Abstract
Early diagnostics and knowledge of the progress of atherosclerotic plaques are key parameters which can help start the most efficient treatment. Reliable prediction of growing of atherosclerotic plaques could be very important part of early diagnostics to judge potential impact of the plaque and to decide necessity of immediate artery recanalization. For this pilot study we have a large set of measured data from total of 482 patients. For each patient the width of the plaque from left and right side during at least 5 years at regular intervals for 6 months was measured Patients were examined each 6 months and width of the plaque was measured using ultrasound B-image and the data were stored into a database. The first part is focused on rule-based expert system designed for evaluation of suggestion to immediate recanalization according to progress of the plaque. These results will be verified by an experienced sonographer. This system could be a starting point to design an artificial neural network with adaptive learning based on image processing of ultrasound B-images for classification of the plaques using feature analysis. The principle of the network is based on edge detection analysis of the plaques using feed-forwarded network with Error Back-Propagation algorithm. Training and learning of the ANN will be time-consuming processes for a long-term research. The goal is to create ANN which can recognize the border of the plaques and to measure of the width. The expert system and ANN are two different approaches, however, both of them can cooperate.
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This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project IT4Innovations excellence in science - LQ1602.
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Blahuta, J., Soukup, T., Skacel, J. (2018). Pilot Design of a Rule-Based System and an Artificial Neural Network to Risk Evaluation of Atherosclerotic Plaques in Long-Range Clinical Research. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_9
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