AIMC Topic: Magnetic Resonance Imaging
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Association of the characteristics of brain magnetic resonance imaging with genes related to disease onset in schizophrenia patients.
SLAS technology
Mar 28, 2025
BACKGROUND: Schizophrenia (SCH) is a complex neurodevelopmental disorder, whose pathogenesis is not fully elucidated. This article aims to reveal disease-specific brain structural and functional changes and their potential genetic basis by analyzing ...
Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.
BMC cancer
Mar 28, 2025
BACKGROUND: Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert...
Comparative Assessment of Manual Segmentation of Cerebral Infarction Lesions in Experimental Animals Based on Magnetic Resonance Imaging Using Artificial Intelligence.
Bulletin of experimental biology and medicine
Mar 28, 2025
The aim of this study was to evaluate the quality of manual segmentation of cerebral infarction lesions in experimental animals with modeled brain infarct based on magnetic resonance imaging compared to an automated artificial intelligence approach. ...
Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment.
BMC medical imaging
Mar 27, 2025
BACKGROUND: To evaluate the performance of a deep learning reconstruction (DLR) based on Adaptive-Compressed sensing (CS)-Network for brain MRI and validate it in a clinical setting.
Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings.
Diagnostic and interventional radiology (Ankara, Turkey)
Mar 26, 2025
PURPOSE: This study aimed to evaluate the effectiveness of artificial intelligence (AI) in diagnosing focal nodular hyperplasia (FNH) of the liver using magnetic resonance imaging (MRI) and compare its performance with that of radiologists.
Multidisciplinary Consensus Prostate Contours on Magnetic Resonance Imaging: Educational Atlas and Reference Standard for Artificial Intelligence Benchmarking.
International journal of radiation oncology, biology, physics
Mar 26, 2025
PURPOSE: Evaluation of artificial intelligence (AI) algorithms for prostate segmentation is challenging because ground truth is lacking. We aimed to: (1) create a reference standard data set with precise prostate contours by expert consensus, and (2)...
Using deep learning to enhance reporting efficiency and accuracy in degenerative cervical spine MRI.
The spine journal : official journal of the North American Spine Society
Mar 26, 2025
BACKGROUND CONTEXT: Cervical spine MRI is essential for evaluating degenerative cervical spondylosis (DCS) but is time-consuming to report and subject to interobserver variability. The integration of artificial intelligence in medical imaging offers ...
Toward a Refined PI-RADS: The Feasibility and Limitations of More Informative Metrics in Reviewing MRI Scans.
Journal of magnetic resonance imaging : JMRI
Mar 26, 2025
The Prostate Imaging-Reporting and Data System (PI-RADS) is a widely-adopted framework for assessing prostate cancer risk using multiparametric MRI. However, as advancements in imaging and data analytics emerge, PI-RADS faces pressure to integrate no...
Predicting hemorrhagic transformation in acute ischemic stroke: a systematic review, meta-analysis, and methodological quality assessment of CT/MRI-based deep learning and radiomics models.
Emergency radiology
Mar 26, 2025
Acute ischemic stroke (AIS) is a major cause of mortality and morbidity, with hemorrhagic transformation (HT) as a severe complication. Accurate prediction of HT is essential for optimizing treatment strategies. This review assesses the accuracy and ...
Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.
BMC medical imaging
Mar 26, 2025
BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focu...
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