AIMC Topic: Magnetic Resonance Imaging

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Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

Schizophrenia bulletin
BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (...

segcsvd: A Convolutional Neural Network-Based Tool for Quantifying White Matter Hyperintensities in Heterogeneous Patient Cohorts.

Human brain mapping
White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel disease (CSVD). WMH are associated with cognitive decline and increased risk of stroke and dementia, and a...

A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI.

Mathematical biosciences and engineering : MBE
The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigatio...

Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.

Cancer medicine
BACKGROUND: Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-acquired pre- and mid-treatment quantitatively capture informatio...

Large Language Model Ability to Translate CT and MRI Free-Text Radiology Reports Into Multiple Languages.

Radiology
Background High-quality translations of radiology reports are essential for optimal patient care. Because of limited availability of human translators with medical expertise, large language models (LLMs) are a promising solution, but their ability to...

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Human brain mapping
Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging mod...

Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.

Clinical and experimental dental research
OBJECTIVES: Advancements in artificial intelligence (AI)-driven predictive modeling in dentistry are outpacing the clinical translation of research findings. Predictive modeling uses statistical methods to anticipate norms related to TMJ dynamics, co...

Prediction of Post Traumatic Epilepsy Using MR-Based Imaging Markers.

Human brain mapping
Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of PTE, current methods for predicting its occurrence remain limited. In this study, we aimed to ident...

[Study on predicting new onset heart failure events in patients with hypertrophic cardiomyopathy using machine learning algorithms based on clinical and magnetic resonance features].

Zhonghua xin xue guan bing za zhi
To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms. The study was a retrospective co...