Pilot design of a rule-based system and an artificial neural network to risk evaluation of atherosclerotic plaques in long-range clinical research

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F18%3AA0000220" target="_blank" >RIV/47813059:19240/18:A0000220 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-01421-6_9" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-01421-6_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-01421-6_9" target="_blank" >10.1007/978-3-030-01421-6_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pilot design of a rule-based system and an artificial neural network to risk evaluation of atherosclerotic plaques in long-range clinical research

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Pilot design of a rule-based system and an artificial neural network to risk evaluation of atherosclerotic plaques in long-range clinical research

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science

ISBN

9783030014216

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

11

Strana od-do

90-100

Název nakladatele

Springer Verlag

Místo vydání

Cham

Místo konání akce

Rhodes; Greece

Datum konání akce

4. 10. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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