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Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management
Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8705683/ https://www.ncbi.nlm.nih.gov/pubmed/34945749 http://dx.doi.org/10.3390/jpm11121280 |
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author | Butova, Xenia Shayakhmetov, Sergey Fedin, Maxim Zolotukhin, Igor Gianesini, Sergio |
author_facet | Butova, Xenia Shayakhmetov, Sergey Fedin, Maxim Zolotukhin, Igor Gianesini, Sergio |
author_sort | Butova, Xenia |
collection | PubMed |
description | Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care. |
format | Online Article Text |
id | pubmed-8705683 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87056832021-12-25 Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management Butova, Xenia Shayakhmetov, Sergey Fedin, Maxim Zolotukhin, Igor Gianesini, Sergio J Pers Med Review Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care. MDPI 2021-12-02 /pmc/articles/PMC8705683/ /pubmed/34945749 http://dx.doi.org/10.3390/jpm11121280 Text en © 2021 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 | Review Butova, Xenia Shayakhmetov, Sergey Fedin, Maxim Zolotukhin, Igor Gianesini, Sergio Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management |
title | Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management |
title_full | Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management |
title_fullStr | Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management |
title_full_unstemmed | Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management |
title_short | Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management |
title_sort | artificial intelligence evidence-based current status and potential for lower limb vascular management |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8705683/ https://www.ncbi.nlm.nih.gov/pubmed/34945749 http://dx.doi.org/10.3390/jpm11121280 |
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