significant health risk threatening humanity, and they seem to be unique challenges due to their critical location and the complexity of accurate diagnosis and treatment planning. Accurate and timely diagnosis and appropriate treat...

Predicting drug craving among ketamine-dependent users through machine learning based on brain structural measures.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Craving is a core factor driving drug-seeking and -taking, representing a significant risk factor for relapse. This study aims to identify neuroanatomical biomarkers for quantifying and predicting craving.