AIMC Topic: Magnetic Resonance Imaging

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Federated Learning via Conditional Mutual Learning for Alzheimer's Disease Classification on T1w MRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Data-driven deep learning has been considered a promising method for building powerful models for medical data, which often requires a large amount of diverse data to be sufficiently effective. However, the expensive cost of collecting and the privac...

Deep-learning-based MRI in the diagnosis of cerebral infarction and its correlation with the neutrophil to lymphocyte ratio.

Annals of palliative medicine
BACKGROUND: Dizziness is a common symptom in clinic, but there lacks an effective treatment method. This study sought to examine the efficiency of deep learning (DL)-based magnetic resonance imaging (MRI) in the diagnosis of cerebral infarction mainl...

Artificial intelligence and machine learning in aortic disease.

Current opinion in cardiology
PURPOSE OF REVIEW: Discuss foundational concepts for artificial intelligence (AI) and review recent literature on its application to aortic disease.

Emerging artificial intelligence applications in liver magnetic resonance imaging.

World journal of gastroenterology
Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare s...

Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study.

Schizophrenia bulletin
BACKGROUND: Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learnin...

Deep Learning-Assisted Multiphoton Microscopy to Reduce Light Exposure and Expedite Imaging in Tissues With High and Low Light Sensitivity.

Translational vision science & technology
PURPOSE: Two-photon excitation fluorescence (2PEF) reveals information about tissue function. Concerns for phototoxicity demand lower light exposure during imaging. Reducing excitation light reduces the quality of the image by limiting fluorescence e...

Machine learning evaluates changes in functional connectivity under a prolonged cognitive load.

Chaos (Woodbury, N.Y.)
One must be aware of the black-box problem by applying machine learning models to analyze high-dimensional neuroimaging data. It is due to a lack of understanding of the internal algorithms or the input features upon which most models make decisions ...

Can 3D artificial intelligence models outshine 2D ones in the detection of intracranial metastatic tumors on magnetic resonance images?

Journal of the Chinese Medical Association : JCMA
BACKGROUND: This study aimed to compare the prediction performance of two-dimensional (2D) and three-dimensional (3D) semantic segmentation models for intracranial metastatic tumors with a volume ≥ 0.3 mL.

Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation.

Radiographics : a review publication of the Radiological Society of North America, Inc
The classic prostate cancer (PCa) diagnostic pathway that is based on prostate-specific antigen (PSA) levels and the findings of digital rectal examination followed by systematic biopsy has shown multiple limitations. The use of multiparametric MRI (...

Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles.

Investigative radiology
OBJECTIVE: Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations ...