CARA Lab researches automated segmentation and characterization of intravascular structures and lesions, optimizing OCT-based algorithms to assess physiologic characteristics, high-risk plaque identification, and predicting stent-related complications, with the goal of increasing the usability, reliability, and applicability of intravascular OCT in interventional cardiology through the use of trustworthy AI. The lab is a collaboration between Abbott, Radboud University Medical Center, and Amsterdam University Medical Center.
CARA Lab aims to address two challenges that are common in many real-world AI applications: (1) developing accurate AI models that can analyze high-resolution data streams in real-time and (2) deriving accurate and safe models from these data streams that can make long-term predictions with a limited number of training samples. Our focus is on using optical coherence tomography (OCT) pullbacks, which are high-resolution data streams that are critical for decision-making during cardiac interventions. Our goal is to identify patients at risk for future cardiac events so that preventative treatment and enhanced monitoring can be applied to those individuals. We aim to substantially enhance the usability, reliability, and applicability of intravascular OCT in interventional cardiology through the use of trustworthy AI.
To achieve these goals, CARA Lab is focusing on automated segmentation and characterization of intravascular structures and lesions, optimizing OCT-based algorithms to assess physiologic characteristics, high-risk plaque identification, and predicting stent-related complications. Indicators here are publications about the accuracy and repeatability of the developed algorithms. We consider algorithms accurate when they fall within the interobserver variability of human analysts in core laboratory assessment.
The major assumption is that AI-aided interpretation of OCT images will result in increased use of OCT in revascularization decision-making, which will be tested through the evaluation of patient outcomes and surveys. Another assumption is that the OCT interpretation by a core laboratory used to develop and validate the algorithms is accurate. Initial research showed that there is a good overall correlation between OCT findings and histopathological findings, but there are some limitations that can be addressed through comparison with other imaging modalities such as computed tomography (CT). It is also assumed that treating vulnerable plaques could have a clinical benefit for patients, though this has not yet been formally demonstrated in a clinical trial.
CARA Lab will test the impact of AI-driven OCT on (1) decision-making and (2) trust within the catheterization laboratory for both physicians and patients.
