AIMC Topic: Middle Aged

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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...

Paternally Expressed Gene 10 Promoter Methylation Level as a Predictor of HBeAg Seroconversion in Chronic Hepatitis B Patients.

Journal of medical virology
The management of chronic hepatitis B (CHB) encounters challenges like suboptimal antiviral response and the lack of predictive biomarkers. In this study, the role of paternally expressed gene 10 (PEG10) in hepatitis B e antigen (HBeAg) seroconversio...

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...

Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).

Yonsei medical journal
PURPOSE: Black-blood (BB) magnetic resonance images (MRI) offer superior image contrast for the detection and segmentation of brain metastases (BMs). This study investigated the efficacy and accuracy of deep learning (DL) architectures and post-proce...

Deep Learning-Based Landmark Detection Model for Multiple Foot Deformity Classification: A Dual-Center Study.

Yonsei medical journal
PURPOSE: To introduce heatmap-in-heatmap (HIH)-based model for automated diagnosis of foot deformities using weight-bearing foot radiographs, aiming to address the labor-intensive and variable nature of manual diagnosis.