AIMC Topic: Microcirculation
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Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model.
Scientific reports
Jan 24, 2025
Angio-based microvascular resistance (AMR) as a potential alternative to the index of microcirculatory resistance (IMR) and its relationship with microvascular obstruction (MVO) and other cardiac magnetic resonance (CMR) parameters still lacks compre...
Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning.
Computer methods and programs in biomedicine
Jul 23, 2024
BACKGROUND AND OBJECTIVES: Instantaneous wave-free ratio (iFR) is a new invasive indicator of myocardial ischaemia, and its diagnostic performance is as good as the "gold standard" of myocardial ischaemia diagnosis: fractional flow reserve (FFR). iFR...
A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study.
Critical care (London, England)
Jul 10, 2024
BACKGROUND: Impaired microcirculation is a cornerstone of sepsis development and leads to reduced tissue oxygenation, influenced by fluid and catecholamine administration during treatment. Hyperspectral imaging (HSI) is a non-invasive bedside technol...
Artificial neural networks trained on simulated multispectral data for real-time imaging of skin microcirculatory blood oxygen saturation.
Journal of biomedical optics
Jul 10, 2024
SIGNIFICANCE: Imaging blood oxygen saturation ( ) in the skin can be of clinical value when studying ischemic tissue. Emerging multispectral snapshot cameras enable real-time imaging but are limited by slow analysis when using inverse Monte Carlo (M...
A comprehensive approach to prediction of fractional flow reserve from deep-learning-augmented model.
Computers in biology and medicine
Jan 3, 2024
The underuse of invasive fractional flow reserve (FFR) in clinical practice has motivated research towards non-invasive prediction of FFR. Although the non-invasive derivation of FFR (FFR) using computational fluid dynamics (CFD) principles has becom...
Robust Vascular Segmentation for Raw Complex Images of Laser Speckle Contrast Based on Weakly Supervised Learning.
IEEE transactions on medical imaging
Jan 2, 2024
Laser speckle contrast imaging (LSCI) is widely used for in vivo real-time detection and analysis of local blood flow microcirculation due to its non-invasive ability and excellent spatial and temporal resolution. However, vascular segmentation of LS...
Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning.
Journal of biomedical optics
Mar 20, 2023
SIGNIFICANCE: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static...
Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy.
Scientific reports
May 11, 2021
Microvascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcir...
Lightweight pyramid network with spatial attention mechanism for accurate retinal vessel segmentation.
International journal of computer assisted radiology and surgery
Mar 22, 2021
PURPOSE: The morphological characteristics of retinal vessels are vital for the early diagnosis of pathological diseases such as diabetes and hypertension. However, the low contrast and complex morphology pose a challenge to automatic retinal vessel ...
Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning.
Journal of cancer research and clinical oncology
Aug 27, 2020
PURPOSE: Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI...
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