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The Volta Data Collection & Enrichment Process
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Volta Medical Data Collection and Enrichment ecosystem seamlessly captures EP lab rich data via collaboration with other data inspired centers. This allows Volta’s algorithms to continue to evolve and to feed a pipeline for other complex arrhythmia solutions.
Volta Data Collection Process for Electrophysiology Workflows
AI CHALLENGES
Data Privacy and Security
Medical data is sensitive and requires the implementation of adequate technical and organizational measures to ensure its security.
Compliance with data protection regulations (GDPR/HIPAA) is paramount.
Data Quantity and Quality
Machine learning and deep-learning models require medical datasets with sufficient size and accuracy. Data quality is hard to define and project dependent.
Data Collection
Peri-procedural data comes from multiple sources. Lack of standardization in how medical data is stored and formatted.
Data Preparation
Annotation, curation, and validation of medical data requires a high-level of expertise.
Bias in Data Sources
Representativeness of the data used to train the models is critical to avoid bias.
Clinical Implementation
To ensure adoption, involvement of the different stakeholders from the development phase is critical and clinical evidence is key.
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OUR APPROACH
Patient Privacy
All data collected is anonymized or pseudonymized in compliance with Data Protection regulations. Notably, data undergoes state-of-the-art encryption (dual process, dual keys). Our policy meets the highest standards of data protection.
Data Driven
Our algorithms have been trained on very large databases of intracardiac signals, carefully annotated by expert physicians.

Our database is continuously enriched with new procedural data that allows us to further improve our AI solutions.
Robust Clinical Validation
Our solution has undergone significant clinical validation with a multicentric clinical trial in Europe (the EvAI-Fib Trial, JCE 2022).

We are currently conducting an international randomized clinical trial (the Tailored-AF trial, NCT04702451) to further validate its use for persistent AF.
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AI CHALLENGES
Data Privacy and Security

Medical data is sensitive and requires the implementation of adequate technical and organizational measures to ensure its security.
Compliance with data protection regulations (GDPR/HIPAA) is paramount.

Data Quantity and Quality

Machine learning and deep-learning models require medical datasets with sufficient size and accuracy. Data quality is hard to define and project dependent.

Data Collection

Peri-procedural data comes from multiple sources. Lack of standardization in how medical data is stored and formatted.

Data Preparation

Annotation, curation, and validation of medical data requires a high-level of expertise.

Bias in Data Sources

Representativeness of the data used to train the models is critical to avoid bias.

Clinical Implementation

To ensure adoption, involvement of the different stakeholders from the development phase is critical and clinical evidence is key.

Data Privacy and Security

Medical data is sensitive and requires the implementation of adequate technical and organizational measures to ensure its security.
Compliance with data protection regulations (GDPR/HIPAA) is paramount.

Data Quantity and Quality

Machine learning and deep-learning models require medical datasets with sufficient size and accuracy. Data quality is hard to define and project dependent.

Data Collection

Peri-procedural data comes from multiple sources. Lack of standardization in how medical data is stored and formatted.

Data Privacy and Security
Medical data is sensitive and requires the implementation of adequate technical and organizational measures to ensure its security.
Compliance with data protection regulations (GDPR/HIPAA) is paramount.
Data Quantity and Quality
Machine learning and deep-learning models require medical datasets with sufficient size and accuracy. Data quality is hard to define and project dependent.
Data Collection
Peri-procedural data comes from multiple sources. Lack of standardization in how medical data is stored and formatted.
Data Preparation
Annotation, curation, and validation of medical data requires a high-level of expertise.
Bias in Data Sources
Representativeness of the data used to train the models is critical to avoid bias.
Clinical Implementation
To ensure adoption, involvement of the different stakeholders from the development phase is critical and clinical evidence is key.
OUR APPROACH
Patient Privacy
All data collected is anonymized or pseudonymized in compliance with Data Protection regulations. Notably, data undergoes state-of-the-art encryption (dual process, dual keys). Our policy meets the highest standards of data protection.
Data Driven
Our algorithms have been trained on very large databases of intracardiac signals, carefully annotated by expert physicians.

Our database is continuously enriched with new procedural data that allows us to further improve our AI solutions.
Robust Clinical Validation
Our solution has undergone significant clinical validation with a multicentric clinical trial in Europe (the EvAI-Fib Trial, JCE 2022).

We are currently conducting an international randomized clinical trial (the Tailored-AF trial, NCT04702451) to further validate its use for persistent AF.