Automatic Estimation of Coronary Blood Flow Velocity Step 1 for Developing a Tool to Diagnose Patients With Micro-Vascular Angina Pectoris

Khanmohammadi, Mahdieh; Engan, Kjersti; Sæland, Charlotte; Eftestøl, Trygve; Larsen, Alf Inge

doi:10.3389/fcvm.2019.00001

Cited by 26 publications

(28 citation statements)

References 24 publications

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“…In the absence of genomic DNA, platelets preserve organells including mitochondria from megakaryocytes to maintain its structure and function. The, platelet’s lifespan is (7–10 days), is determined by the mitochondria ( Melchinger et al, 2019 ). The packaging of 5–8 mitochondria is critical for vital platelet functions including aerobic respiration and metabolism.…”

Section: Platelet Mitochondria and Coagulopathymentioning

confidence: 99%

“…( Hayashi et al, 2011 ). Importantly, it has been recently shown that apoptotic platelets may induce clotting ≥50-fold faster than normal platelets ( Melchinger et al, 2019 ). In addition, platelets have recently gained attention as important mediators of the immune system ( van der Meijden & Heemskerk, 2019 ).…”

Section: Platelet Mitochondria and Coagulopathymentioning

confidence: 99%

“…Further actions for platelet mitochondria include; platelet activation of permeability transition, ROS generation, reduction of mitochondrial membrane potential and platelet apoptosis. It is noteworthy that an increase in mitochondrial ROS production in platelets leads to severe oxidative stress that alters ATP production and mitochondrial membrane potential leading to further platelet activation ( Ran et al, 2009 , Tang et al, 2011 , Wang et al, 2017 , Melchinger et al, 2019 ). Moreover, the upregulation of ROS drives thrombosis through cytochrome-C release by mitochondria triggers the apoptotic caspase cascade driven by BAX and BAD resulting in mitochondrial damage and apoptosis ( Wang et al, 2017 , Melchinger et al, 2019 ).…”

Section: Platelet Mitochondria and Coagulopathymentioning

confidence: 99%

“…In healthy context, mitophagy protects platelets from oxidative stress and mitochondrial destruction by removing damaged mitochondria to prevent platelet apoptosis ( Lee et al, 2016 ). When platelet mitophagy is impaired, which is implicated in COVID-19 pathogenesis, increased platelet apoptosis occurs contributing in enhanced thrombosis ( Lee et al, 2016 , Wang et al, 2017 , Melchinger et al, 2019 ). Therfore, preserving platelet mitochondrial function may be an additional means of decreasing the risk of potentially fatal thrombotic events in COVID-19 pathogenesis.…”

Section: Platelet Mitochondria and Coagulopathymentioning

confidence: 99%

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Mitochondria and microbiota dysfunction in COVID-19 pathogenesis

et al. 2020

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Highlights Mitochondria are the hub of cellular oxidative homeostasis. Mitochondria are the major source of reactive oxygen species (ROS). Extracellular mitochondria are found in blood, in circulating platelets and vesicles. COVID-19 pathogenesis is aggravated by the hyper- inflammatory state. Inflammation activates events leading to microbiota & mitochondrial oxidative damage. Mitochondrial damage contributes to coagulopathy, ferroptosis & microbial dysbiosis. Blood & platelet mitochondria dysfunction may accelerate systemic coagulopathy events. Targeting mitochondria dysfunction may provide useful therapeutic strategies against COVID-19 pathogenesis.

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Section: Platelet Mitochondria and Coagulopathymentioning

confidence: 99%

Section: Platelet Mitochondria and Coagulopathymentioning

confidence: 99%

Section: Platelet Mitochondria and Coagulopathymentioning

confidence: 99%

Section: Platelet Mitochondria and Coagulopathymentioning

confidence: 99%

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Mitochondria and microbiota dysfunction in COVID-19 pathogenesis

et al. 2020

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“…These measures include myocardial mass, ventricular volumes, wall thickness, wall motion and ejection fraction. State-of-the-art performance for automatic segmentation is achieved with artificial neural networks [ 6 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

et al. 2021

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Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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