research-article

Reliable Probabilistic Prediction of High-Risk Asymptomatic Carotid Plaques

Authors:

Harris Papadopoulos,

Efthyvoulos Kyriacou,

Andrew Nicolaides,

Constantinos PattichisAuthors Info & Claims

EANN '15: Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS)

Article No.: 16, Pages 1 - 9

https://doi.org/10.1145/2797143.2797160

Published: 25 September 2015 Publication History

Abstract

Non-invasive ultrasound imaging of carotid plaques can provide information on the characteristics of the arterial wall including the size and consistency of atherosclerotic plaques. As presented until now this can be used to determine cerebrovascular risk stratification. The aim of this study is to continue the effort being done to estimate the risk based on the characteristics from ultrasound carotid images by applying an approach that produces lower and upper bounds for the conditional probability of the risk for a stroke event. The analysis is based on ultrasonic plaque texture features and clinical features in patients that were followed up for eight years and had asymptomatic internal carotid artery (ICA) stenosis at the baseline. The important addition of this work is that the proposed approach is much more informative than conventional techniques by providing reliable probabilities instead of a plain yes/no answer.

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Cited By

Papadopoulos HKyriacou ENicolaides A(2017)Unbiased confidence measures for stroke risk estimation based on ultrasound carotid image analysisNeural Computing and Applications10.1007/s00521-016-2590-328:6(1209-1223)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s00521-016-2590-3

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Reliable Probabilistic Prediction of High-Risk Asymptomatic Carotid Plaques

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cover image ACM Other conferences

EANN '15: Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS)

September 2015

266 pages

ISBN:9781450335805

DOI:10.1145/2797143

Editors:
Lazaros Iliadis
Democritus University of Thrace, Greece
,
Chrisina Jane
Coventry University, UK

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Aristotle University of Thessaloniki
INNS: International Neural Network Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2015

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16th EANN workshops

16th EANN workshops: 16th Engineering Applications of Neural Networks Conference WORKSHOPS

September 25 - 28, 2015

Island, Rhodes, Greece

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EANN '15 Paper Acceptance Rate 36 of 60 submissions, 60%;

Overall Acceptance Rate 36 of 60 submissions, 60%

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Cited By

Papadopoulos HKyriacou ENicolaides A(2017)Unbiased confidence measures for stroke risk estimation based on ultrasound carotid image analysisNeural Computing and Applications10.1007/s00521-016-2590-328:6(1209-1223)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s00521-016-2590-3

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