AIMC Topic: Female

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Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Radiology. Artificial intelligence Sep 1, 2024
Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and patients with ovarian cancer. Materials and Methods This retrospective study in...
Male Middle Aged Deep Learning Liver Cirrhosis Ovarian Neoplasms Radiographic Image Interpretation, Computer-Assisted Ascites Tomography, X-Ray Computed Female Aged Adult Humans Retrospective Studies
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Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Radiology. Artificial intelligence Sep 1, 2024
Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI withou...
Breast Neoplasms Middle Aged Deep Learning Contrast Media Retrospective Studies Female Aged Adult Image Interpretation, Computer-Assisted Carcinoma, Ductal, Breast Magnetic Resonance Imaging Predictive Value of Tests Breast Carcinoma, Intraductal, Noninfiltrating Humans
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A machine learning-based prediction model for gout in hyperuricemics: a nationwide cohort study.

Rheumatology (Oxford, England) Sep 1, 2024
OBJECTIVE: To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout.
Humans Machine Learning Cohort Studies Retrospective Studies Adult Israel Female Male ROC Curve Gout Aged Middle Aged Hyperuricemia Risk Assessment Uric Acid
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Automated deep learning segmentation of neuritic plaques and neurofibrillary tangles in Alzheimer disease brain sections using a proprietary software.

Journal of neuropathology and experimental neurology Sep 1, 2024
Neuropathological diagnosis of Alzheimer disease (AD) relies on semiquantitative analysis of phosphorylated tau-positive neurofibrillary tangles (NFTs) and neuritic plaques (NPs), without consideration of lesion heterogeneity in individual cases. We ...
Deep Learning Image Processing, Computer-Assisted Alzheimer Disease Neurofibrillary Tangles Humans Brain Software Female Plaque, Amyloid Aged Male
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Mixed methods assessment of the influence of demographics on medical advice of ChatGPT.

Journal of the American Medical Informatics Association : JAMIA Sep 1, 2024
OBJECTIVES: To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD.
Adult Male Sociodemographic Factors Demography Humans Artificial Intelligence Female
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Identification of A0 minimum ablative margins for colorectal liver metastases: multicentre, retrospective study using deformable CT registration and artificial intelligence-based autosegmentation.

The British journal of surgery Aug 30, 2024
BACKGROUND: Several ablation confirmation software methods for minimum ablative margin assessment have recently been developed to improve local outcomes for patients undergoing thermal ablation of colorectal liver metastases. Previous assessments wer...
Colorectal Neoplasms Male Middle Aged Liver Neoplasms Humans Disease Progression Margins of Excision Retrospective Studies Artificial Intelligence Radiofrequency Ablation Tomography, X-Ray Computed Female Aged
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Multimodal ischemic stroke recurrence prediction model based on the capsule neural network and support vector machine.

Medicine Aug 30, 2024
Ischemic stroke (IS) has a high recurrence rate. Machine learning (ML) models have been developed based on single-modal biochemical tests, and imaging data have been used to predict stroke recurrence. However, the prediction accuracy of these models ...
Recurrence China Support Vector Machine Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Male Retrospective Studies Middle Aged Female Deep Learning Aged Humans
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Machine learning-driven risk assessment of coronary heart disease: Analysis of NHANES data from 1999 to 2018.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences Aug 28, 2024
OBJECTIVES: The high incidence of coronary artery heart disease (CHD) poses a significant burden and challenge to public health systems globally. Effective prevention and early diagnosis of CHD have become key strategies to alleviate this burden. Thi...
Middle Aged Female Coronary Artery Disease Humans Risk Assessment Risk Factors Coronary Disease Machine Learning Nutrition Surveys Aged Algorithms United States Male
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Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.

Journal of the American College of Cardiology Aug 27, 2024
BACKGROUND: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD).
Humans Female Young Adult Adult Male Deep Learning Adolescent Child, Preschool Ventricular Dysfunction, Right Child Ventricular Dysfunction, Left Electrocardiography Heart Defects, Congenital Predictive Value of Tests Magnetic Resonance Imaging, Cine
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Enhancing Obstetric Ultrasonography With Artificial Intelligence in Resource-Limited Settings.

JAMA Aug 27, 2024
Resource-Limited Settings Humans Artificial Intelligence Point-of-Care Systems Pregnancy Ultrasonography, Prenatal Female Developing Countries Gestational Age
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