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Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation

Duplex ultrasound (DUS) is the most widely used method for surveillance of arteriovenous fistulae (AVF) created for dialysis. However, DUS is poor at predicting AVF outcomes and there is a need for novel methods that can more accurately evaluate multidirectional AVF flow. In this study we aimed to e...

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Autores principales: Poushpas, Sepideh, Normahani, Pasha, Kisil, Ilya, Szubert, Ben, Mandic, Danilo P., Jaffer, Usman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368269/
https://www.ncbi.nlm.nih.gov/pubmed/37490491
http://dx.doi.org/10.1371/journal.pone.0286952
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author Poushpas, Sepideh
Normahani, Pasha
Kisil, Ilya
Szubert, Ben
Mandic, Danilo P.
Jaffer, Usman
author_facet Poushpas, Sepideh
Normahani, Pasha
Kisil, Ilya
Szubert, Ben
Mandic, Danilo P.
Jaffer, Usman
author_sort Poushpas, Sepideh
collection PubMed
description Duplex ultrasound (DUS) is the most widely used method for surveillance of arteriovenous fistulae (AVF) created for dialysis. However, DUS is poor at predicting AVF outcomes and there is a need for novel methods that can more accurately evaluate multidirectional AVF flow. In this study we aimed to evaluate the feasibility of detecting AVF stenosis using a novel method combining tensor-decomposition of B-mode ultrasound cine loops (videos) of blood flow and machine learning classification. Classification of stenosis was based on the DUS assessment of blood flow volume, vessel diameter size, flow velocity, and spectral waveform features. Real-time B-mode cine loops of the arterial inflow, anastomosis, and venous outflow of the AVFs were analysed. Tensor decompositions were computed from both the ‘full-frame’ (whole-image) videos and ‘cropped’ videos (to include areas of blood flow only). The resulting output were labelled for the presence of stenosis, as per the DUS findings, and used as a set of features for classification using a Long Short-Term Memory (LSTM) neural network. A total of 61 out of 66 available videos were used for analysis. The whole-image classifier failed to beat random guessing, achieving a mean area under the receiver operating characteristics (AUROC) value of 0.49 (CI 0.48 to 0.50). In contrast, the ‘cropped’ video classifier performed better with a mean AUROC of 0.82 (CI 0.66 to 0.96), showing promising predictive power despite the small size of the dataset. The combined application of tensor decomposition and machine learning are promising for the detection of AVF stenosis and warrant further investigation.
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spelling pubmed-103682692023-07-26 Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation Poushpas, Sepideh Normahani, Pasha Kisil, Ilya Szubert, Ben Mandic, Danilo P. Jaffer, Usman PLoS One Research Article Duplex ultrasound (DUS) is the most widely used method for surveillance of arteriovenous fistulae (AVF) created for dialysis. However, DUS is poor at predicting AVF outcomes and there is a need for novel methods that can more accurately evaluate multidirectional AVF flow. In this study we aimed to evaluate the feasibility of detecting AVF stenosis using a novel method combining tensor-decomposition of B-mode ultrasound cine loops (videos) of blood flow and machine learning classification. Classification of stenosis was based on the DUS assessment of blood flow volume, vessel diameter size, flow velocity, and spectral waveform features. Real-time B-mode cine loops of the arterial inflow, anastomosis, and venous outflow of the AVFs were analysed. Tensor decompositions were computed from both the ‘full-frame’ (whole-image) videos and ‘cropped’ videos (to include areas of blood flow only). The resulting output were labelled for the presence of stenosis, as per the DUS findings, and used as a set of features for classification using a Long Short-Term Memory (LSTM) neural network. A total of 61 out of 66 available videos were used for analysis. The whole-image classifier failed to beat random guessing, achieving a mean area under the receiver operating characteristics (AUROC) value of 0.49 (CI 0.48 to 0.50). In contrast, the ‘cropped’ video classifier performed better with a mean AUROC of 0.82 (CI 0.66 to 0.96), showing promising predictive power despite the small size of the dataset. The combined application of tensor decomposition and machine learning are promising for the detection of AVF stenosis and warrant further investigation. Public Library of Science 2023-07-25 /pmc/articles/PMC10368269/ /pubmed/37490491 http://dx.doi.org/10.1371/journal.pone.0286952 Text en © 2023 Poushpas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Poushpas, Sepideh
Normahani, Pasha
Kisil, Ilya
Szubert, Ben
Mandic, Danilo P.
Jaffer, Usman
Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation
title Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation
title_full Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation
title_fullStr Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation
title_full_unstemmed Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation
title_short Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation
title_sort tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: an initial evaluation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368269/
https://www.ncbi.nlm.nih.gov/pubmed/37490491
http://dx.doi.org/10.1371/journal.pone.0286952
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