Sponsors & CRO
Bitfount unlocks insights from imaging and EHR data to identify sites with patients during study feasibility and accelerate enrolment while reducing screen failures by finding the most eligible patients for the trial.
key features and benefits
No data leaves
No data anonymisation required
Broad data support
Imaging (inc. DICOM), EHR, and more
Install in minutes
Install via desktop app or Python SDK
Certifiably secure
HIPAA & GDPR compliant and ISO27001 certified
No 3rd party hardware
Integrate with your IT systems and EHRs
Deploying deep-learning AI models on data directly within hospital systems helped identify ~100x more eligible patients than would normally be found via the current system of manual image analyses and health record searches.
Define study criteria and model selection
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Model deployment via Bitfount
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Model runs on site data
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Sites receive results
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Stadtspital Zürich
Queen’s University Belfast
Moorfields Eye Hospital
Bay Area Retina Associates
RCMG
Berner Augenklinik
RVAF
Retina Macula Institute
University of Utah
The Retina Clinic London
Universität Regensburg
Erie Retina Research
Ganglion Orvosi Központ
Cascade Medical Research Inst.
Verum Research LLC
No, Bitfount’s zero-trust architecture ensures Bitfount never receives data or analysis results. Analysis results are transferred between data custodians and data scientists end-to-end encrypted using encryption keys held by each party and not accessible to Bitfount. Read more here.
Information Governance and Legal teams love Bitfount since it removes the need for complex Data Sharing Agreements (DSA) and Material Transfer Agreements (MTA), and massively simplifies Data Protection Impact Assessments (DPIA). Bitfount is also fully GDPR, HIPAA and ISO27001 compliant. Visit our online Trust Centre here.
No. Our desktop application has been designed to be operated without requiring any coding knowledge. This includes connecting datasets, joining projects, running tasks and accessing analysis results. Data scientists and algorithm developers can choose to interact with Bitfount via our python SDK.
There aren’t any specific requirements, however AI analysis at scale can be compute-intensive and will run more efficiently, especially for image analysis, if you have access to a machine (physical or virtual) with a GPU. Suitable devices include any Apple silicon model, or a Windows or Linux machine with an Nvidia GPU. Not sure if you have the right setup? Reach out to us at support@bitfount.com for guidance.