Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
Keywords:Diagnosis, Artificial neural networks, Deep venous thrombosis.
AbstractDeep Venous Thrombosis (DVT) is a manifestation of a Thromboembolic Disease (ET). When in a DVT the venous thrombus detaches and travel through the bloodstream can cause a Pulmonary Embolism Thrombus (PET). The existence of Deep Venous Thrombosis (DVT) in the lower extremities has been described as one of the main risk factors for the development of PET. It is considered that up to 90% of pulmonary emboli come from venous thrombi of the lower extremities. The most commonly used techniques for the detection of DVT are clinical probability models, D-dimer and non-invasive imaging tests, such as ultrasound for DVT and computed angiotomography (CT) for pulmonary embolism. However, due to the non-specificity of the symptoms of DVT, the threshold for ordering an ultrasound is low, in addition to being a complicated process that requires the participation of a specialist doctor for its interpretation. In recent decades, machine learning has emerged as support in decision-making for the diagnosis of various diseases, some of the most used technologies in the field of medicine include Support Vector Machine (SVM), Decision Trees and Neural Networks Artificial (RNA). This article reviews the existing technologies for the detection of DVT as well as the main machine learning algorithms commonly used in biomedical applications; The design of a computerized system that uses machine learning techniques as a support tool for the timely detection of a possible DVT is proposed.
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Copyright (c) 2020 Fong-Mata María Berenice, Inzunza-González Everardo, García-Guerrero Enrique Efrén, Mejía Medina David Abdel, Morales Contreras Oscar Adrián, Gómez-Roa Antonio
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