Voice-Enabled Intelligent Decision Support System for Phlebology with 3D Vein Reconstruction

Abstract

we present an intelligent decision support system for phlebology that addresses subjectivity and delays in diagnosing chronic venous disease (CVD) of the lower extremities. We implemented the system in Python and integrated three modules. First, we used convolutional neural networks to classify images and determine the CEAP class. Second, we developed a voice interface based on the SpeechRecognition and pyttsx3 libraries to support structured dictation of examination reports and to generate clinical recommendations. Third, we reconstructed the venous system in 3D from axial MRI slices and performed a quantitative assessment of the cross-sectional area of the inferior vena cava (IVC). The system improves diagnostic accuracy, reduces report preparation time from 15 to 7 minutes, and enables documentation without manual entry. We tested all components on clinical data, and the system meets the requirements of modern non-invasive phlebology.