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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
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
An observational study to evaluate the clinical performance of an AI-based tool for the presence of obstructive coronary artery disease (CAD) identification
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