Sponsors & CRO
De-risk site feasibility and patient recruitment
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.
Use cases
Patient pre-screening
Empower sites to boost enrolment and reduce screen fails
AI-driven analyses custom-developed to match your trials’ inclusion/exclusion criteria to find eligible patients based on EHR- and image-based biomarkers. Increase site yield and accelerate recruitment while reducing site burden.
Site selection
Accelerate trial setup and improve patient diversity
Make faster, more informed site selection decisions based on objective data about the patient populations accessible via each site. No more relying on over-optimistic estimates of patient availability and diversity.
Feasibility studies
Augment your trial modelling with real-world data
Inform feasibility studies by collecting data on patient demographics, disease prevalence, current treatments and more by partnering with trial sites to share insights from their data, without requiring them to share any of it.
Protocol design
Optimise trial protocols with federated data science
Safely simulate and iterate different sets of inclusion/exclusion criterial to maximise the trial’s chances of success and minimise the need for time-consuming protocol amendments.
Trial monitoring
Move towards adaptive trial design by closing the feedback loop in real time
Get real-time insights directly from trial sites in order to inform decision-making and iron out bottlenecks. From image quality assessments to protocol adherence reports.
Post-market surveillance
Go to market with federated pharmacovigilance
Automatically monitor drug- and device safety, identify adverse events, and find opportunities for improvement by tapping into real-time source-of-truth imaging and EHR data using federated data science.
key features and benefits
A single easy-to-integrate platform for all use-cases
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
88% reduction in costs for patient pre-screening in retinal disease trials with federated AI
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.
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Clinical trial pre-screening
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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Some of the AI-enabled sites using Bitfount
Stadtspital Zürich
Queen’s University Belfast
Moorfields Eye Hospital
Bay Area Retina Associates
RCMG
Berner Augenklinik
RVAF
Retina Macula Institute
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University of Utah
The Retina Clinic London
Universität Regensburg
Erie Retina Research
Ganglion Orvosi Központ
Cascade Medical Research Inst.
Verum Research LLC
Frequently asked questions
Can Bitfount access my data?
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.
What will my Information Governance team think of this?
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.
Do I need to be able to code?
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.
Are there any hardware requirements?
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.
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