SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review

Introduction. Medical imaging techniques can diagnose many diseases at the early stages of their development, improving the patient survival. Artificial intelligence (AI) systems, requiring the high-quality annotated and marked-up sets of medical images, are a suitable and promising means of improving the diagnostics’ quality. The purpose of the study was to develop a methodology and software for creating AIS training sets. Material and methods. We compared the main annotation methods’ performance and accuracy and based the information system on the most efficient method in both domains to develop an optimal approach. To markup objects of interest, we used the cluster model of lesions localization previously developed by the authors. We used C++ and Kotlin programming languages for software development. Results. A structured annotation template with delivered a glossary of terms became the basis of the information system. The latter consists of three interacting modules, two of which are executed on a remote server’s capacities and one on a personal computer or mobile device of the end-user. The first module is a web service responsible for the workflow logic. The second module, a web server, is responsible for interacting with client applications. Its role is to identify users and manage the database and Picture Archiving and Communication System (PACS) connections. The front-end module is a web application with a graphical interface that assists the end-user in images’ markup and annotation. Conclusions. An algorithmic basis and a software package have been created for annotation and markup of CT images. The resulting information system was used in a large-scale lung cancer screening project for the creation of medical imaging datasets.

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Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

10.2196/preprints.26228 ◽

2020 ◽

Author(s):

Ying Liu ◽

Ziyan Yu ◽

Shuolan Jing ◽

Honghu Jiang ◽

Chunxia Wang

Keyword(s):

Artificial Intelligence ◽

Medical Imaging ◽

Bibliometric Analysis ◽

Clinical Medicine ◽

Medical Physics ◽

Medical Image Analysis ◽

The United States ◽

Independent Learning ◽

Learning Ability ◽

Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

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Machine Learning in Healthcare Communication

Encyclopedia ◽

10.3390/encyclopedia1010021 ◽

2021 ◽

Vol 1 (1) ◽

pp. 220-239

Author(s):

Sarkar Siddique ◽

James C. L. Chow

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cancer Therapy ◽

Medical Imaging ◽

Health Education ◽

Human Intelligence ◽

Computer Algorithms ◽

Dialogue Management ◽

Topical Review ◽

Healthcare Communication

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

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Deep learning-enabled medical computer vision

npj Digital Medicine ◽

10.1038/s41746-020-00376-2 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Andre Esteva ◽

Katherine Chou ◽

Serena Yeung ◽

Nikhil Naik ◽

Ali Madani ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Medical Imaging ◽

Real World ◽

Recent Progress ◽

Medical Applications ◽

Modern Computer ◽

Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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