AIMC Topic: Aged

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Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.

Cancer medicine
OBJECTIVES: Accurate and noninvasive detection of p53 status in isocitrate dehydrogenase mutant (IDH-mt) glioma is clinically meaningful for molecular stratification of glioma, yet it remains challenging. We aimed to investigate the diagnostic effica...

Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground-Glass Nodules Based on AI-Extracted Radiomic Features.

Thoracic cancer
BACKGROUND: With the widespread adoption of low-dose CT screening, the detection of pulmonary ground-glass nodules (GGNs) has risen markedly, presenting diagnostic challenges in distinguishing preinvasive lesions from invasive adenocarcinomas (IAC). ...

Developing an Explainable Prognostic Model for Acute Ischemic Stroke: Combining Clinical and Inflammatory Biomarkers With Machine Learning.

Brain and behavior
BACKGROUND: Predicting the prognosis of patients with acute cerebral infarction (ACI) is crucial for clinical decision-making and personalized treatment. However, existing models often lack the comprehensive integration of clinical and biological ind...

Effect of Deep Learning-Based Artificial Intelligence on Radiologists' Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging.

Korean journal of radiology
OBJECTIVE: To evaluate the effect of deep learning (DL)-based artificial intelligence (AI) software on the diagnostic performance of radiologists with different experience levels in detecting nigrosome 1 (N1) abnormalities on susceptibility map-weigh...

The Critical Role of APOE+ Macrophages in the Immune Microenvironment and Prognosis of Lung Adenocarcinoma.

Journal of cellular and molecular medicine
The immunoregulatory functions and clinical implications of APOE+ macrophages within the tumour microenvironment of lung adenocarcinoma remain incompletely defined. In this study, single-cell transcriptome analysis revealed distinct subsets of APOE+ ...

Diagnostic Performance of ChatGPT-4.0 in Histopathological Analysis of Gliomas: A Single Institution Experience.

Neuropathology : official journal of the Japanese Society of Neuropathology
This study aimed to evaluate the performance of ChatGPT-4.0 as a diagnostic support tool for pathologists in identifying different types of gliomas based on histopathological data and to compare its performance with that of another artificial intelli...

Glucagon-like Peptide-1 Receptor Agonists in Asthma Exacerbations: An Application of High-Dimensional Iterative Causal Forest to Identify Subgroups.

Pharmacoepidemiology and drug safety
BACKGROUND: Glucagon-like Peptide-1 Receptor Agonists (GLP1RA) may reduce asthma exacerbation (AE) risk, but it is unclear which populations benefit most. Recent pharmacoepidemiologic studies have employed iterative causal forest (iCF), a machine lea...

ODIASP: An Open-Source Software for Automated SMI Determination-Application to an Inpatient Population.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: The diagnosis of malnutrition has evolved with the GLIM recommendations, which advocate for integrating phenotypic criteria, including muscle mass measurement. The GLIM framework specifically suggests using skeletal muscle index (SMI) ass...

A multi-stage 3D convolutional neural network algorithm for CT-based lung segment parcellation.

Journal of applied clinical medical physics
BACKGROUND: Current approaches to lung parcellation utilize established fissures between lobes to provide estimates of lobar volume. However, deep learning segment parcellation provides the ability to better assess regional heterogeneity in ventilati...

Brain Age Prediction: Deep Models Need a Hand to Generalize.

Human brain mapping
Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns abou...