Ceevra Reveal 3 is intended as a medical imaging system that allows the processing, review, 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.

Device Description

Ceevra Reveal 3 ("Reveal 3"), manufactured by Ceevra, Inc. (the "Company"), is a software as a medical device with two main functions: (1) it is used by Company personnel to generate three-dimensional (3D) images from existing patient CT and MR imaging, and (2) it is used by clinicians to view and interact with the 3D images during preoperative planning and intraoperatively.

Clinicians view 3D images via the Reveal 3 Mobile Image Viewer software application which runs on compatible mobile devices, and the Reveal 3 Desktop Image Viewer software application which runs on compatible computers. The 3D images may also be displayed on compatible external displays, or in virtual reality (VR) format with a compatible off-the-shelf VR headset.

Reveal 3 includes additional features that enable clinicians to interact with the 3D images including rotating, zooming, panning, and selectively showing or hiding individual anatomical structures.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for the Ceevra Reveal 3, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

Acceptance Criteria (Metric)	Reported Device Performance
Prostate (from MR prostate imaging)	0.87 Sørensen-Dice coefficient (DSC)
Bladder (from MR prostate imaging)	0.90 Sørensen-Dice coefficient (DSC)
Neurovascular bundles (from MR prostate imaging)	7.8 mm Hausdorff distance at 95th percentile (HD-95)
Kidney (from CT abdomen imaging)	0.89 Sørensen-Dice coefficient (DSC)
Kidney (from MR abdomen imaging)	0.87 Sørensen-Dice coefficient (DSC)
Artery (from CT abdomen imaging)	0.87 Sørensen-Dice coefficient (DSC)
Artery (from MR abdomen imaging)	0.83 Sørensen-Dice coefficient (DSC)
Vein (from CT abdomen imaging)	0.86 Sørensen-Dice coefficient (DSC)
Vein (from MR abdomen imaging)	0.81 Sørensen-Dice coefficient (DSC)
Artery (from CT chest imaging)	0.85 Sørensen-Dice coefficient (DSC)
Vein (from CT chest imaging)	0.81 Sørensen-Dice coefficient (DSC)

Note: The document explicitly states "Performance was verified by comparing segmentations generated by the machine learning models against segmentations generated by medical professionals from the same imaging study." This implies that the acceptance criteria for each metric were met if the reported performance values were achieved or exceeded. However, specific numerical thresholds for acceptance criteria (e.g., "must be ≥ 0.85 DSC") are not explicitly stated in the provided text, only the reported performance. The presented table assumes the reported performance values themselves serve as the basis for demonstrating compliance.

Study Details:

Sample Size used for the test set and the data provenance:
- Sample Size: 141 imaging studies.
- Data Provenance: Actual CT or MR imaging studies of patients.
  - No dataset contained more than one imaging study from any particular patient.
  - Independence of training and testing data was enforced at the level of the scanning institution (studies from a specific institution were used for either training or testing but not both).
  - Diversity in patient population was ensured across patient age, patient sex, and scanner manufacturers.
  - Subgroup analysis was performed for patient age, patient sex, and scanner manufacturers.
    - Non-prostate related datasets: 40% female, 60% male.
    - Across all datasets by age: 32% under 60, 32% 60-70, 30% over 70, 6% unknown age.
    - Scanner manufacturers included GE Medical Systems, Siemens, Toshiba, and Philips Medical Systems.
    - Ethnicity of patients was generally correlated to the overall US population.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- The document states "segmentations generated by medical professionals." It does not specify the number of medical professionals or their specific qualifications (e.g., radiologist with X years of experience).
Adjudication method for the test set:
- The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1) for resolving disagreements among medical professionals if multiple experts were used to create the ground truth. It simply states "segmentations generated by medical professionals."
If a multi-reader multi-case (MRMC) comparative effectiveness study was done:
- No, an MRMC comparative effectiveness study comparing human readers with and without AI assistance was not mentioned or described. The study focused on the performance of the machine learning models in comparison to ground truth established by medical professionals.
If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, performance was verified by comparing the segmentations generated by the machine learning models against the ground truth. This indicates a standalone performance evaluation of the algorithm.
The type of ground truth used:
- Expert consensus/manual segmentation by medical professionals. The document states: "Performance was verified by comparing segmentations generated by the machine learning models against segmentations generated by medical professionals from the same imaging study."
The sample size for the training set:
- The exact sample size for the training set is not explicitly stated. It only mentions that "No imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used for both."
How the ground truth for the training set was established:
- The document does not explicitly detail how the ground truth for the training set was established. However, given that the ground truth for the test set was established by "medical professionals," it is highly probable that the training set also used ground truth established by medical professionals or similar expert annotations.

Summary

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Ceevra, Inc. % Ken Koster CTO 149 New Montgomery Street, 4th Fl. SAN FRANCISCO CA 94105

April 25, 2023

Re: K222676

Trade/Device Name: Ceevra Reveal 3 Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: March 20, 2023 Received: March 21, 2023

Dear Ken Koster:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for

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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely.

Jessica Lamb

Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K222676

Device Name Ceevra Reveal 3

Indications for Use (Describe)

The machine learning algorithms in use by Ceevra Reveal 3 are for use only for adult patients (22 and over). Threedimensional images for patients under the age of 22 or of unknown age will be generated without the use of any machine learning algorithms.

Type of Use (Select one or both, as applicable)
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X Prescription Use (Part 21 CFR 801 Subpart D)

Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) Summary

1. General Information

510(k) Sponsor	Ceevra, Inc.
Address	149 New Montgomery St, 4th FloorSan Francisco, CA 94105
Correspondence Person	Ken KosterCTO, Ceevra, Inc.
Contact Information	Email: kkoster@ceevra.comPhone: 415-305-5326
Date Prepared	April 18, 2023

2. Subject Device

Proprietary Name	Ceevra Reveal 3
Common Name	Reveal 3
Classification Name	Automated Radiological Image Processing Software
Regulation Number	21 CFR 892.2050
Product Code	QIH
Regulatory Class	II

3. Predicate Device

Proprietary Name	Ceevra Reveal 2.0
Premarket Notification	K173274
Classification Name	System, Image Processing, Radiological
Regulation Number	21 CFR 892.2050
Product Code	LLZ
Regulatory Class	II

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Device Description 4.

Reveal 3 includes additional features that enable clinicians to interact with the 3D images including rotating, zooming, panning, and selectively showing or hiding individual anatomical structures.

ર. 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.

Substantial Equivalence 6.

As detailed in the following tables, the intended use and technological characteristics of the subject device are substantially equivalent to the predicate devices.

Table 6.1: Comparison of Indications for Use Statements

Subject Device:Ceevra Reveal 3 (K222676)	Primary Predicate:Ceevra Reveal 2.0 (K173274)
Ceevra Reveal 3 is intended as a medical imagingsystem that allows the processing, review, analysis,communication and media interchange ofmulti-dimensional digital images acquired from CT orMR imaging devices and that such processing mayinclude the generation of preliminary segmentations ofnormal anatomy using software that employs machine	Ceevra Reveal 2.0 is intended as a medical imagingsystem that allows the processing, review, analysis,communication and media interchange ofmulti-dimensional digital images acquired from CT orMR imaging devices. It is also intended as softwarefor preoperative surgical planning, and as software forthe intraoperative display of the aforementioned

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learning and other computer vision algorithms. It is alsointended as software for preoperative surgical planning,and as software for the intraoperative display of theaforementioned multi-dimensional digital images.Ceevra Reveal 3 is designed for use by health careprofessionals and is intended to assist the clinician whois responsible for making all final patient managementdecisions.	multi-dimensional digital images. Ceevra Reveal 2.0is designed for use by health care professionals and isintended to assist the clinician who is responsible formaking all final patient management decisions.
The machine learning algorithms in use by CeevraReveal 3 are for use only for adult patients (22 andover). Three-dimensional images for patients under theage of 22 or of unknown age will be generated withoutthe use of any machine learning algorithms.

Feature/Function	Subject DeviceCeevra Reveal 3(K222676)	Primary PredicateCeevra Reveal 2.0(K173274)
Supported image Modalities	CT and MR	CT and MR
Intended users	Healthcare Professionals	Healthcare Professionals
Intended environment	Healthcare facilities such ashospitals and clinics	Healthcare facilities suchas hospitals and clinics
Device Class	Class II	Class II
Image analysis features	Interactive manipulationand 3D visualization	Interactive manipulationand 3D visualization
Preoperative viewing of 3D images	Yes	Yes
Intraoperative viewing of 3D images	Yes	Yes
3D images used intraoperatively for real-timeguidance, navigation or otherwise integratedwith surgical instruments	No	No
Segmentation work performed by	Internal Operators	Internal Operators
Built-in features for end-user to compareCT/MR to device output	No	No
Quantitative outputs calculated by device	No	No
Software generates semi-automatedsegmentations of abnormal anatomy	No	No
Software generates semi-automatedsegmentations of certain normal anatomy	Yes	No

Table 7.2: Comparison of Device Characteristics

7. Performance Data

Safety and performance of Ceevra Reveal 3 has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/Amd 1: 2015- Medical device software - Software life cycle processes, in addition to the FDA Guidance

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documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices."

Four machine learning models are included in Ceevra Reveal 3. These models were verified with datasets of actual CT or MR imaging studies of patients. A total of 141 imaging studies were used to evaluate the device. No dataset contained more imaging study from any particular patient. No imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used for both. The data used in the device validation ensured diversity in patient population and scanner manufacturers. Subgroup analysis was performed for patient age, patient sex, and scanner manufacturers. For non-prostate related datasets, verification datasets included 40% female patients and 60% male patients. Across all datasets, 32% of patients were under 60 years old, 32% were 60 to 70 years old, 30% were over 70 years old, and 6% were of unknown age. Scanner manufacturers included GE Medical Systems, Siemens, Toshiba, and Philips Medical Systems. Ethnicity of patients in the datasets was reasonably correlated to the overall US population.

Performance was verified by comparing segmentations generated by the machine learning models against segmentations generated by medical professionals from the same imaging study. The performance of the machine learning models, characterized by the Sørensen-Dice coefficient (DSC) or the Hausdorff distance metric at the 95th percentile (HD-95), was as follows: prostate imaging) 0.87 DSC; bladder (from MR prostate imaging) 0.90 DSC; neurovascular bundles (from MR prostate imaging) 7.8 mm HD-95; kidney (from CT abdomen imaging) 0.89 DSC; kidney (from MR abdomen imaging) 0.87 DSC; artery (from CT abdomen imaging) 0.87 DSC; artery (from MR abdomen imaging) 0.83 DSC; vein (from CT abdomen imaging) 0.86 DSC: vein (from MR abdomen imaging) 0.81 DSC: artery ffrom CT chest imaging) 0.85 DSC; vein (from CT chest imaging) 0.81 DSC.

8. Conclusion

Ceevra Reveal 3 is deemed to be substantially equivalent to its predicate device based on indications for use, technological characteristics and performance testing. Ceevra Reveal 3 raises no new questions related to safety or effectiveness.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).