AIMC Topic: Reproducibility of Results

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Evaluating retinal blood vessels for predicting white matter hyperintensities in ischemic stroke: A deep learning approach.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Oct 10, 2024
OBJECTIVE: This study aims to investigate whether a deep learning approach incorporating retinal blood vessels can effectively identify ischemic stroke patients with a high burden of White Matter Hyperintensities (WMH) using Nuclear Magnetic Resonanc...
Middle Aged Risk Assessment Deep Learning Magnetic Resonance Imaging Predictive Value of Tests Humans Ischemic Stroke Image Interpretation, Computer-Assisted White Matter Risk Factors Retinal Vessels Aged Retrospective Studies Leukoencephalopathies Cross-Sectional Studies Female Male Reproducibility of Results
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Deep learning from head CT scans to predict elevated intracranial pressure.

Journal of neuroimaging : official journal of the American Society of Neuroimaging Oct 10, 2024
BACKGROUND AND PURPOSE: Elevated intracranial pressure (ICP) resulting from severe head injury or stroke poses a risk of secondary brain injury that requires neurosurgical intervention. However, currently available noninvasive monitoring techniques f...
Middle Aged Deep Learning Reproducibility of Results Humans Intracranial Pressure Aged Tomography, X-Ray Computed Intracranial Hypertension Adult Female Male
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AI discernment in foot and ankle surgery research: A survey investigation.

Foot and ankle surgery : official journal of the European Society of Foot and Ankle Surgeons Oct 9, 2024
BACKGROUND: This study evaluated the ability to differentiate between AI-generated and human-authored abstracts in foot and ankle surgery.
Humans Abstracting and Indexing Foot Ankle Orthopedic Procedures Artificial Intelligence Reproducibility of Results Biomedical Research Surveys and Questionnaires
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Analysis of nailfold capillaroscopy images with artificial intelligence: Data from literature and performance of machine learning and deep learning from images acquired in the SCLEROCAP study.

Microvascular research Oct 9, 2024
OBJECTIVE: To evaluate the performance of machine learning and then deep learning to detect a systemic scleroderma (SSc) landscape from the same set of nailfold capillaroscopy (NC) images from the French prospective multicenter observational study SC...
Female Reproducibility of Results Aged Adult Humans Prospective Studies Nails Microscopic Angioscopy Scleroderma, Systemic Support Vector Machine Male Predictive Value of Tests Middle Aged Image Interpretation, Computer-Assisted Deep Learning France
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A systematic scoping review exploring variation in practice in specimen mammography for Intraoperative Margin Analysis in Breast Conserving Surgery and the role of artificial intelligence in optimising diagnostic accuracy.

European journal of radiology Oct 9, 2024
PURPOSE: Specimen Mammography (SM) is commonly used in Breast Conserving Surgery (BCS) for intraoperative margin analysis. A systematic scoping review was conducted to identify sources of methodological variation in Specimen Mammography Interpretatio...
Breast Neoplasms Artificial Intelligence Mammography Female Margins of Excision Reproducibility of Results Mastectomy, Segmental Humans Sensitivity and Specificity
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Lumbar Radicular Pain in the Eyes of Artificial Intelligence: Can You 'Imagine' What I 'Feel'?

World neurosurgery Oct 8, 2024
OBJECTIVE: Pain is a complex sensory and emotional experience that significantly impacts individuals' well-being. Lumbar radicular pain (LRP) is a prevalent neuropathic pain affecting 9.9% to 25% of the population annually. Accurate identification of...
Lumbar Vertebrae Humans Radiculopathy Artificial Intelligence Low Back Pain Reproducibility of Results
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Energy-integrating detector based ultra-high-resolution CT with deep learning reconstruction for the assessment of calcified lesions in coronary artery disease.

Journal of cardiovascular computed tomography Oct 8, 2024
BACKGROUND: The aim of this study to compare of the image quality of calcified lesions in coronary artery disease between deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR) on energy-integrating detector (EID) based ul...
Severity of Illness Index Deep Learning Predictive Value of Tests Aged Radiographic Image Interpretation, Computer-Assisted Vascular Calcification Humans Coronary Angiography Coronary Artery Disease Phantoms, Imaging Male Retrospective Studies Coronary Vessels Computed Tomography Angiography Middle Aged Multidetector Computed Tomography Female Reproducibility of Results
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Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations.

The international journal of cardiovascular imaging Oct 7, 2024
PURPOSE: This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic mal...
Coronary Artery Disease Running Athletes Coronary Vessels Asymptomatic Diseases Computed Tomography Angiography Coronary Stenosis Fractional Flow Reserve, Myocardial Severity of Illness Index Aged Humans Artificial Intelligence Predictive Value of Tests Male Prospective Studies Coronary Angiography Middle Aged Reproducibility of Results Radiographic Image Interpretation, Computer-Assisted
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Comparing Human-Level and Machine Learning Model Performance in White Blood Cell Morphology Assessment.

European journal of haematology Oct 6, 2024
INTRODUCTION: There is an increasing research focus on the role of machine learning in the haematology laboratory, particularly in blood cell morphologic assessment. Human-level performance is an important baseline and goal for machine learning. This...
Humans Observer Variation Machine Learning Reproducibility of Results Image Processing, Computer-Assisted Leukocytes
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Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

Diabetes, obesity & metabolism Oct 6, 2024
AIM: To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation.
Reproducibility of Results Gene Expression Profiling Genome-Wide Association Study Diabetic Nephropathies Humans Machine Learning Biomarkers Mendelian Randomization Analysis Quantitative Trait Loci
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