AIMC Topic: Aged
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Advancements in Frank's sign Identification using deep learning on 3D brain MRI.
Scientific reports
Jan 18, 2025
Frank's sign (FS) is a diagnostic marker associated with aging and various health conditions. Despite its clinical significance, there lacks a standardized method for its identification. This study aimed to develop a deep learning model for automated...
A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data.
Scientific reports
Jan 18, 2025
Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a variety of symptoms including, persistent coughing and mucus production, shortness of breath, wheezing, and chest tightness. As the disease advances, exacerbations, i.e. a...
Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.
BMC medical imaging
Jan 18, 2025
OBJECTIVES: The objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs).
ds-FCRN: three-dimensional dual-stream fully convolutional residual networks and transformer-based global-local feature learning for brain age prediction.
Brain structure & function
Jan 18, 2025
The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive...
The efficiency of artificial intelligence for management and clinical decision-making in the identification of patients with hidden HCV infection (Intelligen-C strategy).
Gastroenterologia y hepatologia
Jan 18, 2025
INTRODUCTION: Artificial intelligence (AI) allows the optimization of diagnostic processes for hepatitis C virus (HCV) patients. Our objective was to evaluate the clinical, economic, and management benefits of an AI-based clinical decision support sy...
CT-based Machine Learning Radiomics Modeling: Survival Prediction and Mechanism Exploration in Ovarian Cancer Patients.
Academic radiology
Jan 17, 2025
RATIONALE AND OBJECTIVES: To create a radiomics model based on computed tomography (CT) to predict overall survival in ovarian cancer patients. To combine Rad-score with genomic data to explore the association between gene expression and Rad-score.
Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma.
European journal of nuclear medicine and molecular imaging
Jan 17, 2025
PURPOSE: Radiomics-based machine learning (ML) models of amino acid positron emission tomography (PET) images have shown efficiency in glioma prediction tasks. However, their clinical impact on physician interpretation remains limited. This study inv...
Deep Learning-Derived Quantitative Scores for Chronic Rhinosinusitis Assessment: Correlation With Quality of Life Outcomes.
American journal of rhinology & allergy
Jan 17, 2025
BackgroundComputed tomography (CT) plays a crucial role in assessing chronic rhinosinusitis, but lacks objective quantifiable indicators.ObjectiveThis study aimed to use deep learning for automated sinus segmentation to generate distinct quantitative...
Deep learning-based free-water correction for single-shell diffusion MRI.
Magnetic resonance imaging
Jan 17, 2025
Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white...
Deep learning-aided diagnosis of acute abdominal aortic dissection by ultrasound images.
Emergency radiology
Jan 17, 2025
PURPOSE: Acute abdominal aortic dissection (AD) is a serious disease. Early detection based on ultrasound (US) can improve the prognosis of AD, especially in emergency settings. We explored the ability of deep learning (DL) to diagnose abdominal AD i...
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