P54 A MACHINE LEARNING SYSTEM FOR CAROTID PLAQUE VULNERABILITY ASSESSMENT BASED ON ULTRASOUND IMAGES

DOI

10.1016/j.artres.2018.10.107How to use a DOI?

Abstract

Purpose/Background/Objectives: Carotid plaque vulnerability assessment is essential for the identification of high-risk patients. A specific mouse model for the study of carotid atherosclerosis has been recently developed. Aim of this study was to develop a predictive mathematical model for carotid plaque vulnerability assessment based on the post processing of micro-Ultrasound (μUS) images only.

Methods: 17 ApoE-/- male mice (16 weeks) were employed. After three weeks of high-fat diet, a tapered cast, designed to induce the formation of an unstable plaque upstream from the cast and a stable one downstream from it, was surgically placed around the right common carotid. μUS examination was repeated before the surgical procedure and after three months from it. Color-Doppler, B-mode and Pulsed-wave Doppler images were acquired to assess morphological, functional and hemodynamic parameters. In particular, texture analysis was applied on both the atherosclerotic lesions post-processing B-mode images. Peak velocity (Vp), Relative Turbolence Intensity (rTI) and velocity range (rangevel) were assessed from PW-Doppler images. Relative Distension (relD) and Pulse Wave Velocity (PWV) were evaluated for both the regions. All the μUS indexes underwent a feature reduction process and were used to train different machine learning approaches.

Results: The downstream region presented higher PWV values than the upstream one; furthermore, it was characterized by higher values of rTI and rangevel. The weighted kNN classifier supplied the best providing 92.6% accuracy, 91% sensitivity and 94% specificity.

Conclusions: The mathematical predictive model could represent a valid approach to be translated in the clinical field and easily employed in clinical practice.

Open Access

This is an open access article distributed under the CC BY-NC license.

TY  - JOUR
AU  - Nicole Di Lascio
AU  - Claudia Kusmic
AU  - Anna Solini
AU  - Vincenzo Lionetti
AU  - Francesco Faita
PY  - 2018
DA  - 2018/12/04
TI  - P54 A MACHINE LEARNING SYSTEM FOR CAROTID PLAQUE VULNERABILITY ASSESSMENT BASED ON ULTRASOUND IMAGES
JO  - Artery Research
SP  - 94
EP  - 94
VL  - 24
IS  - C
SN  - 1876-4401
UR  - https://doi.org/10.1016/j.artres.2018.10.107
DO  - 10.1016/j.artres.2018.10.107
ID  - DiLascio2018
ER  -