STUDIES

The Razorbill study

The Challenges:

The standard of care for nAMD is repeated intravitreal injection with anti-VEGFs injections.
Evidence shows that optimal outcomes are achieved with treatment frequency of approximately 8 injections/ year.
However, evidence also show that the average number of injections and treatment visits observed in real- world clinical situations are significantly lower than expected. Contributing factor to this undertreatment is an overwhelmed healthcare system which cause physicians to miss disease activity in 16.6%1 to 29.6%2 of OCT readings compared to Reading Centers.

Clinical studies are complex, expensive and time consuming.

Today the average time to bring a new drug to the market is ten to twelve years3.
The initial phase of R&D, drug discovery, can take five to six years, followed by an additional five to seven years for clinical trials.
To bring a new drug to the market takes an estimated average cost of $2.6 billion.
Key inefficiencies are:

- decentralized data in multiple locations
- long time (e.g. 9-10 months) to submit to regulatory approval, which then can take up.

  1. Gunnemann Fet al. (2017)  Influence of OCT-examination during ranibizumab treatment of AMD patients in a real-life setting (Ocean study). Invest. Ophthalmol. Vis. Sci;58:412.
  1. Martin DF, Maguire MG, et al. (2012). Comparison of Age-related Macular Degeneration Treatments Trials (CATT) Research Group. Ranibizumab and bevacizumab for treatment of neovascular age-related macular degeneration: two-year results. Ophthalmology 2012;1388–98.
  2. Deloitte. Intelligent Clinical Trials. (2020). https://www2.deloitte.com/content/dam/insights/us/articles/22934_intelligent-clinical-trials/DI_Intelligent-clinical-trials.pdf

2 challenges, 1 solution - Discovery®

The Razorbill study hypothesizes that Discovery will add specific supportive  information for a faster and more precise evaluation of anatomical signs of disease activity particularly in less specialised centres.

Discovery as a clinical study tool improve efficiency providing:

  • real-time transparency & accessibility
  • decentralized access to data
  • one-stop solution for: data collection (eCRFs)-image collection-image analysis-faster analysis supported by AI, quantification measurements

Check out Discovery for Clinical Studies

Learn more about the features of Discovery for Clinical Studies and the benefits it brings to Clinical Studies' processes.

Study design

The study consists of two parts:

PHASE 1
  • Prospective data collection
  • 5 countries
  • 18 clinics / sites

Key activities:

Anonymised patients’ files are uploaded to Discovery.
Additional data is collected through 5 eCRF templates


PHASE 2
  • OCT Enrichment analysis
  • 5 countries
  • 36 reviewers

Key activities:

  • Patients are redistributed to reviewers automatically based on protocol logic
  • Each reviewer analyses patients with and without AI enhancement
  • OCT analysis data is collected through 1 eCRF template

READ THE PUBLICATION HERE  

Partners

Project sponsor
Global healthcare company based in Switzerland that provides solutions to address the evolving needs of patients worldwide.

Check the other clinical studies we are involved in:

Check our Discovery for Clinical Studies

Learn more about the features of Discovery for Clinical Studies and the benefits it brings to Clinical Studies' processes.

*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.