Fast and accurate classification of echocardiograms using deep learning

Echocardiography is essential to modern cardiology. However, human interpretation limits high throughput analysis, limiting echocardiography from reaching its full clinical and research potential for precision medicine. Deep learning is a cutting-edge machine-learning technique that has been useful in analyzing medical images but has not yet been widely applied to echocardiography, partly due to the complexity of echocardiograms' multi view, multi modality format. The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views. To this end, we anonymized 834,267 transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51 percent female, 26 percent obese) seen between 2000 and 2017 and labeled them according to standard views. Images covered a range of real world clinical variation. We built a multilayer convolutional neural network and used supervised learning to simultaneously classify 15 standard views. Eighty percent of data used was randomly chosen for training and 20 percent reserved for validation and testing on never seen echocardiograms. Using multiple images from each clip, the model classified among 12 video views with 97.8 percent overall test accuracy without overfitting. Even on single low resolution images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5 percent for board-certified echocardiographers. Confusional matrices, occlusion experiments, and saliency mapping showed that the model finds recognizable similarities among related views and classifies using clinically relevant image features. In conclusion, deep neural networks can classify essential echocardiographic views simultaneously and with high accuracy. Our results provide a foundation for more complex deep learning assisted echocardiographic interpretation.Comment: 31 pages, 8 figure

Artificial intelligence, machine learning, deep learning, and big data techniques for the advancements of superconducting technology: a road to smarter and intelligent superconductivity

The last 100 years of experience within the superconducting community have proven that addressing the challenges faced by this technology often requires incorporation of other disruptive techniques or technologies into superconductivity. Artificial intelligence (AI) methods including machine learning, deep learning, and big data techniques have emerged as highly effective tools in resolving challenges across various industries in recent decades. The concept of AI entails the development of computers that resemble human intelligence. The papers published in the focus issue, "Focus on Artificial Intelligence and Big Data for Superconductivity", represent the cutting-edge and forefront research activities in the field of AI for superconductivity

Comparative Review of Object Detection Algorithms in Small Single-Board Computers

Object detection is a crucial task in computer vision with a wide range of applications. However, deploying object detection algorithms on small single-board computers (SBCs) poses unique challenges. In this review article, we present an in-depth comparative analysis of object detection algorithms tailored for small SBCs. We have conducted an extensive literature review on existing research in object detection algorithms and evaluated the performance of different approaches on benchmark datasets. Our review encompasses cutting-edge deep learning methods, which are YOLO, SSD, and Faster R-CNN. We delve into the challenges and limitations of implementing these algorithms on small SBCs and offer recommendations for optimizing their performance in such environments. Our analysis aims to shed light on the strengths and weaknesses of various object detection algorithms for small SBCs, ultimately guiding practitioners in making informed decisions and identifying potential avenues for future research in this domain

Information Communication Technology and the African Student

To engage students, improve learning and become a cutting edge educator, it becomes necessary to combine traditional classroom instruction with online or mobile learning activities through the technological world which moves so fast and changes so rapidly. The objective of this study was to build an evidence-based framework that explains the challenge of the developing countries’ students in respect of maximizing the full potentials of the computer for educational activities. Questionnaires were administered to 213 students of the University of Ibadan and the Polytechnic, Ibadan, Oyo state of Nigeria. A major limitation to maximizing the full potentials of the computer is poor power energy supply. 62.9% of the population understudied pay to use computer for academic purposes. The cost per hour ranged between #50 to #100 plus. The benefits of collaborative learning and teaching with multiple instructors; integration of external expertise and video conferencing system to create geographically distributed discussion of panels of experts is visibly not maximized. Ultimately, the significant gain in economic productivity as a result of education which may be the most promising way to stimulate general economic growth is lost. This study strongly recommends improved access to computers for the African students.Keywords: Information communication technology, education, challenges, development, statistic

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence

How We Can Apply AI, and Deep Learning to our HR Functional Transformation and Core Talent Processes?

[Excerpt] While organizations agree with the importance of AI, only 31% are ready to embrace or have already applied it to their HR process. There are varying levels of acceptance for AI across the HR function. Top areas of implementation are: recruiting and hiring (49%), HR strategy and employee management decisions (31%), analysis of workplace policies (24%), and automation of tasks previously performed by humans (22%)

Expanding Our Boundaries With Technology

When Kate first came to speak at the ACL Conference at Lee University, I think I\u27m not exaggerating by saying we fell in love with her and she fell in love with us. We enjoyed her and her enthusiasm and she enjoyed us. I was trying to figure out what I was going to plan for another general session at this year\u27s conference and about that time Steve Preston sent me a note saying, \u27Just got a note from Kate, and she is so excited that ACL was coming back here and she wanted to come to the conference. Was there anything she could do?\u27 Our conference theme is about expanding our boundaries in the area of information literacy, which is certainly appropriate. We are also expanding our boundaries technologically. The two things go hand and hand. So, I asked her to come join us today. I\u27m sure all of you know by now, she is the head of SOLINET. For those of you not from this part of the country, it\u27s the largest of the OCLC networks and besides all the usual stuff, they are very well known especially in this part of the country for the wonderful workshops that they put on. So Kate, tell us what\u27s going on

Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専