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Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management.
Butova, Xenia; Shayakhmetov, Sergey; Fedin, Maxim; Zolotukhin, Igor; Gianesini, Sergio.
  • Butova X; Department of Fundamental and Applied Research in Surgery, Pirogov Russian National Research Medical University, 117997 Moscow, Russia.
  • Shayakhmetov S; Department of Radiotechnics, Faculty of Technical Cybernetics, National Research University of Electronic Technology, 124498 Moscow, Russia.
  • Fedin M; Department of Data Science, Faculty of Information Technology, Monash University, Melbourne 3800, Australia.
  • Zolotukhin I; Department of Fundamental and Applied Research in Surgery, Pirogov Russian National Research Medical University, 117997 Moscow, Russia.
  • Gianesini S; Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy.
J Pers Med ; 11(12)2021 Dec 02.
Article in English | MEDLINE | ID: covidwho-1592007
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Reviews Language: English Year: 2021 Document Type: Article Affiliation country: Jpm11121280

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Reviews Language: English Year: 2021 Document Type: Article Affiliation country: Jpm11121280