scholarly journals Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

2021 ◽  
Vol 8 ◽  
Author(s):  
Yaghoub Dabiri ◽  
Jiang Yao ◽  
Vaikom S. Mahadevan ◽  
Daniel Gruber ◽  
Rima Arnaout ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mitral Valve ◽  
Principal Component ◽  
Generative Adversarial Networks ◽  
3 Dimensional ◽  
Adversarial Networks ◽  
Key Points ◽  
Particle Hydrodynamics ◽  
Smoothed Particle ◽  
Geometrical Data

Severe mitral regurgitation (MR) is a cardiac disease that can lead to fatal consequences. MitraClip (MC) intervention is a percutaneous procedure whereby the mitral valve (MV) leaflets are connected along the edge using MCs. The outcomes of the MC intervention are not known in advance, i.e., the outcomes are quite variable. Artificial intelligence (AI) can be used to guide the cardiologist in selecting optimal MC scenarios. In this study, we describe an atlas of shapes as well as different scenarios for MC implantation for such an AI analysis. We generated the MV geometrical data from three different sources. First, the patients' 3-dimensional echo images were used. The pixel data from six key points were obtained from three views of the echo images. Using PyGem, an open-source morphing library in Python, these coordinates were used to create the geometry by morphing a template geometry. Second, the dimensions of the MV, from the literature were used to create data. Third, we used machine learning methods, principal component analysis, and generative adversarial networks to generate more shapes. We used the finite element (FE) software ABAQUS to simulate smoothed particle hydrodynamics in different scenarios for MC intervention. The MR and stresses in the leaflets were post-processed. Our physics-based FE models simulated the outcomes of MC intervention for different scenarios. The MR and stresses in the leaflets were computed by the FE models for a single clip at different locations as well as two and three clips. Results from FE simulations showed that the location and number of MCs affect subsequent residual MR, and that leaflet stresses do not follow a simple pattern. Furthermore, FE models need several hours to provide the results, and they are not applicable for clinical usage where the predicted outcomes of MC therapy are needed in real-time. In this study, we generated the required dataset for the AI models which can provide the results in a matter of seconds.

scholarly journals Fractional Wavelet-Based Generative Scattering Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Jiasong Wu ◽  
Xiang Qiu ◽  
Jing Zhang ◽  
Fuzhi Wu ◽  
Youyong Kong ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Reduction Method ◽  
Gaussian White Noise ◽  
Principal Component ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Adversarial Networks ◽  
Dimensionality Reduction Method

Generative adversarial networks and variational autoencoders (VAEs) provide impressive image generation from Gaussian white noise, but both are difficult to train, since they need a generator (or encoder) and a discriminator (or decoder) to be trained simultaneously, which can easily lead to unstable training. To solve or alleviate these synchronous training problems of generative adversarial networks (GANs) and VAEs, researchers recently proposed generative scattering networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate an image. The advantage of GSNs is that the parameters of ScatNets do not need to be learned, while the disadvantage of GSNs is that their ability to obtain representations of ScatNets is slightly weaker than that of CNNs. In addition, the dimensionality reduction method of principal component analysis (PCA) can easily lead to overfitting in the training of GSNs and, therefore, affect the quality of generated images in the testing process. To further improve the quality of generated images while keeping the advantages of GSNs, this study proposes generative fractional scattering networks (GFRSNs), which use more expressive fractional wavelet scattering networks (FrScatNets), instead of ScatNets as the encoder to obtain features (or FrScatNet embeddings) and use similar CNNs of GSNs as the decoder to generate an image. Additionally, this study develops a new dimensionality reduction method named feature-map fusion (FMF) instead of performing PCA to better retain the information of FrScatNets,; it also discusses the effect of image fusion on the quality of the generated image. The experimental results obtained on the CIFAR-10 and CelebA datasets show that the proposed GFRSNs can lead to better generated images than the original GSNs on testing datasets. The experimental results of the proposed GFRSNs with deep convolutional GAN (DCGAN), progressive GAN (PGAN), and CycleGAN are also given.

The Frontiers of Deep Learning for Earth System Modelling 

2021 ◽  
Author(s):  
David Hall
Keyword(s):  
Artificial Intelligence ◽  
Bias Correction ◽  
Neural Nets ◽  
Generative Adversarial Networks ◽  
System Modelling ◽  
Earth System ◽  
Adversarial Networks ◽  
The Earth ◽  
Medium Range ◽  
Earth System Modelling

<p>This talk gives an overview of cutting-edge artificial intelligence applications and techniques for the earth-system sciences. We survey the most important recent contributions in areas including extreme weather, physics emulation, nowcasting, medium-range forecasting, uncertainty quantification, bias-correction, generative adversarial networks, data in-painting, network-HPC coupling, physics-informed neural nets, and geoengineering, amongst others. Then, we describe recent AI breakthroughs that have the potential to be of greatest benefit to the geosciences. We also discuss major open challenges in AI for science and their potential solutions. This talk is a living document, in that it is updated frequently, in order to accurately relect this rapidly changing field.</p>

scholarly journals Serlio and Artificial Intelligence: Problematizing the Image-to-Object Workflow

2021 ◽  
pp. 3-12
Author(s):  
Jean Jaminet ◽  
Gabriel Esquivel ◽  
Shane Bugni
Keyword(s):  
Artificial Intelligence ◽  
Data Storage ◽  
Intelligent Systems ◽  
Communication Theory ◽  
Human Perception ◽  
Generative Adversarial Networks ◽  
Virtual Design ◽  
Storage And Retrieval ◽  
Adversarial Networks ◽  
Design Production

AbstractVirtual design production demands that information be increasingly encoded and decoded with image compression technologies. Since the Renaissance, the discourses of language and drawing and their actuation by the classical disciplinary treatise have been fundamental to the production of knowledge within the building arts. These early forms of data compression provoke reflection on theory and technology as critical counterparts to perception and imagination unique to the discipline of architecture. This research examines the illustrated expositions of Sebastiano Serlio through the lens of artificial intelligence (AI). The mimetic powers of technological data storage and retrieval and Serlio’s coded operations of orthographic projection drawing disclose other aesthetic and formal logics for architecture and its image that exist outside human perception. Examination of aesthetic communication theory provides a conceptual dimension of how architecture and artificial intelligent systems integrate both analog and digital modes of information processing. Tools and methods are reconsidered to propose alternative AI workflows that complicate normative and predictable linear design processes. The operative model presented demonstrates how augmenting and interpreting layered generative adversarial networks drive an integrated parametric process of three-dimensionalization. Concluding remarks contemplate the role of human design agency within these emerging modes of creative digital production.

scholarly journals Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Holly Burrows ◽  
Javad Zarrin ◽  
Lakshmi Babu-Saheer ◽  
Mahdi Maktab-Dar-Oghaz
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
User Interface ◽  
Convolutional Neural Networks ◽  
Mental Health Problems ◽  
Well Being ◽  
Working Environment ◽  
Generative Adversarial Networks ◽  
Deep Convolutional Neural Networks ◽  
Adversarial Networks

It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them.

Conditional Generative Adversarial Networks to Model iPSC-Derived Cancer Stem Cells

2020 ◽  
Vol 24 (1) ◽  
pp. 134-141 ◽  
Author(s):  
Saori Aida ◽  
Hiroyuki Kameda ◽  
Sakae Nishisako ◽  
Tomonari Kasai ◽  
Atsushi Sato ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Stem Cells ◽  
Drug Discovery ◽  
Cancer Stem Cells ◽  
Low Cost ◽  
Generative Adversarial Networks ◽  
Cell Detection ◽  
Smart System ◽  
Adversarial Networks ◽  
Normal Image

The realization of effective and low-cost drug discovery is imperative to enable people to easily purchase and use medicines when necessary. This paper reports a smart system for detecting iPSC-derived cancer stem cells by using conditional generative adversarial networks. This system with artificial intelligence (AI) accepts a normal image from a microscope and transforms it into a corresponding fluorescent-marked fake image. The AI system learns 10,221 sets of paired pictures as input. Consequently, the system’s performance shows that the correlation between true fluorescent-marked images and fake fluorescent-marked images is at most 0.80. This suggests the fundamental validity and feasibility of our proposed system. Moreover, this research opens a new way for AI-based drug discovery in the process of iPSC-derived cancer stem cell detection.

Elastic-Plastic and Frature Analysis of Mobile Phones Using SPH

2006 ◽  
Author(s):  
Nobuki Yamagata ◽  
Yuzuru Sakai ◽  
Pedro Marcal
Keyword(s):  
Structural Analysis ◽  
Mobile Phones ◽  
Particle Method ◽  
Particle Distributions ◽  
3 Dimensional ◽  
Elastic Plastic ◽  
Particle Hydrodynamics ◽  
Gas Dynamics Problems ◽  
Smoothed Particle ◽  
Sph Algorithm

Smoothed Particle Hydrodynamics (SPH) was invented by Lucy[1], Monaghan and Gingold [2] for gas dynamics problems in astrophysics and extended to treat solid continua in this decade[3]]. The SPH technique uses no underlying grid — it is a pure Lagrangian particle method. The absence of a mesh and the calculation of interactions among particles based on their separation alone that large deformations can be computed without difficulty. It is for this reason that SPH has the potential to be a valuable computational tool. In this paper we have been using the SPH algorithm to compute the structural analysis of the mobile phones without mesh data. Using the visualization software MPAVE the particle distributions for the mobile phone could be easily produced in 3 dimensions and the elastic-plastic analysis and the fracture analysis have been performed effectively. The results show the possibility for practical use of a particle method to 3 dimensional structural analysis of the usual industrial products.

scholarly journals De Novo Drug Design using Artificial Intelligence ASYNT-GAN

2020 ◽  
Author(s):  
Ivan Jacobs ◽  
Manolis Maragoudakis
Keyword(s):  
Molecular Structure ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
De Novo ◽  
Protein Structures ◽  
Generative Adversarial Networks ◽  
Text Generation ◽  
De Novo Drug Design ◽  
3D Space ◽  
Adversarial Networks

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

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