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Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report

Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, measur...

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Autores principales: Yeh, Chih-Yang, Lee, Hsun-Hua, Islam, Md. Mohaimenul, Chien, Chiu-Hui, Atique, Suleman, Chan, Lung, Lin, Ming-Chin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776545/
https://www.ncbi.nlm.nih.gov/pubmed/36553056
http://dx.doi.org/10.3390/diagnostics12123047
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author Yeh, Chih-Yang
Lee, Hsun-Hua
Islam, Md. Mohaimenul
Chien, Chiu-Hui
Atique, Suleman
Chan, Lung
Lin, Ming-Chin
author_facet Yeh, Chih-Yang
Lee, Hsun-Hua
Islam, Md. Mohaimenul
Chien, Chiu-Hui
Atique, Suleman
Chan, Lung
Lin, Ming-Chin
author_sort Yeh, Chih-Yang
collection PubMed
description Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, measuring the velocity of blood flow, and then physicians, according to the above information, determining the severity of artery stenosis for generating final ultrasound reports. Generation of transcranial doppler (TCD) and extracranial carotid doppler (ECCD) ultrasound reports involves a lot of manual review processes, which is time-consuming and makes it easy to make errors. Accurate classification of the severity of artery stenosis can provide an early opportunity for decision-making regarding the treatment of artery stenosis. Therefore, machine learning models were developed and validated for classifying artery stenosis severity based on hemodynamic features. This study collected data from all available cases and controlled at one academic teaching hospital in Taiwan between 1 June 2020, and 30 June 2020, from a university teaching hospital and reviewed all patients’ medical records. Supervised machine learning models were developed to classify the severity of artery stenosis. The receiver operating characteristic curve, accuracy, sensitivity, specificity, and positive and negative predictive value were used for model performance evaluation. The performance of the random forest model was better compared to the logistic regression model. For ECCD reports, the accuracy of the random forest model to predict stenosis in various sites was between 0.85 and 1. For TCD reports, the overall accuracy of the random forest model to predict stenosis in various sites was between 0.67 and 0.86. The findings of our study suggest that a machine learning-based model accurately classifies artery stenosis, which indicates that the model has enormous potential to facilitate screening for artery stenosis.
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spelling pubmed-97765452022-12-23 Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report Yeh, Chih-Yang Lee, Hsun-Hua Islam, Md. Mohaimenul Chien, Chiu-Hui Atique, Suleman Chan, Lung Lin, Ming-Chin Diagnostics (Basel) Article Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, measuring the velocity of blood flow, and then physicians, according to the above information, determining the severity of artery stenosis for generating final ultrasound reports. Generation of transcranial doppler (TCD) and extracranial carotid doppler (ECCD) ultrasound reports involves a lot of manual review processes, which is time-consuming and makes it easy to make errors. Accurate classification of the severity of artery stenosis can provide an early opportunity for decision-making regarding the treatment of artery stenosis. Therefore, machine learning models were developed and validated for classifying artery stenosis severity based on hemodynamic features. This study collected data from all available cases and controlled at one academic teaching hospital in Taiwan between 1 June 2020, and 30 June 2020, from a university teaching hospital and reviewed all patients’ medical records. Supervised machine learning models were developed to classify the severity of artery stenosis. The receiver operating characteristic curve, accuracy, sensitivity, specificity, and positive and negative predictive value were used for model performance evaluation. The performance of the random forest model was better compared to the logistic regression model. For ECCD reports, the accuracy of the random forest model to predict stenosis in various sites was between 0.85 and 1. For TCD reports, the overall accuracy of the random forest model to predict stenosis in various sites was between 0.67 and 0.86. The findings of our study suggest that a machine learning-based model accurately classifies artery stenosis, which indicates that the model has enormous potential to facilitate screening for artery stenosis. MDPI 2022-12-05 /pmc/articles/PMC9776545/ /pubmed/36553056 http://dx.doi.org/10.3390/diagnostics12123047 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yeh, Chih-Yang
Lee, Hsun-Hua
Islam, Md. Mohaimenul
Chien, Chiu-Hui
Atique, Suleman
Chan, Lung
Lin, Ming-Chin
Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report
title Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report
title_full Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report
title_fullStr Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report
title_full_unstemmed Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report
title_short Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report
title_sort development and validation of machine learning models to classify artery stenosis for automated generating ultrasound report
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776545/
https://www.ncbi.nlm.nih.gov/pubmed/36553056
http://dx.doi.org/10.3390/diagnostics12123047
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