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Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity.
Scientific reports
Jan 24, 2025
This study develops predictive models for Chinese female patients with VL utilizing machine learning techniques. The aim is to create an effective model that can assist in clinical diagnosis and treatment of vaginal relaxation, thereby enhancing wome...
A machine learning based algorithm accurately stages liver disease by quantification of arteries.
Scientific reports
Jan 24, 2025
A major histologic feature of cirrhosis is the loss of liver architecture with collapse of tissue and vascular changes per unit. We developed qVessel to quantify the arterial density (AD) in liver biopsies with chronic disease of varied etiology and ...
AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.
Journal of medical Internet research
Jan 24, 2025
BACKGROUND: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection...
A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches.
Molecular genetics and genomics : MGG
Jan 24, 2025
Stroke is a leading cause of death and disability globally, particularly in China. Identifying risk factors for stroke at an early stage is critical to improving patient outcomes and reducing the overall disease burden. However, the complexity of str...
A commercial AI tool untrained for COVID-19 demonstrates slight improvement in the interpretation of COVID-19 pneumonia x-rays, especially among inexperienced readers.
Radiologia
Jan 24, 2025
INTRODUCTION: Our objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumo...
Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
JCO clinical cancer informatics
Jan 24, 2025
PURPOSE: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evalu...
AI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study.
PloS one
Jan 24, 2025
To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standard double-reading in real-world clinical settings for rib fractures (RFs) detection on CT images. This study included 243 consecutive chest trauma pat...
Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.
PloS one
Jan 24, 2025
Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence the success of treatment. The objective of this study is to define the model for predicting the adh...
Explainable machine learning model for assessing health status in patients with comorbid coronary heart disease and depression: Development and validation study.
International journal of medical informatics
Jan 23, 2025
BACKGROUND: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and vali...
Application of elastic net for clinical outcome prediction and classification in progressive supranuclear palsy: A multicenter cohort study.
Parkinsonism & related disorders
Jan 23, 2025
BACKGROUND: Previous studies have used machine learning to identify clinically relevant atrophic regions in progressive supranuclear palsy (PSP). This study applied Elastic Net (EN) in PSP to uncover key atrophic patterns, offering a novel approach t...
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