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Clinical Studies without Discovery
screened
recruited
analysed
Clinical Studies with Discovery
Discovery transforms data workflows, enabling parallel workflows and centralizing data analysis to accelerate clinical study timelines
CHALLENGES
DELAYS & LOSS TO FOLLOW UP: Enrolling patients in a study may involve a third-party evaluation, increasing onboarding time and risking a potential loss to follow up between first visit and enrollment. Additionally, pivotal GO/NO GO decisions can be delayed due to the operational challenges of aggregating data from multiple sites.
A SIGNIFICANT INVESTMENT & RISK: Clinical studies are one of the largest expenses for pharma and life science companies and many risk failure due to suboptimal study design or an inadequate definition of patient subgroups.
SOLUTIONS:
With Discovery for Clinical Trials it is possible to:
- Centralize data collection: to increase visibility and monitoring throughout the study development and make real time decisions
- Evaluate in real time enrollment criteria and endpoints: to support patient enrollment with real time data analysis
RESULTS:
MONITORING STUDY PROGRESS ANYWHERE AT ANYTIME
SAME DAY PATIENT EVALUATION WORKFLOWS
SPONSOR: Novartis
STUDY OBJECTIVES:
1. Evaluate the influence of automated OCT image enrichment (using AI) on disease activity assessment in nAMD patients treated with anti-VEGF injections
2. Assess if RetinAI’s Discovery can optimize clinical workflows
DISCOVERY’S ROLE:
in phase I of the study → data collection
in phase II of the study → automatic OCT enrichment using RetinAI’s fluid and layer segmentation certified models
SPONSOR: Isarna Therapeutics GmbH
STUDY OBJECTIVE: Evaluate the influence of automated OCT image enrichment (using AI) on disease activity assessment in nAMD patients treated with anti-VEGF injections
DISCOVERY’S ROLE: Provide a same day data evaluation workflow for enrollment by review of the clinical and imaging data between sites and reading center. RetinAI’s fluid and layer segmentation models are used to evaluate the primary endpoints (reduction of fluid and center macula thickness).
STUDY OBJECTIVE: Evaluate the statistical significance (sensitivity and specificity) of the LuxIA algorithm for screening Diabetic Retinopathy, compared to three, independent and blinded graders.
DISCOVERY’S ROLE: Platform was used for the data collection and data review phases. In the data collection phase, Discovery’s eCRFs were used to collect clinical patient data as well as multi-modality imaging data such as fundus images (used for analysis of the algorithm) and OCTs. During the review phase, the workflow deployed in Discovery, ensured graders were blinded to each other’s grading outcomes for robust, unbiased analysis.
*Disclaimer
RetinAI Discovery is a CE-marked medical device according to the Medical Devices Regulation (EU) 2017/745
and the AI models are CE-marked devices according to Medical Devices Directive 93/42/EEC
RetinAI Discovery® is a 510(k) FDA Cleared medical device in US.
RetinAI Discovery® and Retinai® are both trademarks of RetinAI Medical AG.
The AI modules for biomarkers, fluid and layer segmentation and quantification
in retinal pathologies are for research use only in the USA.
The Advanced Segmentation and GA modules are for research use only.
Please be advised these tools are not intended to be a substitute for medical advice, diagnosis or treatment.
We do not warrant any reliance on the accuracy, completeness or usefulness of any content.
