Labs
CARA Lab

CARA Lab

A collaboration between Abbott, Radboud University Medical Center, and Amsterdam University Medical Center.

Meibergdreef 9, 1105 AZ Amsterdam

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.

Sustainable Development Goals

Research projects

Automated OCT-assessment. Aims to develop efficient annotation strategies for individual frames and for multi-frame (pullback) analysis.

End-to-end learning for vulnerable plaque features. Aims to develop methods to assess high-risk plaques and to predict plaque rupture or dissection.

Optimizing OCT-derived physiological measures. Aims to develop algorithms to indicate physiologic coronary artery features and stent-related hemodynamic changes to original OCT-pullback.

Prediction of stent failure. Aims to develop algorithms to identify OCT-based predictors of stent failure and predict risk of stent failure.

AI-driven OCT guidance in coronary revascularization. Aims to investigate ethical issues of AI-driven revascularization strategies.

People

Niels van Royen
Ivana Išgum
Bram van Ginneken
Clarisa Sánchez Gutiérrez
Jos Thannhauser

PHD Students

Niels van Royen
Ivana Išgum
Bram van Ginneken
Clarisa Sánchez Gutiérrez
Jos Thannhauser

Partners

Abbott, the Radboud University Medical Center, and the Amsterdam University Medical Center, will all be responsible for obtaining sufficient data for the development and validation of the intended algorithms. Development, testing, and validation will mostly be performed by members of the radiology and cardiology departments of the Radboud University Medical Center and Amsterdam University Medical Center. Abbott will have the leading role in implementing the final product within a readily available OCT system. To enable this, Abbott will also be involved in the development phase by means of half-yearly meetings and all researchers will spend time at Abbott R&D in Norwood, Massachusetts, e.g. via internships.

Partners:

  • Abbott

  • Radboud University Medical Center

  • Amsterdam University Medical Center
Abbott
Radboud UMC
Amsterdam University Medical Centers (AUMC)
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