Clinical Research
Geographic Atrophy
Enhance the success of your GA study
Increase your study’s probability of success by leveraging insights from historical GA trials.
Our GA tools are designed to optimize patient selection and enrich your study with participants showing high disease activity, thereby increasing the likelihood of statistically significant efficacy readouts at early timepoints with the minimum number of enrolled patients.
Source: Ursula Schmidt-Erfurth et al. | Disease activity and therapeutic response to pegcetacoplan for geographic atrophy identified by deep learning-based analysis of OCT
RetInSight’s GA Monitor
RetInSight’s GA Monitor is the only AI-based tool that provides immediate insights into the most critical biomarkers in geographic atrophy:
- Retinal pigment epithelium (RPE) loss1,
- photoreceptor (PR) degeneration (also known as EZ layer loss)2,
- and the overlap of both 3 4 5
This data helps differentiate between fast and slow progressors using the PR/RPE ratio, allowing you to assess disease activity accurately.
Understanding Disease Activity:
- Fast Progressor = High Disease Activity
- Slow Progressor = Low Disease Activity
Source: Ursula Schmidt-Erfurth et al. | Disease activity and therapeutic response to pegcetacoplan for geographic atrophy identified by deep learning-based analysis of OCT
Benefits of using the GA Monitor in your clinical study
- Comprehensive reporting: All key parameters are displayed in a single report, making it easy for monitors and auditors to review.
- Informed decision-making: Treatment decisions can be made based on real-time reading results.
- Visual assessment: En-face images enable visual scan checks.
- User-friendly integration: Easy upload process with no special equipment required; compatible with any web browser.
- Enhanced patient communication: Detailed reports facilitate patient communication, supporting patient retention.
- Secure data management: All reports are stored within the system and can be reprinted at any time.
FAQs
What does RetInSight’s GA analysis provide for clinical research?
RetInSight’s GA algorithms provide automated OCT‑based segmentation and quantification of structural features associated with geographic atrophy.
Why is OCT‑based analysis valuable for GA clinical studies?
OCT provides layer‑specific structural information and is already standard imaging in most sponsored trial sites.
Using AI‑based OCT analysis gives trial sponsors access to both RPE and EZ integrity measures, enabling a more complete structural assessment.
Why is it important to distinguish between RPE loss and photoreceptor degeneration (EZ loss) in GA clinical trials?
RPE loss and EZ loss represent different but related structural changes in GA.
Differentiating them is important because:
- RPE loss is a classical structural marker used in many GA studies.
- EZ loss (photoreceptor degeneration) may extend beyond the area of RPE loss, providing additional insight into the structural impact of GA.
- The relationship between EZ loss and RPE loss can help characterize different structural patterns of disease activity and a differentiation between slow and fast progression.
RetInSight’s GA algorithms quantify both layers independently and together, supporting detailed imaging‑based research endpoints.
How does AI‑based OCT analysis improve GA study data quality?
AI provides:
- standardized segmentation of GA, RPE loss, and EZ loss across all sites
- layer‑specific quantitative metrics
- high scalability for large imaging datasets
- consistent results independent of grader variability
These features improve imaging data quality and reproducibility.
Our team of clinical, scientific, and AI experts is ready to bring our extensive expertise to your trial. Contact us to learn how we can support your study’s success!
References
- Sophie Riedl et al. | The Effect of Pegcetacoplan Treatment on Photoreceptor Maintenance in Geographic Atrophy Monitored by Artificial Intelligence – Based OCT Analysis
- Julia Mai et al. | Comparison of Fundus Autofluorescence Versus Optical Coherence Tomography–based Evaluation of the Therapeutic Response to Pegcetacoplan in Geographic Atrophy
- Wolf-Dieter Vogl et al. | Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning
- Ursula Schmidt-Erfurth et al. | Monitoring der Progression von geografischer Atrophie in der optischen Kohärenztomographie
- Ursula Schmidt-Erfurth et al. | Disease activity and therapeutic response to pegcetacoplan for geographic atrophy identified by deep learning-based analysis of OCT