Diagnocat

Diagnocat is an AI-powered computer-assisted detection (CADe) software that analyzes cone beam CT (CBCT) images to aid dental professionals in detecting periapical radiolucency (bone lesions at tooth roots). The device provides automated detection and localization of suspected periapical radiolucency on permanent teeth as a concurrent aid to clinical interpretation, not as a replacement for professional judgment. It is intended for use by licensed dental professionals with at least two years of CBCT reading experience on patients 22 years and older.

Both Diagnocat and its predicate device are software-only, AI-based CADe systems using supervised machine learning with similar principles of operation. The primary technological difference is input image type: Diagnocat analyzes 3D CBCT images while the predicate analyzes 2D periapical radiographs. Both detect the same dental finding (periapical radiolucency) and provide concurrent reading workflow. Diagnocat supports additional image formats (DICOM, JPEG, TIFF, PNG) versus the predicate, and offers both web and desktop application configurations versus desktop-only for the predicate.

Not stated in this summary. The document references FDA guidance on "Cybersecurity in Medical Devices" but does not cite specific ISO, IEC, ASTM, or other consensus standards by number.

Diagnocat is substantially equivalent because it shares the same intended use (aid in detecting periapical radiolucency), same classification (21 CFR 892.2070), same product code (MYN), and fundamentally identical technological principles as the predicate Overjet device. Minor differences in image input type and age range do not alter the core diagnostic purpose. Performance testing demonstrates Diagnocat achieves sensitivity of 0.854 and specificity of 0.991 in periapical radiolucency detection, with an MRMC study showing improved clinician performance (AUC increase of 0.027), confirming safety and effectiveness equivalent to the predicate.