Hepatic VCAR is a CT image analysis software package that allows the analysis and visualization of Liver CT data derived from DICOM 3.0 compliant CT scans. Hepatic VCAR is designed for the purpose of assessing liver morphology, including liver lesion, provided the lesion has different CT appearance from surrounding liver tissue; and its change over time through automated tools for liver lobe, liver segments and liver lesion segmentation and measurement. It is intended for use by clinicians to process, review, archive, print and distribute liver CT studies.

This software will assist the user by providing initial 3D segmentation, visualization, and quantitative analysis of liver anatomy. The user has the ability to adjust the contour and confirm the final segmentation.

Device Description

Hepatic VCAR is a CT image analysis software package that allows the analysis and visualization of Liver CT data derived from DICOM 3.0 compliant CT scans. Hepatic VCAR was designed for the purpose of assessing liver morphology, including liver lesion, provided the lesion has different CT appearance from surrounding liver tissue; and its change over time through automated tools for liver, liver lobe, liver segments and liver lesion segmentation and measurement.

Hepatic VCAR is a post processing software medical device built on the Volume Viewer (K041521) platform, and can be deployed on the Advantage Workstation (AW) (K110834) and AW Server (K081985) platforms, CT Scanners, and PACS stations or cloud in the future.
This software will assist the user by providing initial 3D segmentation, vessel analysis, visualization, and quantitative analysis of liver anatomy. The user has the ability to adjust the contour and confirm the final segmentation.
In the proposed device, two new algorithms utilizing deep learning technology were introduced. One such algorithm segments the liver producing a liver contour editable by the user; another algorithm segments the hepatic artery based on an initial user input point. The hepatic artery segmentation is also editable by the user.

AI/ML Overview

The provided text describes the 510(k) summary for Hepatic VCAR, a CT image analysis software package. The submission outlines the device's intended use and the validation performed, particularly highlighting the introduction of two new deep learning algorithms for liver and hepatic artery segmentation.

Here's an analysis of the acceptance criteria and study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly define "acceptance criteria" through numerical thresholds for performance metrics. Instead, it states that "Verification and validation including risk mitigations have been executed with results demonstrating Hepatic VCAR met the design inputs and user needs with no unexpected results or risks."

For the new deep learning algorithms, the performance is described qualitatively:

Feature/Algorithm	Acceptance Criteria (Implied)	Reported Device Performance
Liver Segmentation	Produces a liver contour that is editable by the user and is capable of segmentation.	Bench tests show algorithms performed as expected. Demonstrated capability of liver segmentation utilizing the deep learning algorithm.
Hepatic Artery Segmentation	Segments the hepatic artery based on initial user input, editable by the user, and capable of segmentation.	Bench tests show algorithms performed as expected. Demonstrated capability of hepatic artery segmentation utilizing the deep learning algorithm.
Overall Software Performance	Meets design inputs and user needs, no unexpected results or risks.	Verification and validation met design inputs and user needs with no unexpected results or risks.
Usability/Clinical Acceptance	Functionality is clinically acceptable for assisting users in 3D segmentation, visualization, and quantitative analysis.	Assessed by 3 board-certified radiologists using a 5-point Likert scale, demonstrating capability.

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

Test Set Sample Size: "A representative set of clinical sample images" was used for the clinical assessment. The exact number of cases/images is not specified in the provided text.
Data Provenance: The provenance of the data (e.g., country of origin, retrospective or prospective) is not specified.

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

Number of Experts: For the clinical assessment of the deep learning algorithms, 3 board certified radiologists were used.
Qualifications of Experts: They are described as "board certified radiologists." The number of years of experience is not specified.
For the "ground truth" used in "bench tests," the text states "ground truth annotated by qualified experts," but the number and specific qualifications of these experts are not explicitly detailed beyond "qualified experts."

4. Adjudication Method for the Test Set

The text states that the "representative set of clinical sample images was assessed by 3 board certified radiologists using 5-point Likert scale." It does not specify an explicit adjudication method (e.g., 2+1, 3+1 consensus) for establishing the "ground truth" or assessing the device's performance based on the radiologists' Likert scale ratings. The Likert scale assessment sounds more like an evaluation of clinical acceptability/usability rather than establishing ground truth for quantitative segmentation accuracy.

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

A formal MRMC comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance is not explicitly described in the provided text. The clinical assessment mentioned ("assessed by 3 board certified radiologists using 5-point Likert scale") appears to be an evaluation of the device's capability rather than a direct comparison of human performance with and without AI assistance. Therefore, no effect size for human reader improvement is provided.

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

Yes, standalone performance was assessed for the algorithms. The text states:

"Bench tests that compare the output of the two new algorithms with ground truth annotated by qualified experts show that the algorithms performed as expected."
This indicates an evaluation of the algorithm's direct output against an established ground truth before human interaction/adjustment.

7. The Type of Ground Truth Used

Based on the document:

For the "bench tests" of the new deep learning algorithms, the ground truth was "ground truth annotated by qualified experts." This suggests expert consensus or expert annotation was used.
For the clinical assessment by 3 radiologists using a Likert scale, it's more of a qualitative assessment of the device's capability rather than establishing a definitive ground truth for each case.

8. The Sample Size for the Training Set

The sample size for the training set (used to train the deep learning algorithms) is not specified in the provided text.

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

The text states that the deep learning algorithms were trained, but it does not explicitly describe how the ground truth for the training set was established. It only mentions that the ground truth for bench tests was "annotated by qualified experts."

Summary

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GE Medical Systems SCS % Lifeng Wang Regulatory Affairs Manager 283 rue de la Miniere 78530 Buc FRANCE

March 20, 2020

Re: K193281

Trade/Device Name: Hepatic VCAR Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray System Regulatory Class: Class II Product Code: JAK, LLZ Dated: February 19, 2020 Received: February 20, 2020

Dear Lifeng Wang:

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 and Part 809); medical device reporting of medical device-related adverse events) (21 CFR

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for 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.

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and 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)

Device Name

Hepatic VCAR

Indications for Use (Describe)

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

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510(k) Summary

K193281

In accordance with 21 CFR 807.92 the following summary of information is provided:

Date:	Nov 26, 2019
Submitter:	GE Medical Systems SCS (Establishment Registration Number – 9611343)
	283 rue de la Miniere
	78530 Buc, France
Primary ContactPerson:	Lifeng Wang
	Regulatory Affairs Manager
	GE Healthcare
	Phone: +86 10 57083145
	Email: lifeng.wang@ge.com
Secondary ContactPerson:	Elizabeth Mathew
	Senior Regulatory Affairs Manager
	GE Healthcare
	Phone: (262) 424-7774
	Email: Elizabeth.Mathew@ge.com
Proposed Device	> Device Name: Hepatic VCAR
	> Regulation number/ Product Code: 21 CFR 892.1750 Computed tomographyx-ray system / JAK
	> Secondary Regulation number/ Product Code: 21 CFR 892.2050 Picturearchiving and communications system/ LLZ
	> Classification: Class II
Predicate Device:	> Device Name: Hepatic VCAR
	> 510(k) number: K133649
	> Regulation number/ Product Code: 21 CFR 892.1750 Computed tomographyx-ray system / JAK
	> Secondary Regulation number/ Product Code: 21 CFR 892.2050 Picturearchiving and communications system/ LLZ
	> Classification: Class II
Device Description	Hepatic VCAR is a CT image analysis software package that allows the analysisand visualization of Liver CT data derived from DICOM 3.0 compliant CT scans.Hepatic VCAR was designed for the purpose of assessing liver morphology,including liver lesion, provided the lesion has different CT appearance fromsurrounding liver tissue; and its change over time through automated tools forliver, liver lobe, liver segments and liver lesion segmentation and measurement.
	Hepatic VCAR is a post processing software medical device built on the VolumeViewer (K041521) platform, and can be deployed on the Advantage Workstation

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Image /page/4/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters "GE" in a stylized, cursive font, enclosed within a blue circle. There are three white teardrop shapes surrounding the circle, positioned at the top and on either side.

	(AW) (K110834) and AW Server (K081985) platforms, CT Scanners, and PACSstations or cloud in the future.This software will assist the user by providing initial 3D segmentation, vesselanalysis, visualization, and quantitative analysis of liver anatomy. The user hasthe ability to adjust the contour and confirm the final segmentation.In the proposed device, two new algorithms utilizing deep learning technologywere introduced. One such algorithm segments the liver producing a liver contoureditable by the user; another algorithm segments the hepatic artery based on aninitial user input point. The hepatic artery segmentation is also editable by theuser.
	This software will assist the user by providing initial 3D segmentation, vesselanalysis, visualization, and quantitative analysis of liver anatomy. The user hasthe ability to adjust the contour and confirm the final segmentation.
Intended Use/Indication for Use:	Hepatic VCAR is a CT image analysis software package that allows the analysisand visualization of Liver CT data derived from DICOM 3.0 compliant CT scans.Hepatic VCAR is designed for the purpose of assessing liver morphology,including liver lesion, provided the lesion has different CT appearance fromsurrounding liver tissue; and its change over time through automated tools forliver, liver lobe, liver segments and liver lesion segmentation and measurement. Itis intended for use by clinicians to process, review, archive, print and distributeliver CT studies.
	This software will assist the user by providing initial 3D segmentation, vesselanalysis, visualization, and quantitative analysis of liver anatomy. The user hasthe ability to adjust the contour and confirm the final segmentation.
Technology:	The modified Hepatic VCAR employs two deep learning convolutional neuralnetworks to segment the liver contour and the hepatic artery on CT liver examswhile the predicate device uses a traditional deterministic method to segment theliver and manual tools to segment the vascular structure including the hepaticartery. These changes do not change the Indications for Use from the predicate,and represent equivalent technological characteristics, with no impact on controlmechanism, and operating principle.The table below summarizes the feature/technological comparison between thepredicate device and the proposed device:
	Specification	Predicate Device:Hepatic VCAR (K133649)	Proposed Device:Hepatic VCAR
	Liversegmentation	Atlas algorithm basedsegmentation	Deep Learning algorithmbased segmentation
	Hepatic arterysegmentation	Manual segmentation tools("autoselect" and scalpel)	Semi-automatic segmentationworkflow based on deeplearning segmentation of thehepatic artery and edition
			tools to correct/refine theresult.
Determination ofSubstantialEquivalence:	Verification and validation including risk mitigations have been executed withresults demonstrating Hepatic VCAR met the design inputs and user needs withno unexpected results or risks.
	Hepatic VCAR was designed and will be manufactured under the Quality SystemRegulations of 21CFR 820 and ISO 13485. The following quality assurancemeasures have been applied to the development of the device:
	Risk Analysis Requirements Reviews Design Reviews Performance testing (Verification, Validation) Safety testing (Verification)
	Bench tests that compare the output of the two new algorithms with ground truthannotated by qualified experts show that the algorithms performed as expected.
	A representative set of clinical sample images was assessed by 3 board certifiedradiologists using 5-point Likert scale. The assessment demonstrated thatcapability of liver segmentation and hepatic artery segmentation utilizing the deeplearning algorithm by Hepatic VCAR.
	The substantial equivalence was also based on software documentation for a"Moderate" level of concern device.
Conclusion:	GE Healthcare considers proposed device Hepatic VCAR to be as safe, aseffective, and performance is substantially equivalent to the predicate device.

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Image /page/5/Picture/1 description: The image shows the logo for General Electric (GE). The logo consists of the letters "GE" in a stylized, cursive font, enclosed within a blue circle. There are three white, teardrop-shaped elements surrounding the circle, positioned at the top, left, and right sides. The logo is simple and recognizable, representing the company's brand identity.

Regulation Number and Section

§ 892.1750 Computed tomography x-ray system.

(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.