# K222676: Ceevra Reveal 3

- Pathway: 510(k) (Traditional)
- Applicant: Ceevra, Inc.
- Decided: Apr 25, 2023 after 231 days
- Product code: QIH (Automated Radiological Image Processing Software)
- Device class: 2
- Regulation: 21 CFR 892.2050
- Recalls of this device: none
- Data: FDA decisions to Oct 25, 2026; recalls to Oct 12, 2026

## Lineage
Longest line of descent: 10 generations back to K924630 (1993). 12 ancestors in all.
5 later devices descend from it; 3 cite it directly.
0 of 18 devices in its family (counting itself) have a Class I recall.

## Predicates on the family tree (from the FDA summary)
- K173274: Ceevra Reveal 2.0, Ceevra, Inc. (2018) Summary page 4.

## Most influential descendants
- K233568: Ceevra Reveal 3+ (2023), 2 descendants of its own
- K243933: Ceevra Reveal 3+ (2025), 1 descendant of its own
- K261677: Artificial Intelligence Renal Assessment (AIRA) Version 1 (2026), 0 descendants of its own
- K261595: Ceevra Reveal 4 (2026), 0 descendants of its own
- K252091: Surgical Reality Viewer (2026), 0 descendants of its own

## Indications for use
Ceevra Reveal 3 is intended as a medical imaging system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT or MR imaging devices and that such processing may include the generation of preliminary segmentations of normal anatomy using software that employs machine learning and other computer vision algorithms. It is also intended as software for preoperative surgical planning, and as software for the intraoperative display of the aforementioned multi-dimensional digital images. Ceevra Reveal 3 is designed for use by health care professionals and is intended to assist the clinician who is responsible for making all final patient management decisions. The machine learning algorithms in use by Ceevra Reveal 3 are for use only for adult patients (22 and over). Three-dimensional images for patients under the age of 22 or of unknown age will be generated without the use of any machine learning algorithms.

Source page: https://510kreport.com/device/K222676
Methodology: https://510kreport.com/methodology