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Application of machine-learning methods in age-at-death estimation from 3D surface scans of the adult acetabulum.
Forensic science international
Oct 28, 2024
OBJECTIVE: Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate ind...
Predicting Portal Pressure Gradient in Patients with Decompensated Cirrhosis: A Non-invasive Deep Learning Model.
Digestive diseases and sciences
Oct 28, 2024
BACKGROUND: A high portal pressure gradient (PPG) is associated with an increased risk of failure to control esophagogastric variceal hemorrhage and refractory ascites in patients with decompensated cirrhosis. However, direct measurement of PPG is in...
Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.
Ecotoxicology and environmental safety
Oct 28, 2024
There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on hea...
Development and Evaluation of a Convolutional Neural Network for Microscopic Diagnosis Between Pleomorphic Adenoma and Carcinoma Ex-Pleomorphic Adenoma.
Head & neck
Oct 27, 2024
AIMS: To develop a model capable of distinguishing carcinoma ex-pleomorphic adenoma from pleomorphic adenoma using a convolutional neural network architecture.
Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model.
ESC heart failure
Oct 27, 2024
AIMS: Heart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs).
Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.
Oral diseases
Oct 27, 2024
OBJECTIVES: Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes.
Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.
Abdominal radiology (New York)
Oct 26, 2024
PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.
Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram.
Diagnostic and interventional imaging
Oct 26, 2024
PURPOSE: The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC).
Impact of nutrition-related laboratory tests on mortality of patients who are critically ill using artificial intelligence: A focus on trace elements, vitamins, and cholesterol.
Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition
Oct 25, 2024
BACKGROUND: This study aimed to understand the collective impact of trace elements, vitamins, cholesterol, and prealbumin on patient outcomes in the intensive care unit (ICU) using an advanced artificial intelligence (AI) model for mortality predicti...
Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network.
European radiology
Oct 25, 2024
OBJECTIVE: To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of virtual contrast-enhanced (vCE) breast MRI.
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