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Automated Prediction of Hepatic Arterial Stenosis
Several thousand life-saving liver transplants are performed each year. One of the most common causes of early transplant failure is arterial stenosis of the anastomotic junction. Early detection of transplant arterial stenosis can help prevent transplant failure and the need to re-transplant. Doppl...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543337/ https://www.ncbi.nlm.nih.gov/pubmed/28815106 |
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author | Baraboo, Justin J. Dinakarpandian, Deendayal Chan, Sherwin S. |
author_facet | Baraboo, Justin J. Dinakarpandian, Deendayal Chan, Sherwin S. |
author_sort | Baraboo, Justin J. |
collection | PubMed |
description | Several thousand life-saving liver transplants are performed each year. One of the most common causes of early transplant failure is arterial stenosis of the anastomotic junction. Early detection of transplant arterial stenosis can help prevent transplant failure and the need to re-transplant. Doppler ultrasound is the most common screening method, but it suffers from poor specificity. Positive screening cases proceed to angiography which is an invasive and expensive procedure. A more accurate test could decrease the number of normal patients who would have to undergo this invasive diagnostic procedure. We present a turnkey clinical decision support tool for automated prediction of stenosis based on Fourier spectrum analysis of Doppler sonograms to compute a Stenosis Index that has been shown to have higher accuracy than traditional measures. The results of the automated approach compare favorably with the manual approach. Software is available from the authors on request. |
format | Online Article Text |
id | pubmed-5543337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | American Medical Informatics Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-55433372017-08-16 Automated Prediction of Hepatic Arterial Stenosis Baraboo, Justin J. Dinakarpandian, Deendayal Chan, Sherwin S. AMIA Jt Summits Transl Sci Proc Articles Several thousand life-saving liver transplants are performed each year. One of the most common causes of early transplant failure is arterial stenosis of the anastomotic junction. Early detection of transplant arterial stenosis can help prevent transplant failure and the need to re-transplant. Doppler ultrasound is the most common screening method, but it suffers from poor specificity. Positive screening cases proceed to angiography which is an invasive and expensive procedure. A more accurate test could decrease the number of normal patients who would have to undergo this invasive diagnostic procedure. We present a turnkey clinical decision support tool for automated prediction of stenosis based on Fourier spectrum analysis of Doppler sonograms to compute a Stenosis Index that has been shown to have higher accuracy than traditional measures. The results of the automated approach compare favorably with the manual approach. Software is available from the authors on request. American Medical Informatics Association 2017-07-26 /pmc/articles/PMC5543337/ /pubmed/28815106 Text en ©2017 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose |
spellingShingle | Articles Baraboo, Justin J. Dinakarpandian, Deendayal Chan, Sherwin S. Automated Prediction of Hepatic Arterial Stenosis |
title | Automated Prediction of Hepatic Arterial Stenosis |
title_full | Automated Prediction of Hepatic Arterial Stenosis |
title_fullStr | Automated Prediction of Hepatic Arterial Stenosis |
title_full_unstemmed | Automated Prediction of Hepatic Arterial Stenosis |
title_short | Automated Prediction of Hepatic Arterial Stenosis |
title_sort | automated prediction of hepatic arterial stenosis |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543337/ https://www.ncbi.nlm.nih.gov/pubmed/28815106 |
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