Case Study: Floy reduces CT and MRI annotation time with Encord

Floy Reduced CT & MRI Annotation Time with AI-Assisted Labeling

Floy develops AI that helps radiologists detect critical incidental findings in medical images, but its radiology annotators were slowed down by unreliable, inflexible labeling tools. The company needed a better way to annotate complex DICOM scans, support collaborative workflows, and reduce time lost to cumbersome local-download and re-upload processes. Floy turned to Encord for a medical-imaging annotation platform.

Encord provided Floy with DICOM-specific annotation and quality control features, plus collaborative review workflows and a cloud setup that met local data-hosting needs. With Encord, Floy labeled about 60,000 images across 5,100 scans, cut CT labeling time by 50% and MRI labeling time by 20%, and improved annotator satisfaction. Encord also gave Floy better visibility into data quality and annotation performance, helping the team make faster product and staffing decisions.

Floy

Leander Maerkisch

Floy