An ongoing clinical research program validating Cardio Explorer®.
ISO 13485
ISO 27001
Cardio Explorer® was developed, refined, and validated through retrospective diagnostic accuracy studies covering algorithm development, model lock, and intended-use validation. A prospective implementation study is now evaluating clinical adoption, usability, and impact on patient management in routine care.
Initial algorithm development and internal validation
First feasibility cohort: machine-learning-based pattern recognition trained on patients referred for invasive coronary angiography.
Algorithm refinement and lock in a high-risk population
Locked model evaluated on independent validation sets within a high-cardiovascular-risk cohort.
Pivotal evaluation in the intended-use population
Validated in the intended-use population: low-to-intermediate-risk outpatients referred for evaluation of coronary artery disease.
Prospective implementation in primary care
A multicenter controlled study evaluating the acceptance, usability, and impact of Cardio Explorer® on diagnostic pathways in general practice.
Validation of the Algorithm for Predicting Ischemia
A prospective, single-center cohort study validating the memetic pattern-based algorithm (MPA) against rubidium-82 PET myocardial perfusion imaging.
Diagnostic performance was evaluated on the independent validation subsets of Basel and LURIC and on the full Maastricht cohort. A higher AUC indicates better discrimination between patients with and without obstructive CAD.
Organized by evidence type to demonstrate the breadth and maturity of the science behind the Memetic Pattern Algorithm and the Cardio Explorer® platform: from the original 2014 methodology paper to current ESC-level comparative evaluations. Within each category, sorted from newest to oldest.
Independent and collaborative research published in peer-reviewed journals.
Despite extensive guideline coverage and broad access to cardiovascular diagnostics, Germany continues to experience substantial rates of missed and late-diagnosed coronary artery disease—the so-called German paradox. This article reviews the structural factors driving this gap and introduces Cardio Explorer® as a clinically deployable AI decision-support tool that combines clinical, laboratory, and demographic variables into a single probabilistic estimate of obstructive CAD. The paper details the architecture of the Memetic Pattern Algorithm, summarizes validation across the Basel, LURIC, and Maastricht cohorts, and discusses its integration into outpatient cardiology care pathways.
This peer-reviewed study evaluates whether AI-guided pretest probability assessment using Cardio Explorer® adds incremental diagnostic value to rubidium-82 PET/CT myocardial perfusion imaging in patients with suspected coronary artery disease. Integrating the Cardio Explorer® probability estimate improved the prediction of hemodynamically relevant ischemia compared to imaging or clinical scoring alone and reduced equivocal interpretations, supporting a combined clinical-AI plus imaging workflow within the framework of Predictive, Preventive, and Personalized Medicine (PPPM).
Objective: To evaluate the diagnostic performance of an artificial intelligence (AI) model—Cardio Explorer®—in assessing the probability of obstructive coronary artery disease (CAD) in a low- to intermediate-risk outpatient population, using only clinical and laboratory variables already available at the point of care. Design: Retrospective diagnostic accuracy study using prospectively collected medical records from an outpatient cardiology clinic at Maastricht University Medical Center (MUMC+). The reference standard combined invasive coronary angiography, coronary CT angiography, and ≥3 years of event-free follow-up. Results: The locked algorithm achieved an AUC of 0.878 with high sensitivity and a strong negative predictive value, supporting its use as a non-invasive rule-out tool in the intended-use population.
This paper situates Cardio Explorer® within the framework of Predictive, Preventive, and Personalized Medicine (PPPM). It describes how a memetic pattern algorithm—trained and validated across independent European cohorts—can support individualized CAD risk assessment without exposing patients to additional radiation, contrast, or stress testing. The article details the inputs, outputs, intended clinical use, and regulatory status of the tool, and outlines how AI-based pre-test probability can be integrated into shared decision-making for subsequent imaging or invasive evaluation.
This foundational paper introduces a memetic pattern-based algorithm (MPA) for the noninvasive diagnosis and exclusion of coronary artery disease. Using clinical, demographic, and laboratory data, the algorithm reconstructs patient-specific risk patterns and compares them against a reference library of phenotypes with known coronary status. An initial evaluation in a high-prevalence cohort in Basel referred for invasive coronary angiography demonstrated the feasibility of using machine-learned pattern recognition as a triage step prior to invasive testing, establishing the methodological foundation for the Cardio Explorer® platform.
Presentations and abstracts from international cardiology conferences.
This ESC AI Congress abstract compares the third-generation Memetic Pattern Algorithm (MPA v3)—the engine behind Cardio Explorer®—to the ESC 2024 RF-CL (Risk Factor-weighted Clinical Likelihood) algorithm for estimating obstructive coronary artery disease. Using a contemporary outpatient cohort, the analysis evaluates discrimination, calibration, and net reclassification across pre-test probability strata, with a particular focus on the low-to-intermediate risk range, where guideline pathways most often yield ambiguous recommendations. MPA v3 demonstrates improved discrimination and a more favorable rule-out profile than RF-CL while maintaining sensitivity, supporting its role as a non-invasive triage tool prior to advanced imaging.
Registered prospective studies evaluating Cardio Explorer® in clinical practice.
A registered clinical study describing the prospective implementation of the Cardio Explorer® AI algorithm in routine outpatient cardiology practice. The protocol evaluates real-world diagnostic performance, downstream resource utilization, and the impact on clinical decision-making compared with standard guideline-based pathways.
