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Comput Methods Programs Biomed. 2014 Apr;114(2):183-93. doi: 10.1016/j.cmpb.2014.02.006. Epub 2014 Feb 24.

CAROTID - a web-based platform for optimal personalized management of atherosclerotic patients.

Author information

1
School of Electrical and Computer Engineering, National Technical University of Athens, Greece.
2
First Intensive Care Unit, Medical School, National Kapodistrian University of Athens, Greece. Electronic address: sgolemati@med.uoa.gr.
3
Department of Vascular Surgery, Attikon University General Hospital, National Kapodistrian University of Athens, Athens, Greece.
4
Department of Cytopathology, Attikon University General Hospital, National Kapodistrian University of Athens, Athens, Greece.
5
Vidavo SA, Macedonia, Greece.
6
Vidavo SA, Macedonia, Greece; School of Informatics and Telecommunication Engineering, University of Western Macedonia, Greece.

Abstract

Carotid atherosclerosis is the main cause of fatal cerebral ischemic events, thereby posing a major burden for public health and state economies. We propose a web-based platform named CAROTID to address the need for optimal management of patients with carotid atherosclerosis in a twofold sense: (a) objective selection of patients who need carotid-revascularization (i.e., high-risk patients), using a multifaceted description of the disease consisting of ultrasound imaging, biochemical and clinical markers, and (b) effective storage and retrieval of patient data to facilitate frequent follow-ups and direct comparisons with related cases. These two services are achieved by two interconnected modules, namely the computer-aided diagnosis (CAD) tool and the intelligent archival system, in a unified, remotely accessible system. We present the design of the platform and we describe three main usage scenarios to demonstrate the CAROTID utilization in clinical practice. Additionally, the platform was evaluated in a real clinical environment in terms of CAD performance, end-user satisfaction and time spent on different functionalities. CAROTID classification of high- and low-risk cases was 87%; the corresponding stenosis-degree-based classification would have been 61%. Questionnaire-based user satisfaction showed encouraging results in terms of ease-of-use, clinical usefulness and patient data protection. Times for different CAROTID functionalities were generally short; as an example, the time spent for generating the diagnostic decision was 5min in case of 4-s ultrasound video. Large datasets and future evaluation sessions in multiple medical institutions are still necessary to reveal with confidence the full potential of the platform.

KEYWORDS:

Biomarkers; Carotid atherosclerosis; Computer-aided diagnosis; Data management; Image analysis; Web-based platform

PMID:
24636805
DOI:
10.1016/j.cmpb.2014.02.006
[Indexed for MEDLINE]

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