Benefits of AI for Reading Centers
No errors in image interpretation
Less money spent
With AI for reading centers, grading is fast and less expensive
No human bias, better reputation
Improving the reading centers workflow
No time wasted
No more hours spent at a computer screen
Awards and video review
Our proficiency is proved by reputable organizations
How it Works
Altris AI for reading centers ensures data consistency
Altris AI for reading centers allows organizations to provide a standardized and objective assessment of the studied images.
Our system is trained on the biggest clinical dataset of 5 mln OCT scans collected in 11 ophthalmic clinics throughout the years.
As human resources can be expensive, AI makes the image interpretation process faster and cheaper and excludes human bias or potential errors. AI for reading centers harnesses the power of algorithms to facilitate the success of clinical trials.
AI for Clinical Trials
Reading centers play a crucial role in conducting clinical trials and often contribute to the success of the analysis. To perform proper image grading, reading centers must adhere strictly to the established standard operating procedures. However, sometimes the grading process can be extremely costly and time-consuming.
With the vast amount of images that graders have to process, grading becomes even more complicated.
With Altris AI for reading centers, no time is wasted on non-pathological scans, as they are quickly differentiated from pathological OCT scans. Altris system provides users with the detection of 70+ pathologies and pathological signs with 91% accuracy.
A key aspect of clinical research is adherence to good clinical practice guidelines and relevant regulations in the country of practice. Due to the large number of scans that need to be interpreted, graders may miss some pathologies. Our algorithm excludes any biases and provides an accurate approach.
With the largest database of various pathological cases, Altris AI for reading centers ensures no pathological signs are missed.
Since each image is graded by an algorithm using standardized grading protocols, there is less variability in the interpretation and fewer errors.
We provide 91% accumulative accuracy of our AI model
Integrity of analysis
Complete adherence to research protocols
Exceptional level of assessment
Error-free labeling of research images