Cardiologs Holter
- Medical-grade AI
- Cloud-based technology
- Vendor neutrality
10x
Fewer AF false positives while keeping a similar sensitivity1.
Meaning fewer false positives have to be manually deleted1.
Read more42%
Reduction in analysis duration2.
Considerably reduce your editing labor with confidence and focus on the meaningful episodes where your expertise is really needed.
Read more+29%
Sensitivity for VT detection (97% vs 68%, p<0.001)4.
According to a multicenter study comparing the performance of Cardiologs’ deep learning algorithm to a traditional algorithm
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Cardiologs Holter Software-as-a-Service (SaaS) is available from any connected computer and requires no local installation or maintenance. It uses Deep Neural Network (DNN) technology to analyze ECG data and detect more than 20 types of events, including the main arrhythmias. Our offering includes
- Compatibility with ECGs recorded in devices from multiple vendors
- Free platform updates
- Pay per ECG regardless the number of users
- Application Programming Interface (API)
Benefit from expert assistance to free yourself from redundant ECG analysis tasks and tackle inefficiencies.
- Reduce Holter analysis time by about 42% [2]
- Generate easy-to-read reports, automatically enriched with arrhythmia illustrations and metrics
- Work from anywhere
- Use multiple compatible recording devices without additional software
- Collaborate more easily between colleagues, facilities and within care or service networks
Cardiology departments and service providers need innovative solutions to manage staff shortages while coping with the increasing burden of arrhythmias and data.
- By age 40 years, the lifetime risk for AF is ~25%5
- Holter recordings are getting longer, resulting in more data to analyze
- 29% of Cardiologists are burned out6
We need more solutions like Cardiologs, which help us save time, standardize workflow and collaborate.
- Li, J., Rapin, J., Rosier, A., Smith, S.W., Fleureau, Y., Taboulet, P. Deep neural networks improve atrial fibrillation detection in Holter: first results. European Journal of Preventive Cardiology. 2016;23(2uppls):41–55. DOI: 10.1177/2047487316668070.
- Fiorina, L., Marijon, E., Maupain, C., et al. AI-based strategy enables faster Holter ECG analysis with equivalent clinical accuracy compared to a classical strategy. EP Europace. 2020;22(suppl 1), euaa162.374. DOI: 10.1093/europace/ euaa162.374.
- Based on the analysis of 61,000 consecutive recordings in Cardiologs internal database from 12 different service providers located in 4 different countries.
- Multicenter study comparing the performance of Cardiologs’ AI-based algorithm to a traditional algorithm. Fiorina, L., Marijon, E., Maupain, C., et al. Evaluation of an Ambulatory ECG Analysis Platform Using Deep Neural Networks in Routine Clinical Practice. Journal of the American Heart Association. 2022;11(18):e026196. DOI: 10.1161/JAHA.122.026196. Lloyd-Jones, D.M, Wang, T.J, Leip, E.P, Larson, M.G, Levy, D., et al. Lifetime risk for development of atrial fibrillation: the Framingham Heart Study. Circulation. 2004;110(9): 1042–1046. DOI: 10.1161/01.CIR.0000140263.20897.42 Medscape Cardiologist Lifestyle, Happiness & Burnout Report 2023.