AIMC Topic: Elasticity Imaging Techniques
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Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result from NHANES 2021-2023.
BMC gastroenterology
Apr 14, 2025
BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a common chronic liver disease and represents a significant public health issue. Nevertheless, current risk stratification methods remain inadequate. The study aimed to use m...
Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.
Scientific reports
Apr 11, 2025
Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent and improve prognosis potentially. We aimed to develop and validate an explainable prediction model ...
Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM.
Annals of medicine
Mar 19, 2025
BACKGROUND: Chronic hepatitis B (CHB) is a common cause of liver cirrhosis (LC), a condition associated with an unfavourable prognosis. Therefore, timely diagnosis of LC in CHB patients is crucial.
Deep Learning for High Speed Optical Coherence Elastography With a Fiber Scanning Endoscope.
IEEE transactions on medical imaging
Mar 17, 2025
Tissue stiffness is related to soft tissue pathologies and can be assessed through palpation or via clinical imaging systems, e.g., ultrasound or magnetic resonance imaging. Typically, the image based approaches are not suitable during interventions,...
ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning.
Scientific reports
Mar 13, 2025
Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variability in expert opinions and limited integration of advanced imaging techniques. This prospective cohort...
Quantification of tissue stiffness with magnetic resonance elastography and finite difference time domain (FDTD) simulation-based spatiotemporal neural network.
Magnetic resonance imaging
Feb 4, 2025
Quantification of tissue stiffness with magnetic resonance elastography (MRE) is an inverse problem that is sensitive to noise. Conventional methods for the purpose include direct inversion (DI) and local frequency estimation (LFE). In this study, we...
The impact of updated imaging software on the performance of machine learning models for breast cancer diagnosis: a multi-center, retrospective study.
Archives of gynecology and obstetrics
Jan 30, 2025
PURPOSE: Artificial Intelligence models based on medical (imaging) data are increasingly developed. However, the imaging software on which the original data is generated is frequently updated. The impact of updated imaging software on the performance...
Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI.
European radiology
Jan 9, 2025
BACKGROUND: Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (MRE), is well-accepted as a surrogate marker of liver fibrosis.
Liver fibrosis stage classification in stacked microvascular images based on deep learning.
BMC medical imaging
Jan 7, 2025
BACKGROUND: Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fib...
Deep denoising approach to improve shear wave phase velocity map reconstruction in ultrasound elastography.
Medical physics
Dec 23, 2024
BACKGROUND: Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not suffic...
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