Artery Research

Volume 24, Issue C, December 2018, Pages 94 - 94

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

Authors
Nicole Di Lascio1, 2, Claudia Kusmic1, Anna Solini3, Vincenzo Lionetti2, Francesco Faita1
1Institute of Clinical Physiology, CNR, Pisa, Italy
2Institute of Life Sciences, Scuola Superiore Sant’Anna, Pisa, Italy
3Department of Surgical, Medical, Molecular, and Critical Area Pathology, University of Pisa, Pisa, Italy
Available Online 4 December 2018.
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.

Journal
Artery Research
Volume-Issue
24 - C
Pages
94 - 94
Publication Date
2018/12/04
ISSN (Online)
1876-4401
ISSN (Print)
1872-9312
DOI
10.1016/j.artres.2018.10.107How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  -