AIMC Topic: Reproducibility of Results
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Comparison of conventional diffusion-weighted imaging and multiplexed sensitivity-encoding combined with deep learning-based reconstruction in breast magnetic resonance imaging.
Magnetic resonance imaging
Dec 21, 2024
PURPOSE: To evaluate the feasibility of multiplexed sensitivity-encoding (MUSE) with deep learning-based reconstruction (DLR) for breast imaging in comparison with conventional diffusion-weighted imaging (DWI) and MUSE alone.
Quantifying interpretation reproducibility in Vision Transformer models with TAVAC.
Science advances
Dec 20, 2024
Deep learning algorithms can extract meaningful diagnostic features from biomedical images, promising improved patient care in digital pathology. Vision Transformer (ViT) models capture long-range spatial relationships and offer robust prediction pow...
Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.
Magnetic resonance imaging
Dec 19, 2024
OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency o...
Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.
Cell reports. Medicine
Dec 19, 2024
Inability to express the confidence level and detect unseen disease classes limits the clinical implementation of artificial intelligence in the real world. We develop a foundation model with uncertainty estimation (FMUE) to detect 16 retinal conditi...
Machine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson's Disease Mobility Assessments.
Sensors (Basel, Switzerland)
Dec 19, 2024
Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test-retest reliability o...
Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction.
Cardiovascular diabetology
Dec 18, 2024
BACKGROUND: The incidence and mortality of first-ever strokes have risen sharply, especially in the intensive care unit (ICU). Emerging surrogate for insulin resistance, triglyceride-glucose index (TyG), has been linked to stroke prognosis. We aims t...
Length of Stay
Blood Glucose
Time Factors
Hospital Mortality
Stroke
Triglycerides
Predictive Value of Tests
Cause of Death
Male
Middle Aged
Risk Assessment
China
Female
Reproducibility of Results
Risk Factors
Aged
Humans
Machine Learning
Databases, Factual
Retrospective Studies
Biomarkers
Critical Illness
Prognosis
Intensive Care Units
Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements.
Open heart
Dec 18, 2024
BACKGROUND: Cardiac amyloidosis (CA) is an underdiagnosed, progressive and lethal disease. Machine learning applied to common measurements derived from routine echocardiogram studies can inform suspicion of CA.
Lymphoma triage from H&E using AI for improved clinical management.
Journal of clinical pathology
Dec 18, 2024
AIMS: In routine diagnosis of lymphoma, initial non-specialist triage is carried out when the sample is biopsied to determine if referral to specialised haematopathology services is needed. This places a heavy burden on pathology services, causes del...
Deep learning algorithm enables automated Cobb angle measurements with high accuracy.
Skeletal radiology
Dec 17, 2024
OBJECTIVE: To determine the accuracy of automatic Cobb angle measurements by deep learning (DL) on full spine radiographs.
Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.
Journal of magnetic resonance imaging : JMRI
Dec 17, 2024
BACKGROUND: Deep learning (DL) often requires an image quality metric; however, widely used metrics are not designed for medical images.
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