Idoven has developed an AI-powered cardiology platform that utilizes deep neural networks to analyze ECG data, enabling rapid and accurate identification of arrhythmias and cardiac patterns. This technology significantly reduces the time and costs associated with ECG interpretation, facilitating efficient triage and diagnosis for healthcare providers.
Funding
$23.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional ECG interpretation can be time-consuming and costly, potentially delaying triage and diagnosis of critical cardiac conditions. Manual analysis may also lead to variability in accuracy, impacting patient outcomes.
Solution
Idoven offers a cardiology-as-a-service platform that leverages AI to enhance the speed and accuracy of ECG analysis. The platform utilizes deep neural networks trained on a large, diverse ECG database to identify arrhythmias and other cardiac patterns. This AI-powered analysis augments clinician capabilities, facilitating efficient patient triage, early disease detection, and personalized treatment strategies. Idoven's Willem ECG Analysis Platform is CE-marked as a Medical Device Class IIa, capable of detecting 22 cardiac patterns and 4 intervals across various ECG recording devices. The platform is device-neutral and interoperable, ingesting data from standard and proprietary formats via API and non-API tools.
Target Audience
Idoven targets healthcare providers, including cardiologists and general practitioners, MedTech companies, and Life Sciences organizations seeking to improve cardiac diagnostics, risk prediction, and patient monitoring.
Features
- AI-powered detection of 22 cardiac patterns and 4 intervals from ECG data
- Cloud-based infrastructure compliant with ISO 27001, 27017, 27018, 27701, and GDPR
- Device neutrality, compatible with ambulatory ECGs, standard resting ECGs, and insertable cardiac monitors
- Integration with Home Monitoring and Electronic Health Record (EHR) systems
- Digital biomarker development for prediction, cardiotoxicity assessment, and risk stratification
- Remote patient monitoring and digital screening capabilities
- Continuous ECG monitoring from non-invasive devices with medical-grade reports
- Analysis of ECGs ranging from 30 seconds to 30 days in duration
- AI models trained on over 1.25 million hours of manually annotated ECG data