FDA-cleared profile

Ceevra Reveal 3+

Ceevra, Inc. · CT/CTA; MRI · FDA-cleared (510(k))

Prostate imaging Quantification and segmentation

Authorized purpose: Process, review, analyze, communicate, and exchange multidimensional CT or MR images, including preliminary segmentation of normal anatomy, for preoperative surgical planning and intraoperative display; healthcare professionals make all final patient-management decisions. Machine-learning algorithms are for adults aged 22 years and older, while images for patients under 22 or of unknown age are generated without machine learning.

Clinical role: 3D CT/MR image generation and interactive review for preoperative planning and intraoperative display, with preliminary normal-anatomy segmentation and measurement support; the device is not integrated with surgical instruments for real-time guidance or navigation.
Images and patients: CT/CTA; MRI · Multi-region normal anatomy represented in CT or MR images; validated examples include prostate, bladder, neurovascular bundles, kidney, artery, and vein
Key limitation: Keep in mind: Machine-learning algorithms are limited to adults aged 22 years and older; under-22 or unknown-age 3D images are generated without machine learning. Segmentations are preliminary and limited to normal anatomy, and the clinician retains final management responsibility. The device is not intended to provide semi-automated segmentation of abnormal anatomy or integrate with surgical instruments for real-time guidance/navigation; specific current interfaces and deployment are not established.

Overview status: Source-reviewed overview

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Clinical details Purpose, workflow fit, evidence, and safety record

Workflow navigation pilot

Workflow role has not yet been reviewed for this navigation pilot.

A tag identifies a documented role; it does not establish compatibility or local acceptance. Methods and sources

What does this software do?Authorized purpose: Process, review, analyze, communicate, and exchange multidimensional CT or MR images, including preliminary segmentation of normal anatomy, for preoperative surgical planning and intraoperative display; healthcare professionals make all final patient-management decisions. Machine-learning algorithms are for adults aged 22 years and older, while images for patients under 22 or of unknown age are generated without machine learning.
Which images or clinical context are in scope?Modality: CT/CTA; MRI Anatomy or clinical context: Multi-region normal anatomy represented in CT or MR images; validated examples include prostate, bladder, neurovascular bundles, kidney, artery, and vein Input: Existing multidimensional digital images acquired from CT or MR imaging devices; patient age determines whether machine-learning algorithms are used, with no machine learning for patients under 22 or of unknown age.
Who uses it?Intended users: Healthcare professionals; company personnel generate the 3D images in the described service workflow
What does the software produce?Output: Three-dimensional images viewable through compatible mobile and desktop viewer applications, external displays, or compatible off-the-shelf VR headsets; users can manipulate the images, selectively show or hide anatomical structures, view measurements of or between structures, and review preliminary segmentations of normal anatomy.
What is its most important limitation?Key limitation: Machine-learning algorithms are limited to adults aged 22 years and older; under-22 or unknown-age 3D images are generated without machine learning. Segmentations are preliminary and limited to normal anatomy, and the clinician retains final management responsibility. The device is not intended to provide semi-automated segmentation of abnormal anatomy or integrate with surgical instruments for real-time guidance/navigation; specific current interfaces and deployment are not established.

Clinical takeaway. Clinical role: Process, review, analyze, communicate, and exchange multidimensional CT or MR images, including preliminary segmentation of normal anatomy, for preoperative surgical planning and intraoperative display; healthcare professionals make all final patient-management decisions. Machine-learning algorithms are for adults aged 22 years and older, while images for patients under 22 or of unknown age are generated without machine learning.

Evidence takeaway. Evidence from the reviewed source record: FDA K233568 reports software verification and validation using 141 actual CT or MR imaging studies, with training and testing kept independent at the scanning-institution level. Four machine-learning models were compared with medical-professional segmentations: prostate DSC 0.87, bladder DSC 0.90, neurovascular-bundle HD-95 7.8 mm, kidney DSC 0.89 for CT abdomen and 0.87 for MR abdomen, artery DSC 0.87 for CT abdomen, 0.83 for MR abdomen, and 0.85 for CT chest, and vein DSC 0.86 for CT abdomen, 0.81 for MR abdomen, and 0.81 for CT chest. Volume, diameter, and two-point-distance measurements were verified within a mean difference of plus or minus 10%.

Purpose: Process, review, analyze, communicate, and exchange multidimensional CT or MR images, including preliminary segmentation of normal anatomy, for preoperative surgical planning and intraoperative display; healthcare professionals make all final patient-management decisions. Machine-learning algorithms are for adults aged 22 years and older, while images for patients under 22 or of unknown age are generated without machine learning.

Workflow role: 3D CT/MR image generation and interactive review for preoperative planning and intraoperative display, with preliminary normal-anatomy segmentation and measurement support; the device is not integrated with surgical instruments for real-time guidance or navigation.

Required input: Existing multidimensional digital images acquired from CT or MR imaging devices; patient age determines whether machine-learning algorithms are used, with no machine learning for patients under 22 or of unknown age.

Output and clinician action: Three-dimensional images viewable through compatible mobile and desktop viewer applications, external displays, or compatible off-the-shelf VR headsets; users can manipulate the images, selectively show or hide anatomical structures, view measurements of or between structures, and review preliminary segmentations of normal anatomy.

Care setting: Healthcare facilities such as hospitals and clinics; machine-learning use is limited to adult patients aged 22 years and older

Intended users: Healthcare professionals; company personnel generate the 3D images in the described service workflow

Limitations: Machine-learning algorithms are limited to adults aged 22 years and older; under-22 or unknown-age 3D images are generated without machine learning. Segmentations are preliminary and limited to normal anatomy, and the clinician retains final management responsibility. The device is not intended to provide semi-automated segmentation of abnormal anatomy or integrate with surgical instruments for real-time guidance/navigation; specific current interfaces and deployment are not established.

Evidence summary: FDA K233568 reports software verification and validation using 141 actual CT or MR imaging studies, with training and testing kept independent at the scanning-institution level. Four machine-learning models were compared with medical-professional segmentations: prostate DSC 0.87, bladder DSC 0.90, neurovascular-bundle HD-95 7.8 mm, kidney DSC 0.89 for CT abdomen and 0.87 for MR abdomen, artery DSC 0.87 for CT abdomen, 0.83 for MR abdomen, and 0.85 for CT chest, and vein DSC 0.86 for CT abdomen, 0.81 for MR abdomen, and 0.81 for CT chest. Volume, diameter, and two-point-distance measurements were verified within a mean difference of plus or minus 10%.

Evidence

Performance evidence

Metrics are shown only when they are tied to a source, endpoint, population, and tested product version. Candidate literature without exact product and version linkage is not shown as product evidence.

Evidence summary: FDA K233568 reports software verification and validation using 141 actual CT or MR imaging studies, with training and testing kept independent at the scanning-institution level. Four machine-learning models were compared with medical-professional segmentations: prostate DSC 0.87, bladder DSC 0.90, neurovascular-bundle HD-95 7.8 mm, kidney DSC 0.89 for CT abdomen and 0.87 for MR abdomen, artery DSC 0.87 for CT abdomen, 0.83 for MR abdomen, and 0.85 for CT chest, and vein DSC 0.86 for CT abdomen, 0.81 for MR abdomen, and 0.81 for CT chest. Volume, diameter, and two-point-distance measurements were verified within a mean difference of plus or minus 10%.

Reported sensitivity: Research pending

Reported specificity: Research pending

No contextualized exact-version metric has completed evidence review. Regulatory-document values, when available, are shown above with their limitations.

Ceevra Reveal 3+ regulatory performance study summary

DesignStand-Alone Performance
PopulationAdult
Scopeexact submission
Tested version3
Samplen=141
IndependenceIndependence not established

K233568

ACR AI Central summarizes the regulatory study; consult the exact FDA materials before comparative use.

Source

Safety and lifecycle

Postmarket record

Recall and adverse-event records are shown only after product matching. Adverse-event reports do not establish incidence or causality.

Postmarket safety review: The exact-identifier FDA recall query returned no native recall record for K233568. This limited result does not establish absence of recalls or other safety information and does not cover later versions, family records, MAUDE reports, corrections, or field notices.

Pricing & contracts Public price, contract terms, and reimbursement

Price not public here; enter your quote

Not verified: Pricing information is recorded but not verified against a resolving source.

Not verified:public pricing.
Not yet reviewed:pricing model, setup and integration fees, recurring fees, minimum commitment, contract duration, renewal terms, cancellation terms, support and service terms, trial or pilot terms.

Reimbursement

Not publicly available:reimbursement.

IT implementation Deployment, interfaces, data handling, and support

System and version relationships

Match the AI release, system build, configuration and region. Source documentation does not establish acceptance of an installed system.

No specific system/version pairing has completed review for this FDA record. A broad DICOM or product-family statement does not establish a named viewer build's support.

Review Merge, Visage, Sectra, AGFA and IntelePACS context

Local implementation checklist

Agree the evidence and acceptance owner with your team and suppliers. Checkmarks apply to this page only and are not acceptance receipts.

Data path. Document input routing, gateways, identifiers, de-identification and every destination.
Output destination. Verify object types, overlays, measurements, report location and access to the original images.
Latency and failures. Agree a workload and turnaround target; test queues, timeouts, missing results and fallback reading.
Security documents. Review hosting, retention, access, audit logs, incident handling and required contractual documents.
Ownership. Name the clinical, IT and vendor owners, acceptance authority and support escalation path.
Training. Confirm reader/admin training, known limitations and how to correct or override outputs.
Updates and rollback. Define version change control, revalidation triggers, stop-use criteria and a tested rollback route.

Open the printable evaluation checklist

Integration: The FDA packet describes communication and media interchange of CT/MR images plus compatible mobile and desktop applications, external displays, and off-the-shelf VR headset viewing. It does not establish a specific current PACS, RIS, EHR, interface version, or deployment topology.

Not verified:deployment, security, training, monitoring.
Not yet reviewed:data hosting location, data retention, documented data use, hardware requirements, network requirements, identity and access, installation timeline.

ACR exact-submission catalog context

ACR AI Central provides an exact-submission model card for Ceevra Reveal 3+; FDA labeling remains controlling for clinical use, limitations, and performance.

exact submission · checked 2026-09-08

Source

Sources & review details FDA identity, research record, source ledger, and history

Regulatory identity

FDA record and catalog context

The FDA listing establishes the regulatory identity. It does not by itself establish local workflow fit, pricing, security, or performance in your environment.

FDA submission: K233568

Modality: CT/CTA; MRI

Anatomy: Multi-region normal anatomy represented in CT or MR images; validated examples include prostate, bladder, neurovascular bundles, kidney, artery, and vein

Clearance type: 510(k)

Decision date: 2023-12-05

FDA status: FDA-cleared

Research record

What has been checked

This audit distinguishes completed source review from fields that have not yet been researched.

Exact FDA record reviewedStatus

2026-09-20Last searched

21Fields reviewed

9Source classes checked

0Screened PubMed leads

0Screened trial leads

0Unreviewed FDA recall leads

The refreshed discovery snapshot contains no PubMed or ClinicalTrials.gov candidates; no external evidence was promoted. The current discovery snapshot contains 0 PubMed and 0 ClinicalTrials.gov candidate records; its dated, count-matched human screening remains current. Unlinked candidates remain quarantined. Native FDA recall identifiers produced 0 postmarket candidate records; 0 have been reviewed (0 published, 0 rejected) and 0 remain unreviewed.

Candidate leads were screened against the exact product and tested version. Unlinked records remain quarantined.

Source classes: fda ai list, fda decision summary, acr ai central product, fda device recall, literature index, trial registry, pubmed, clinical trials, openfda device recall

Field review details

Recorded state, verification date, notes and sources for fields without a publishable value. Conflicting records stay explicitly unsettled here; no plain settled value is shown for them.

Public pricing: Not verified - checked 2026-09-08 - Exact FDA submission
No public price or quote for the exact K233568 configuration was established in this review.
Pricing model: Not yet reviewed
Setup and integration fees: Not yet reviewed
Recurring fees: Not yet reviewed
Minimum commitment: Not yet reviewed
Contract duration: Not yet reviewed
Renewal terms: Not yet reviewed
Cancellation terms: Not yet reviewed
Support and service terms: Not yet reviewed
Trial or pilot terms: Not yet reviewed
Reimbursement: Not publicly available - checked 2026-09-08 - Exact FDA submission
No exact-product payer policy, coding instruction, or payment determination was established; eligibility must be evaluated by payer, site of service, and use case.
Deployment: Not verified - checked 2026-09-08 - Exact FDA submission
The reviewed sources do not establish the current exact-release hosting topology, data flow, region, tenancy, or disaster-recovery configuration.
Data hosting location: Not yet reviewed
Data retention: Not yet reviewed
Documented data use: Not yet reviewed
Hardware requirements: Not yet reviewed
Network requirements: Not yet reviewed
Identity and access: Not yet reviewed
Security: Not verified - checked 2026-09-08 - Exact FDA submission
Exact-release security controls, data retention, hosting boundaries, and scoped assurance documentation require vendor confirmation.
Installation timeline: Not yet reviewed
Training: Not verified - checked 2026-09-08 - Exact FDA submission
Current training prerequisites, competency checks, and support commitments for the exact configuration require vendor confirmation.
Monitoring: Not verified - checked 2026-09-08 - Exact FDA submission
Current drift, quality, uptime, alert, escalation, and incident-response commitments for the exact configuration require vendor confirmation.

7 sources - 3 history items

Source ledger

FDA K233568: Ceevra Reveal 3+

U.S. Food and Drug Administration - Published 2023-12-05 - Accessed 2026-09-08

Scope: identity, clinicalUse, modality, anatomy, workflow, evidence, implementation
Artificial Intelligence-Enabled Medical Devices

U.S. Food and Drug Administration - Accessed 2026-08-31

Scope: regulatoryIdentity, modalityContext
Ceevra Reveal 3+ model card (K233568)

American College of Radiology - Published 2026-06-26 - Accessed 2026-09-01

Scope: intendedUsers, careSetting, output, evidenceSummary, anatomy, evidenceStudies
PubMed biomedical literature index

U.S. National Library of Medicine - Accessed 2026-09-01

Scope: evidenceDiscovery
ClinicalTrials.gov study registry

U.S. National Library of Medicine - Accessed 2026-09-01

Scope: evidenceDiscovery, studyStatus
openFDA Device Recall API

U.S. Food and Drug Administration - Accessed 2026-09-02

Scope: recallDiscovery, nativeSubmissionMatching
openFDA exact recall query for K233568

U.S. Food and Drug Administration - Accessed 2026-09-08

Scope: postmarketSafety

Research history

2023-12-05

K233568 FDA decision

Regulatory record

510(k)
2026-09-20

Source-review record updated

Research record

fda record reviewed
2026-09-20

Reader overview source-reviewed

Editorial review

Source-reviewed editorial overlay

Evaluation checklist (resource)

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