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ONLINE MEDICAL INFORMATION SYSTEM TO CREATE A DECISION-MAKING EXPERT SYSTEM FOR RISK ASSESSMENT OF ATHEROSCLEROTIC PLAQUES FROM B-IMAGES AND HISTOLOGICAL PATTERNS

Jiří Blahuta

First published: 2019-06-20https://doi.org/10.5593/sgem2019/2.1/s07.066View metrics

Abstract

We have developed an online information system of digitized B-images and histological patterns acquired from more than 500 patients. In this system, the data are stored in the form of text and image information. For each patient, there is stored information about anamnesis and diagnosis with B-images and histological patterns of atherosclerotic plaques. Firstly, the database is primarily intended for an experienced neurologist to store clinical data for various clinical studies in neurology. In the second phase, the data are a starting point for creation of a decision-making expert system focused on risk assessment of atherosclerotic plaques. This paper describes the front-end and the back-end of the application and API developed for uploading new images. The system is online, modular and runs in all modern web browsers with no extra plugin needed. Using modern web-oriented technologies such as HTML5 and responsive design of the front-end is an indisputable benefit of the application as well. The GUI of the front-end is in Czech language; the translation into English is planned within future development. The goal of the application is to create a sophisticated system for storing and sharing clinical diagnostic information; easy to use with simple GUI but able to work with large datasets containing text and image data across neurological departments not only in the Czech Republic. The data will be used for many clinical studies focused on different results. Finally, the system is a demonstrative example of using modern web-based application in contemporary medicine.

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Publication details

Title
ONLINE MEDICAL INFORMATION SYSTEM TO CREATE A DECISION-MAKING EXPERT SYSTEM FOR RISK ASSESSMENT OF ATHEROSCLEROTIC PLAQUES FROM B-IMAGES AND HISTOLOGICAL PATTERNS
Authors
Jiří Blahuta
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th International Multidisciplinary Scientific GeoConference SGEM2019, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2019
Pages
501-508
SWS Citekey
Blahuta20197501508
ISSN
1314-2704
ISBN
978-619-7408-79-9
Language
en
Publication type
Conference Paper
Keywords
References9
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  2. Blahuta, J., Cermak, P., Soukup, T. How to Detect and Analyze Atherosclerotic Plaques in B-MODE Ultrasound Images: A Pilot Study of Reproducibility of Computer Analysis. Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference AIMSA 2016, Varna, pp 360-363, 2016.

  3. Blahuta, J., Soukup, T., Cermak, P. An Expert System Based on Using Artificial Neural Network and Region-Based Image Processing to Recognition Substantia Nigra and Atherosclerotic Plaques in B-Images: A Prospective Study. 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Cadiz, Spain, June 14-16, 2017, Proceedings, Part I. Lecture Notes in Computer Science 10305, Springer 2017, pp 236-245, 2017.

  4. Blahuta, J., Soukup, T., Skacel, J. Pilot Design of a Rule-Based System and an Artificial Neural Network to Risk Evaluation of Atherosclerotic Plaques in Long-Range Clinical Research. ICANN 2018, Lecture Notes in Computer Science book series (LNCS, volume 11140), Springer, 2018, pp. 90-100, ISSN: 978-3-030-01420-9.

  5. Blahuta, J., Cermak, P., Soukup, T., Vecerek, M. A reproducible method to transcranial B-MODE ultrasound images analysis based on echogenicity evaluation in selectable ROI. (2014) International Journal of Biology and Biomedical Engineering, Vol. 8, pp 98-106, 2014. ISSN: 19984510.

  6. Skoloudik, D., Bartova, P., Maskova P., Dusek, P., Blahuta, J., Langova, K., Walter, U., Herzig, R. Transcranial Sonography of the Insula: Digitized Image Analysis of Fusion Images with Magnetic Resonance. Ultraschall in der Medizin, Georg Thieme Verlag KG Stuttgart (2016).

  7. Horie, J., Fujita, H., Numao, A., Watanabe, Y., Matsubara, T., Uchiyama, Miyamoto, T., Miyamoto, M., Kadowaki, T., Hirata, K. A follow-up study of hyperechogenicity of the substantia nigra in Parkinson’s disease and atypical parkinsonian syndromes. Mov Disord. 2017; 32 (suppl 2), Wiley, 2017.

  8. Nixon, R. Learning PHP, MySQL & JavaScript: With jQuery, CSS & HTML5, 5th Edition, O'Reilly Media, 2018, ISBN-13: 978-1491978917.

  9. Carreras, A., I. Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification. Bioinformatics, Volume 33, Issue 15, 01 August 2017, pp 2424–2426, 2017.

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