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Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach.
BMC medical informatics and decision making
Jan 17, 2025
BACKGROUND: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographi...
Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.
BMC medical imaging
Jan 17, 2025
OBJECTIVE: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aim...
Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke.
Scientific reports
Jan 17, 2025
To validate the clinical feasibility of deep learning-driven magnetic resonance angiography (DL-driven MRA) collateral map in acute ischemic stroke. We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-N...
Machine learning to predict periprosthetic joint infections following primary total hip arthroplasty using a national database.
Archives of orthopaedic and trauma surgery
Jan 17, 2025
INTRODUCTION: Periprosthetic joint infection (PJI) following total hip arthroplasty (THA) remains a devastating complication for patients and surgeons. Given the implications of these infections and the current paucity of risk calculators utilizing m...
An open source convolutional neural network to detect and localize distal radius fractures on plain radiographs.
European journal of trauma and emergency surgery : official publication of the European Trauma Society
Jan 17, 2025
PURPOSE: Distal radius fractures (DRFs) are often initially assessed by junior doctors under time constraints, with limited supervision, risking significant consequences if missed. Convolutional Neural Networks (CNNs) can aid in diagnosing fractures....
Association between automatic AI-based quantification of airway-occlusive mucus plugs and all-cause mortality in patients with COPD.
Thorax
Jan 17, 2025
In this cohort study involving 9399 current and former smokers from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease study, we assessed the relationship between artificial intelligence-quantified mucus plugs on chest CTs and all-caus...
Artificial Intelligence-Powered Human Epidermal Growth Factor Receptor 2 and Tumor Microenvironment Analysis in Human Epidermal Growth Factor Receptor 2-Amplified Metastatic Colorectal Cancer: Exploratory Analysis of Phase II TRIUMPH Trial.
JCO precision oncology
Jan 17, 2025
PURPOSE: Human epidermal growth factor receptor 2 (HER2)-targeted therapies have shown promise in treating -amplified metastatic colorectal cancer (mCRC). Identifying optimal biomarkers for treatment decisions remains challenging. This study explores...
Glaucoma detection and staging from visual field images using machine learning techniques.
PloS one
Jan 17, 2025
PURPOSE: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glau...
Characteristics of ChatGPT users from Germany: Implications for the digital divide from web tracking data.
PloS one
Jan 17, 2025
A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage o...
Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.
PloS one
Jan 17, 2025
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of im...
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