CardIQ Suite is a non-invasive software application designed to provide an optimized application to analyze cardiovascular anatomy and pathology based on 2D or 3D CT cardiac non contrast and angiography DICOM data from acquisitions of the heart. It provides capabilities for the visualization and measurement of vessels and visualization of chamber mobility. CardIQ Suite also aids in diagnosis and determination of treatment paths for cardiovascular diseases to include, coronary artery disease, functional parameters of the heart structures and follow-up for stent placement, bypasses and plaque imaging.

CardIQ Suite provides calcium scoring, a non-invasive software application, that can be used with non-contrasted cardiac images to evaluate calcified plaques in the coronary arteries, heart valves and great vessels such as the aorta. The clinician can use the information provided by calcium scoring to monitor the progression of calcium in coronary arteries over time, and this information may aid the clinician in their determination of the prognosis of cardiac disease.

Device Description

CardIQ Suite is a non-invasive software application designed to work with DICOM CT data acquisitions of the heart. It is a collection of tools that provide capabilities for generating measurement's both automatically and manually, displaying images and associated measurements in an easy-to-read format and tools for exporting images and measurements in a variety of formats.

CardIQ Suite provides an integrated workflow to seamlessly review calcium scoring and coronary CT angiography (CCTA) data. Calcium Scoring has the capability to automatically segment and label the calcifications within the coronary arteries, and then automatically compute a total and per territory calcium score. The calcium segmentation/labeling is using a new deep learning algorithm. The calcium scoring is based on the standard Agatston/Janowitz 130 (AJ 130) and Volume scoring methods for the segmented calcific regions. The software also provides the users a manual calcium scoring capability that allows them to edit (add/delete or update) auto scored lesions. It also allows the user to manually score calcific lesions within coronary arteries, aorta, aortic valve and mitral valve as well as other general cardiac structures. Calcium scoring offers quantitative results in the AJ 130 score, Volume and Adaptive Volume scoring methods.

Calcium Scoring results can be exported as DICOM SR to assist with integration into structured reporting templates. Images can be saved and exported for sharing with referring physicians, incorporating into reports and archiving as part of the CT examination.

The Multi-Planar Reformat (MPR) Cardiac Review and Coronary Review steps provide an interactive toolset for review of cardiac exams. Coronary CTA datasets can be reviewed utilizing the double oblique angles to visually track the path of the coronary arteries as well as to view the common cardiac chamber orientations. Cine capability for multi-phase data may be useful for visualization of cardiac structures in motion such as chambers, valves and arteries, automatic tracking and labeling will allow a comprehensive analysis of the coronaries. Distance measurement and ROI tools are available for quantitative evaluation of the anatomy.

AI/ML Overview

Based on the provided text, here is a description of the acceptance criteria and the study that proves the device meets them:

Device: CardIQ Suite (K233731)
Functionality being assessed: Automated Heart Segmentation, Coronary Tree Segmentation, Coronary Centerline Tracking, and Coronary Artery Labeling (all utilizing new deep learning algorithms).

1. Table of Acceptance Criteria and Reported Device Performance

Feature / Metric	Acceptance Criteria	Reported Device Performance
Automated Outputs Acceptability (Reader Study)	Acceptable by readers for greater than 90% of exams which had good image quality (based on Likert Scales and Additional Grading Scales).	The automated outputs provided by the Heart Segmentation, Coronary Tree Segmentation, Coronary Centerline tracking and Coronary Labeling algorithms incorporated in the subject device CardIQ Suite were scored to be acceptable by the readers for greater than 90% of the exams which had good image quality.
Algorithm Validation (Bench Testing)	Algorithm successfully passes the defined acceptance criteria (specific criteria not detailed in the provided text, but implied for each of the four new deep learning algorithms: heart segmentation, coronary segmentation, coronary centerline tracking, and coronary labeling).	The result of the algorithm validation showed that the algorithm successfully passed the defined acceptance criteria.

2. Sample size used for the test set and the data provenance

Test Set (Reader Study): A "sample of clinical CT images" was used. The exact number of cases is not specified.
Test Set (Bench Testing): A "database of retrospective CT exams" was used. The exact number of cases is not specified.
Data Provenance: The text does not explicitly state the country of origin. The bench testing data is described as "representative of the clinical scenarios where CardIQ Suite is intended to be used," suggesting it covers relevant acquisition protocols and clinical indicators. Both studies are retrospective ("retrospective CT exams" for bench testing and "sample of clinical CT images" for the reader study implying pre-existing data).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Number of Experts: A "reader study evaluation was performed," indicating multiple readers. The exact number is not explicitly stated.
Qualifications of Experts: The text refers to them as "readers." Their specific qualifications (e.g., "radiologist with 10 years of experience") are not detailed.

4. Adjudication method for the test set

The reader study used "Likert Scales and Additional Grading Scales" for evaluation. The specific adjudication method (e.g., 2+1, 3+1 consensus) for establishing a definitive ground truth or resolving discrepancies among readers is not detailed in the provided text. Scores were "to be acceptable by the readers," implying individual reader agreement or perhaps a simple majority/threshold.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Yes, a reader study was performed, which is a type of MRMC study in a sense.
The study did not directly measure human reader improvement with AI vs. without AI assistance quantitatively (e.g., AUC increase). Instead, it focused on the acceptability of the AI-generated outputs.
However, the conclusion states an perceived improvement in workflow efficiency: "Based on the reader study evaluation, we conclude that the automation of Heart Segmentation, Coronary Tree Segmentation, Coronary Centerline Tracking and Coronary Artery Labeling provides an improvement in workflow efficiency when compared to the predicate and reference devices wherein these functionalities were performed manually by the user or using traditional algorithms."

The effect size (quantification of improvement) in terms of reader diagnostic performance is not provided, only the qualitative statement about workflow efficiency and the acceptability of the AI's outputs.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

Yes, a standalone evaluation of the algorithms was performed as "Engineering has performed bench testing for the four newly introduced deep learning algorithms... The result of the algorithm validation showed that the algorithm successfully passed the defined acceptance criteria." This bench testing implies an assessment of the algorithm's performance independent of human interaction for its specific outputs against some predefined criteria.

7. The type of ground truth used

For the Reader Study: The ground truth for evaluating the acceptability of the automated outputs was based on the "scores" given by human "readers" using Likert Scales and Additional Grading Scales. This is a form of expert consensus/reader assessment of the AI's output quality.
For the Bench Testing for Algorithm Validation: The text states "the algorithm successfully passed the defined acceptance criteria". While the exact nature of this "defined acceptance criteria" is not specified, it would typically involve comparing algorithm output to a reference standard, which could be expert-annotated ground truth, a pre-established gold standard, or other quantitative metrics. The document does not specify if it was pathology, outcomes data, or expert consensus. It likely involved expert-derived annotations or quantitative metrics on the retrospective CT exams.

8. The sample size for the training set

The sample size for the training set is not provided in the given text.

9. How the ground truth for the training set was established

The method for establishing ground truth for the training set is not provided in the given text.

Summary

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GE Medical Systems SCS °/o Peter Uhlir Regulatory Affairs Program Manager 283 Rue De La Miniere Buc, 78530 FRANCE

Re: K233731

Trade/Device Name: CardIQ Suite Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-Ray System Regulatory Class: Class II Product Code: JAK, OIH Dated: November 21, 2023 Received: July 3, 2024

Dear Peter Uhlir:

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.

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).

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for

Your device is also subject to, among other requirements, the Quality System (OS) 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.

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 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, Gabriela M. Digitally signed by Rodal -S Gabriela M. Rodal -S Lu Jiang, Ph.D. Assistant Director Diagnostic X-ray Systems 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

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Indications for Use

510(k) Number (if known) K233731

Device Name CardIQ Suite

Indications for Use (Describe)

CardIQ Suite provides calcium scoring, a non-invasive software application, that can be used with non-contrasted cardiac images to evaluate calcified plaques in the coronary arteries, heart valves and great vessels such as the clinician can use the information provided by calcium scoring to monitor the progression of calcium in coronary arteries over time, and this information may aid the clinician in their determination of the prognosis of cardiac disease.

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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are

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

Date:	July 31, 2024
Submitter:	GE Medical Systems SCSEstablishment Registration Number - 9611343283 rue de la Miniere78530 Buc, France
Primary Contact Person:	Peter UhlirRegulatory Affairs Program ManagerGE HealthCareTel: (+36) 70-436-9317Email: peter.uhlir@ge.com
Secondary Contact Person:	Elizabeth MathewSenior Regulatory Affairs ManagerGE HealthCareTel: 262-424-7774Email: Elizabeth.Mathew@ge.com
Device Trade Name:	CardIQ Suite
Common/Usual Name:	System, X-Ray, Tomography, Computed
Primary Classification name:	Computed tomography x-ray system
Primary Regulation Number:	21 CFR 892.1750
Primary Product Code:	JAK
Secondary Product Code:	QIH
Classification:	Class II

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Primary Predicate Device
Device name:	CardIQ Suite
Common/Usual Name:	System, X-Ray, Tomography, Computed
Manufacturer:	GE Medical Systems SCS
510(k) number:	K213725
Classification Name:	Computed tomography x-ray system
Regulation Number:	21 CFR 892.1750
Product Code:	JAK
Classification:	Class II
Reference Device:
Device name:	CardIQ Xpress 2.0
Common/Usual Name	System, X-Ray, Tomography, Computed
Manufacturer:	GE Medical Systems SCS
510(k) number:	K073138
Classification Name:	Computed tomography x-ray system
Regulation Number:	21 CFR 892.1750
Product Code:	JAK
Classification:	Class II

Device Description:

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lesions. It also allows the user to manually score calcific lesions within coronary arteries, aorta, aortic valve and mitral valve as well as other general cardiac structures. Calcium scoring offers quantitative results in the AJ 130 score, Volume and Adaptive Volume scoring methods.

Intended Use:

CardIQ Suite is a collection of non-invasive software features intended to analyze CT cardiovascular anatomy and pathology and aid in determining treatment paths.

Indication for Use:

Technology:

The proposed device CardIQ Suite employs the same fundamental scientific technology as its predicate and reference devices.

Comparison:

The table below summarizes the key feature/technological differences and similarities between the predicate devices:

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Specification	Primary PredicateDevice:CardIQ Suite (K213725)	Subject Device:CardIQ Suite	Comparison
Input Data forCalciumScoring	Image Requirements:* 120kVp* Gated cardiacacquisition* DFOV - 24 cm - 26 cm* Slice thickness ≤ 3mm* Non-contrast	Image Requirements:* 120kVp* Gated cardiacacquisition* DFOV - 24 cm - 35 cm* Slice thickness ≤ 3mm* Non-contrast	Substantially equivalent.The only modification in the subjectdevice comes from the DFOV limitationthat has been adjusted to be lessrestrictive.
Segmentationand labelingcalcific regionsin thecoronaries	Yes,Automated using deeplearning algorithm	Yes,Automated using deeplearning algorithm	Identical
ManualSegmentationand labeling ofcalcific regions	Yes	Yes	Identical
Labeling ofcalcifications	The software providesthe following labels forthe coronary arteriesaccording to regionalterritories.• LAD territory: LeftMain Artery (LMA),Ramus IntermediusBranch (RIB), LeftAnterior Descending(LAD) and all Diagonalbranches.• LCX territory: LeftCircumflex artery (LCX)and all Obtuse marginalbranches.• RCA territory: RightCoronary Artery (RCA),Posterior DescendingArtery (PDA) and	The software providesthe following labels forthe coronary arteriesaccording to regionalterritories.• LAD territory: LeftMain Artery (LMA),Ramus IntermediusBranch (RIB), LeftAnterior Descending(LAD) and all Diagonalbranches.• LCX territory: LeftCircumflex artery (LCX)and all Obtuse marginalbranches.• RCA territory: RightCoronary Artery (RCA),Posterior DescendingArtery (PDA) and	Identical
Specification	Primary PredicateDevice:CardIQ Suite (K213725)	Subject Device:CardIQ Suite	Comparison
	Posterior Lateral Branch(PLB).	Posterior Lateral Branch(PLB).
Computationof Agatstonscore	Yes	Yes	Identical
Calcium Score– VolumeScoringMethod	Yes,Volume and AdaptiveVolume	Yes,Volume and AdaptiveVolume	Identical

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Image /page/8/Picture/1 description: This image shows a table comparing the primary predicate device, subject device, and comparison for cardiac review using CardIQ Suite (K213725) and CardIQ Suite. The primary predicate device and subject device both involve coronary 2D review to assist readers in coronary artery imaging. The comparison states that the devices are substantially equivalent, with the only difference being the introduction of a new deep learning algorithm to automatically segment the heart and provide a segmented 3D Volume Rendering model of the heart.

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Specification	Primary PredicateDevice:CardIQ Suite (K213725)	Subject Device:CardIQ Suite	Comparison
Data Export	CardIQ Suite provides avariety of methods forsharing the results withclinical partners.* Calcium Score resultsand scored images canbe saved as DICOM SRseries and networked toDICOM destinations forstructured reportingpurposes.* Copy individualimages or the resultstable to paste intopersonalizedcommunications.* Screen captureindividual images andresults to save datapertinent to the patientfile for selectivearchiving needs.* Generate file forimporting intocustomized reporttemplates or researchfile management needs* Export selected Images	CardIQ Suite provides avariety of methods forsharing the results withclinical partners.* Calcium Score resultsand scored images canbe saved as DICOM SRseries and networked toDICOM destinations forstructured reportingpurposes.* Copy individualimages or the resultstable to paste intopersonalizedcommunications.* Screen captureindividual images andresults to save datapertinent to the patientfile for selectivearchiving needs.* Generate file forimporting intocustomized reporttemplates or researchfile management needs* Export selected Images	Identical
CoronaryReview step	No	Yes	Substantial EquivalentDeep-Learning Algorithms areincorporated in the subject device toautomatically segment coronary,automatically track and label coronarycenterline in order to improve workflowefficiency. The same functionalitiesalready exist in the reference deviceCardIQ Xpress 2.0 (K073138), thedifference is in how the functionality is
Specification	Primary PredicateDevice:	Subject Device:	Comparison
	CardIQ Suite (K213725)	CardIQ Suite	implemented in the devices. For theCoronary tree segmentation, thereference device, CardIQ Xpress 2.0(K073138) utilizes signal processingmethods to achieve coronarysegmentation, whereas in the subjectdevice a deep-learning based algorithmis implemented to perform the samefunction.
		CardIQ Suite	For the Coronary centerline tracking, thereference device CardIQ Xpress 2.0utilizes mathematical morphology,leveraging on hessian filters and raytracing to provide the coronarycenterline tracking, whereas in thesubject device a deep-learning basedalgorithm is implemented to perform thesame function. The prerequisite for thisalgorithm is that the Coronary treesegmentation must be performed.
		CardIQ Suite	For the Coronary labeling, the referencedevice CardIQ Xpress 2.0 utilizes a rules-based algorithm, based on theknowledge of the anatomy of thecoronaries, whereas in the subjectdevice a deep-learning based algorithmis implemented to perform the samefunction. The prerequisite for thisalgorithm is that the Coronary treesegmentation and Coronary centerlinetracking must be performed.
		CardIQ Suite	From the user perspective the output ofthe coronary segmentation, tracking andlabeling, the editing capability forcoronary segmentation, tracking andlabeling and the workflow remains thesame between the subject device andreference device. The main reason toincorporate the deep learning algorithmsis to improve the workflow efficiency.

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Determination of Substantial Equivalence:

Summary of Non-Clinical, Design Control Testing

CardIQ Suite has successfully completed the design control testing per GE's quality system. It was designed and will be manufactured under the Quality System Regulations of 21CFR 820 and ISO 13485. No additional hazards were identified, and no unexpected test results were observed. The proposed device complies with NEMA PS 3.1 - 3.20 (2022) Digital Imaging and Communications in Medicine (DICOM) Set (Radiology) standard.

The following quality assurance measures were applied to the development of the device:

Requirements Definition
Risk Analysis
Technical Design Reviews
Formal Design Reviews ●
Software Development Lifecycle
Performance testing (Verification, Validation)
Safety Testing (Verification)

The proposed CardIQ Suite has been successfully verified on the AW Server platform. All the testing and results did not raise new or different questions of safety and effectiveness other than those already associated with predicate devices. The documentation level was determined to be Basic Documentation Level.

In addition, Engineering has performed bench testing for the four newly introduced deep learning algorithms in the subject device for automated heart segmentation, coronary segmentation, coronary centerline tracking and coronary labeling, using a database of retrospective CT exams. This database of exams is representative of the clinical scenarios where CardIQ Suite is intended to be used, with consideration of acquisition protocols and clinical indicators. The result of the algorithm validation showed that the algorithm successfully passed the defined acceptance criteria.

Summary of Clinical Testing

A reader study evaluation was performed with a sample of clinical CT images which were processed with the CardIQ Suite software. The purpose of this study was to evaluate the output of the automated Heart Segmentation, Coronary Tree Segmentation, Coronary Centerline Tracking and Coronary Artery Labeling using the Likert Scales and Additional Grading Scales. The reader evaluation concluded that the automated outputs provided by the Heart Segmentation, Coronary Tree Segmentation, Coronary Centerline tracking and Coronary Labeling algorithms incorporated in the subject device CardIQ Suite were scored to be acceptable by the readers for greater than 90% of the exams which had good image quality. Based on the reader study evaluation, we conclude that the automation of Heart Segmentation, Coronary Tree Segmentation, Coronary Centerline Tracking and Coronary Artery Labeling provides an improvement in workflow efficiency when compared to the predicate and reference devices wherein these functionalities were performed manually by the user or using traditional algorithms.

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Conclusion:

CardIQ Suite has substantial equivalent technological characteristics as its predicate devices.

GE's quality system's design, verification, and risk management processes did not identify any new questions of safety or effectiveness, hazards, unexpected results, or adverse effects stemming from the changes to the predicates.

Based on development under GE HealthCare's quality system, successful design verification, software documentation for a "Basic Documentation Level", along with the engineering bench testing and reader study, GE HealthCare believes that the proposed CardIQ Suite is substantially equivalent to, and hence as safe and as effective for its Intended Use as the legally marketed predicate device.

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.