Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.
Who will be affected?
Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central
Authors: No access to the submission form or your user account.
Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Wednesday, July 01, 2020 at 7:00 PM.
Editors: No access to your user account to assign reviewers or make decisions.
Copyeditors: No access to user account. Please download manuscripts you are copyediting before Wednesday, July 01, 2020 at 7:00 PM.
Rampidis G, Logaras E, Samaras A, Rigas ES, Kyparissidis-Kokkinidis I, Siakopoulou S, Kartsidis PE, Kouskouras K, Giannakoulas G, Bamidis P, Billis A
Clinical Performance Evaluation of an Artificial Intelligence–Based Tool for Predicting the Presence of Obstructive Coronary Artery Disease: Protocol for a Cohort Observational Study
Clinical performance evaluation of an AI-based tool for the presence of obstructive coronary artery disease (CAD) identification: Protocol for a Cohort Observational Study
Georgios Rampidis;
Evangelos Logaras;
Athanasios Samaras;
Emmanouil S. Rigas;
Ilias Kyparissidis-Kokkinidis;
Styliana Siakopoulou;
Panagiotis-Emmanouil Kartsidis;
Konstantinos Kouskouras;
Georgios Giannakoulas;
Panagiotis Bamidis;
Antonios Billis
ABSTRACT
Background:
A significant number of individuals undergoing Coronary Computed Tomography Angiography (CCTA) for suspected Coronary Artery Disease (CAD) have non-obstructive or no CAD. There is a need for clinically proven models that can predict the pre-test probability (PTP) of stable CAD and help to identify low-risk individuals. Optimizing patient stratification is of paramount importance to improve diagnostic yield and cost-effectiveness.
Objective:
A study is being carried out to determine whether or not each patient needs to undergo CCTA because of suspected CAD. The main objective of the study is to evaluate the clinical performance of an AI-based tool, as well as its utility by medical professionals.
Methods:
Data has been acquired from 750 participants as part of routine clinical practice in AHEPA General Hospital of Thessaloniki. At least two expert cardiologists and two expert radiologists are involved in the study, who provide the ground truth. A trained AI based model embedded in an easy-to-use and user-friendly web application is implemented in practice.
Results:
Recruitment for the study began in July 2023. Data collection, development, training, deployment of the AI web tool were completed by May 2024, while the analysis of the data is ongoing.
Conclusions:
The proposed study represents a novel approach of a web-based AI-driven solution for optimizing patient stratification with the goal of improving diagnostic yield and cost-effectiveness of CCTA utilization within the context of cardiology clinical practice.
Citation
Please cite as:
Rampidis G, Logaras E, Samaras A, Rigas ES, Kyparissidis-Kokkinidis I, Siakopoulou S, Kartsidis PE, Kouskouras K, Giannakoulas G, Bamidis P, Billis A
Clinical Performance Evaluation of an Artificial Intelligence–Based Tool for Predicting the Presence of Obstructive Coronary Artery Disease: Protocol for a Cohort Observational Study