AIMC Topic: Magnetic Resonance Imaging

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A hybrid filtering and deep learning approach for early Alzheimer's disease identification.

Scientific reports
Alzheimer's disease is a progressive neurological disorder that profoundly affects cognitive functions and daily activities. Rapid and precise identification is essential for effective intervention and improved patient outcomes. This research introdu...

Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging.

Scientific reports
Assessing MGMT promoter methylation is crucial for determining appropriate glioblastoma therapy. Previous studies have focused on intratumoral regions, overlooking the peritumoral area. This study aimed to develop a radiomic model using MRI-derived f...

Exploring Neural Idiosyncrasies in Response to Autonomous Sensory Meridian Response Videos: Naturalistic Functional Magnetic Resonance Imaging Study of Stress and Sensory Processing.

Journal of medical Internet research
BACKGROUND: Autonomous sensory meridian response (ASMR) videos have been increasingly popularized as accessible tools for stress relief. Despite widespread media coverage promoting their benefits, empirical research on the neural mechanisms underlyin...

Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics.

BMC cancer
PURPOSE: To predict the 1p/19q molecular status of Lower-grade glioma (LGG) patients nondestructively, this study developed a deep learning (DL) approach using radiomic to provide a potential decision aid for clinical determination of molecular strat...

A new low-rank adaptation method for brain structure and metastasis segmentation via decoupled principal weight direction and magnitude.

Scientific reports
Deep learning techniques have become pivotal in medical image segmentation, but their success often relies on large, manually annotated datasets, which are expensive and labor-intensive to obtain. Additionally, different segmentation tasks frequently...

Optimization of deep learning models for inference in low resource environments.

Computers in biology and medicine
Artificial Intelligence (AI), and particularly deep learning (DL), has shown great promise to revolutionize healthcare. However, clinical translation is often hindered by demanding hardware requirements. In this study, we assess the effectiveness of ...

Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis.

Annals of medicine
BACKGROUND: Extrinsic adenomyosis exhibits heterogeneous clinical symptoms, with pain being more commonly reported. The relationship between magnetic resonance imaging (MRI) feature and symptom remains unclear.

Machine learning approach to DNA methylation and neuroimaging signatures as biomarkers for psychological resilience in young adults.

Behavioural brain research
Psychological resilience is influenced by both psychological and biological factors. However, the potential of using DNA methylation (DNAm) probes and brain imaging variables to predict psychological resilience remains unclear. This study aimed to in...

DEEP Q-NAS: A new algorithm based on neural architecture search and reinforcement learning for brain tumor identification from MRI.

Computers in biology and medicine
A significant obstacle in brain tumor treatment planning is determining the tumor's actual size. Magnetic resonance imaging (MRI) is one of the first-line brain tumor diagnosis. It takes a lot of effort and mostly depends on the operator's experience...

A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis.

Scientific data
Endometriosis affects approximately 190 million females of reproductive age worldwide. Magnetic Resonance Imaging (MRI) has been recommended as the primary non-invasive diagnostic method for endometriosis. This study presents new female pelvic MRI mu...