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.

Device Description

Ceevra Reveal 3+, as modified, ("Modified 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 Mobile Image Viewer software application which runs on compatible mobile devices, and the 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.

Modified Reveal 3+ includes features that enable clinicians to interact with the 3D images including rotating, zooming, panning, selectively showing or hiding individual anatomical structures, and viewing measurements of or between anatomical structures.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for the Ceevra Reveal 3+ device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the reported performance metrics. The study evaluated the accuracy of segmentations generated by the machine learning models. The performance metrics reported are the Sørensen-Dice coefficient (DSC) for volume-based segmentation accuracy and the Hausdorff distance metric at the 95th percentile (HD-95) for surface distance accuracy.

Anatomical Structure	Imaging Modality	Metric	Reported Device Performance
Prostate	MR prostate imaging	DSC	0.90
Bladder	MR prostate imaging	DSC	0.93
Neurovascular bundles	MR prostate imaging	HD-95	6.6 mm
Kidney	CT abdomen imaging	DSC	0.92
Kidney	MR abdomen imaging	DSC	0.89
Artery	CT abdomen imaging	DSC	0.90
Artery	MR abdomen imaging	DSC	0.87
Vein	CT abdomen imaging	DSC	0.88
Vein	MR abdomen imaging	DSC	0.82
Pulmonary artery	CT chest imaging	DSC	0.82
Pulmonary vein	CT chest imaging	DSC	0.83
Airways	CT chest imaging	DSC	0.82
Bronchopulmonary segments	CT chest imaging	DSC	0.86

2. Sample Size Used for the Test Set and Data Provenance

Sample Size: A total of 133 imaging studies were used to evaluate the device.
Data Provenance: The text does not explicitly state the country of origin. However, it indicates that the device's machine learning algorithms are for use with adults (22 and over) and that "Ethnicity of patients in the datasets was reasonably correlated to the overall US population," implying the data is likely from the United States or at least representative of the US population. It was retrospective data, sourced from various scanning institutions. Independence of training and testing data was enforced at the institution level.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The text 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 specific number of experts is not mentioned.
Their qualifications are broadly described as "medical professionals," without further detail on their experience level or subspecialty (e.g., radiologist with X years of experience).

4. Adjudication Method for the Test Set

The text implies a direct comparison between the AI's segmentation and the "medical professionals'" segmentation. It does not specify an adjudication method (e.g., 2+1, 3+1 consensus with multiple readers) for establishing the ground truth if there were discrepancies among medical professionals. It simply states "segmentations generated by medical professionals." This might imply a single expert's ground truth, or a pre-established consensus for each case, but no specific method is detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a MRMC comparative effectiveness study was not explicitly stated or described. The study focused on the performance of the AI model itself (standalone) compared to human-generated ground truth. There is no mention of comparing human readers with AI assistance versus human readers without AI assistance. Therefore, no effect size of how much human readers improve with AI vs. without AI assistance is provided.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone performance evaluation was done. The study specifically verified the performance of the "machine learning models" by comparing their generated segmentations directly against ground truth established by medical professionals.

7. The Type of Ground Truth Used

The ground truth used was expert consensus / expert-generated segmentations. The text states it was established by "segmentations generated by medical professionals."

8. The Sample Size for the Training Set

The document does not provide the exact sample size for the training set. It only states 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." It also mentions that "The data used in the device validation ensured diversity in patient population and scanner manufacturers."

9. How the Ground Truth for the Training Set Was Established

The document does not explicitly state how the ground truth for the training set was established. However, given that the evaluation for the test set used segmentations generated by "medical professionals," it is highly probable that the ground truth for the training set was established in a similar manner, likely through manual segmentation by medical experts.

Summary

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Ceevra, Inc. Ken Koster CTO 149 New Montgomery St. 4th Floor San Francisco, California 94105

March 4, 2025

Re: K243933

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: OIH Dated: January 10, 2025 Received: January 10, 2025

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 (the 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 available 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.

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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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 medical devices and radiation-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-regulatory

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assistance/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 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

Submission Number (if known)

K243933

Device Name

Ceevra Reveal 3+

Indications for Use (Describe)

Type of Use (Select one or both, as applicable)

| Prescription Use (Part 21 CFR 801 Subpart D)

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

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510(k) Summary

General Information 1.

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	March 2, 2025

2. Updated 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. Originally Cleared Device

Proprietary Name	Ceevra Reveal 3+
Common Name	Reveal 3+
Premarket Notification	K233568
Classification Name	Automated Radiological Image Processing Software
Regulation Number	21 CFR 892.2050

Ceevra, Inc., Traditional 510(k) Page 1

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Product Code	QIH
Regulatory Class	II

Device Description 4.

ನ. Indications for Use

Substantial Equivalence 6.

As detailed in the following tables, the indications for use and technological characteristics of the updated device are substantially equivalent to the originally cleared device.

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			Table 6.1: Comparison of Indications for Use Statements
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Updated Device:	Originally Cleared Device:
Modified Ceevra Reveal 3+	Ceevra Reveal 3+ (K233568)
Ceevra Reveal 3+ is intended as a medical imagingsystem that allows the processing, review, analysis,communication and media interchange of multi-dimensional digital images acquired from CT or MRimaging devices and that such processing may includethe generation of preliminary segmentations of normalanatomy using software that employs machine learningand other computer vision algorithms. It is also intendedas software for preoperative surgical planning, and assoftware for the intraoperative display of theaforementioned multi-dimensional digitalimages.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.	Ceevra Reveal 3+ is intended as a medical imagingsystem that allows the processing, review, analysis,communication and media interchange of multi-dimensional digital images acquired from CT or MRimaging devices and that such processing may includethe generation of preliminary segmentations of normalanatomy using software that employs machine learningand other computer vision algorithms. It is also intendedas software for preoperative surgical planning, and assoftware for the intraoperative displayof theaforementioned multi-dimensional digitalimages.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.
The machine learning algorithms in use by Ceevra	The machine learning algorithms in use by Ceevra
Reveal 3+ are for use only for adult patients (22 and	Reveal 3+ are for use only for adult patients (22 and
over). Three-dimensional images for patients under the	over). Three-dimensional images for patients under the
age of 22 or of unknown age will be generated without	age of 22 or of unknown age will be generated without
the use of any machine learning algorithms.	the use of any machine learning algorithms.

Feature/Function	Updated Device:Modified Ceevra Reveal 3+	Originally Cleared Device:Ceevra Reveal 3+ (K233568)
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 such ashospitals and clinics
Device Class	Class II	Class II
Image analysis features	Interactive manipulation and3D visualization	Interactive manipulation and3D visualization
Preoperative use	Yes	Yes
Intraoperative use	Yes	Yes

Table 6.2: Comparison of Technological Characteristics

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Feature/Function	Updated Device:Modified Ceevra Reveal 3+	Originally Cleared Device:Ceevra Reveal 3+ (K233568)
3D images used intraoperativelyfor real-time guidance, navigationor otherwise integrated withsurgical instruments	No	No
Segmentation work performed by	Internal Operators	Internal Operators
Built-in features for end-user tocompare CT/MR to device output	No	No
Quantitative measurementscalculated by device	Volume of structure, diameterof structure, distance betweentwo points	Volume of structure, diameterof structure, distance betweentwo points
Software generates semi-automated segmentations ofabnormal anatomy	No	No
Software generates semi-automated segmentations ofcertain normal anatomy	Yes	Yes
Technology used to generate semi-automated segmentations ofpulmonary arteries within thelung, pulmonary veins within thelung, pulmonary airways, andbronchopulmonary segments	Machine Learning	Computer vision algorithmsnot utilizing Machine Learning

7. Performance Data

Safety and performance of Modified 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 documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions. "

Four machine learning models are included in Modified Ceevra Reveal 3+. These models were verified with datasets of actual CT or MR imaging studies of patients. A total of 133 imaging studies were used to evaluate the device. No dataset contained more than one imaging study from any particular patient. No

Ceevra, Inc., Traditional 510(k) Page 4

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Image /page/8/Picture/0 description: The image shows the logo for CEEVRA. The logo consists of a blue circular design made up of smaller curved lines that resemble a stylized letter 'C'. Below the circular design, the word "CEEVRA" is written in a blue sans-serif font. The overall design is clean and modern.

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 included 48% female patients and 52% male patients. Across all datasets, 31% of patients were under 60 years old, 36% were 60 to 70 years old, 27% were over 70 years old, and 6% were of unknown age. Scanner manufacturers included GE Medical Systems, Toshiba, Hitachi, 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 (from MR prostate imaging) 0.90 DSC; bladder (from MR prostate imaging) 0.93 DSC; neurovascular bundles (from MR prostate imaging) 6.6 mm HD-95; kidney (from CT abdomen imaging) 0.92 DSC; kidney (from MR abdomen imaging) 0.89 DSC; artery (from CT abdomen imaging) 0.90 DSC; artery (from MR abdomen imaging) 0.87 DSC; vein (from CT abdomen imaging) 0.88 DSC; vein (from MR abdomen imaging) 0.82 DSC; pulmonary attery (from CT chest imaging) 0.82 DSC; pulmonary vein (from CT chest imaging) 0.83 DSC, airways (from CT chest imaging) 0.82 DSC; bronchopulmonary segments (from CT chest imaging) 0.86 DSC.

The accuracy of measurement features has been validated on phantom data and on datasets of actual CT or MR imaging studies of patients, including CT and MR imaging studies processed with machine learning models.

Conclusion 8.

Based on the intended use, indications for use, technological characteristics, and performance comparison to the originally cleared Reveal 3+ device (K233568), Modified Ceevra Reveal 3+ device is deemed to not raise new questions of safety and effectiveness and is substantially equivalent to the originally cleared device in terms of safety, efficacy, and performance.

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).