AI can be a powerful tool for those developing new drug-based ophthalmological therapies, helping pharmaceutical organizations identify novel therapeutic targets and accelerate clinical trial workflows. One such company sought to fuel AI development by building a rich ophthalmology imaging database from previous clinical trials. Their goal was to create high‑quality ground truth annotations for Optical Coherence Tomography (OCT) images, essential for training and testing robust machine learning algorithms.
But the company’s existing process was slow and fragmented. The team had to ship raw imaging data on physical drives to reading centers, where readers would perform annotations on disconnected platforms. Once complete, reading centers would send the annotated files back, where the team reconciled them with internal data and finally loaded them into high‑performance computing environments for analysis. Each annotation campaign could take three months or more, significantly delaying their time to trial and go-to-market for new AI models and therapies.
Learn more about how they saved 3+ months per annotation campaign with Flywheel.
