Voronoi Health Analytics Incorporated · Class II · Cleared Mar 16, 2026
| K-number | K253944 |
| Device name | Data Analysis Facilitation Suite (DAFS) |
| Applicant | Voronoi Health Analytics Incorporated |
| Product code | QIH |
| Device class | Class II |
| Decision date | Mar 16, 2026 |
| Decision | Substantially Equivalent |
| Regulation | 892.2050 |
DAFS is a software-only medical device for trained healthcare professionals that automatically segments, visualizes, and quantitatively analyzes anatomical structures in CT images. It processes DICOM-formatted CT data to identify body composition tissues and internal organs, provides measurements of area, volume, and intensity, displays 2D and 3D representations, and includes tools for manual editing. Results must be reviewed and confirmed by a qualified healthcare professional as the device is intended as an adjunct to clinical assessment, not for diagnostic interpretation.
DAFS has the same technological characteristics as the predicate device DeepCatch: both are prescription-use devices for clinical experts, both process DICOM CT imaging information, both provide analysis/measurement, 2D/3D visualization, segmentation, 3D rendering, and CSV data export, both segment similar anatomical regions, offer equivalent visualization/editing tools, provide data reporting, and support tabular and PDF export formats.
Not stated in this summary. The document references the Dice Similarity Coefficient (DSC) as a performance metric but does not cite ISO, IEC, ASTM, or other formal consensus standards.
DAFS is substantially equivalent to DeepCatch because both devices have identical indications for use (automated segmentation, visualization, and quantitative analysis of CT images as an adjunct to clinical assessment), identical technological characteristics, and equivalent functionality. Testing demonstrated high performance with mean Dice scores greater than 0.90 for major anatomical structures and median slice annotation error of 0 slices, establishing comparable safety and effectiveness.
View the full FDA submission: accessdata.fda.gov