cvi42 Auto is intended to be used for viewing, post-processing, qualitative evaluation of cardiovasular magnetic resonance (MR) and computed tomography (CT) images in a Digital Imaging and Communications in Medicine (DICOM) Standard format.

It enables a set of tools to assist physicians in qualitative assessment of cardiac images and quantitative measurements of the heart and adjacent vessels: perform calcium scoring: and to confirm the presence of physician-identified lesion in blood vessels.

The target population for cvi42 Auto's manual workflows is not restricted; however, cvi42 Auto's semi-automated machine learning algorithms are intended for an adult population.

cvi42 Auto shall be used only for cardiac images acquired from an MR or CT scanner. It shall be used by qualified medical professionals, experienced in examining and evaluating cardiovascular MR or CT images, for the purpose of obtaining diagnostic information as part of a comprehensive decision-making process.

Device Description

cvi42 Auto is a software as a medical device (SaMD) that is intended for evaluating CT and MR images of the cardiovascular system. Combining digital image processing, visualization, guantification, and reporting tools, cvi42 Auto device is designed to support the physician in confirming the presence or absence of physician-identified lesion in blood vessels and evaluation, documentation and follow up of any such lesions.

cvi42 Auto uses machine learning techniques to aid in semi-automatic contouring of regions of interest of cardiac magnetic resonance (MR) or computed tomography (CT) images as follows:

1. Cardiac Function: semi-automatic contouring of the four heart chambers (including left ventricle, left atrium, right ventricle, right atrium) in MR images.
1. Calcium Assessment: using pixel intensity technique, identify calcified plaque in major coronary arteries in non-contrast enhanced CT images.
1. Coronary Analysis: semi-automatic placement of centerline in coronary vessels to visualize the coronary arteries and assess stenosis in non-contrast enhanced CT images.

The data used to train these machine learning algorithms were sourced from multiple clinical sites from urban centers and from different countries. When selecting data for training, the importance of model generalization was considered and data was selected such that a good distribution of patient demographics, scanner, and image parameters were represented. The separation into training versus validation datasets is made on the study level to ensure no overlap between the two sets. As such, different scans from the same study were not split between the training and validation datasets. None of the cases used for model validation were used for training the machine learning models.

cvi42 Auto software has a graphical user interface which allows users to analyze cardiac images qualitatively and quantitatively for volume/mass, function and signal intensity changes including a reporting function.

The device can be integrated into a hospital, private practice environment, or medical research institution and provides clinical diagnosis decision support tools for the cardiovascular MR and CT technique.

Additionally, the software is designed to generate 3D view of the heart in CT images for qualitative assessment of the coronary artery. No quantitative assessment can be made from the 3D image.

The software does not interface directly with any data collection equipment; instead, the software uploads data files previously generated by such equipment. Its functionality is independent of the type of vendor acquisition equipment. The analysis results are available on-screen and can be saved within the software for future review.

AI/ML Overview

The provided text describes the acceptance criteria and the study that proves the cvi42 Auto Imaging Software Application meets these criteria.

Here's an organized breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are described as pre-defined performance thresholds for the machine learning models. The reported performance is the achieved accuracy or error rate.

Feature / Metric	Acceptance Criteria (Pre-defined)	Reported Device Performance
CMR Function Analysis
Series Classification Accuracy	Defined by True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN)	97% - 100%
Volumetric Mean Absolute Error (MAE) for SAX	Not explicitly stated but calculated.	7% - 10%
Volumetric Mean Absolute Error (MAE) for LAX	Not explicitly stated but calculated.	5% - 9%
Calcium Analysis
Classification Accuracy	Defined by TP, TN, FP, and FN	86% - 99%
Coronary Analysis
Centerline Quality and Performance	Defined by TP and FN	82% - 94%
Mask Performance	Success rate for relevant masks	98% - 100%

Note: The document states that "All performance testing results met Circle's pre-defined acceptance criteria." While specific numerical "acceptance criteria" are not given for all metrics, the reported performance ranges are implicitly within the accepted thresholds.

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

Total anonymized patient images for validation: n = 235
Breakdown by analysis type (note: total is >235 as some analyses might use overlapping sets or different views from the same patient):
- Coronary Analysis: 70 samples
- Calcium Analysis: 102 samples
- SAX Function Contouring: 63 samples
- 2-CV LAX Function Contouring: 63 samples
- 3-CV LAX Function Contouring: 63 samples
- 4-CV LAX Function Contouring: 63 samples
- Function Classification: 252 samples
Data Provenance: "Across all MR and CT machine manufacturers." "At least 50% of the data came from a U.S. population." The data for validation was explicitly stated to not have been used during the development of the training algorithms, indicating a distinct test set. The document implies a retrospective collection of anonymized patient images for validation.

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

The document does not specify the number or qualifications of experts used to establish the ground truth for the test set. It only mentions that the device is "intended to be used by qualified medical professionals, experienced in examining and evaluating cardiovascular MR or CT images, for the purpose of obtaining diagnostic information as part of a comprehensive decision-making process." This likely refers to the users of the device, not necessarily the ground truth adjudicators for the validation study.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1) for establishing the ground truth on the test set. The results are presented as direct performance metrics against an assumed ground truth, but how that ground truth was derived (e.g., single expert, consensus) is not detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The provided text does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to evaluate how human readers improve with AI vs. without AI assistance. The performance data presented focuses on the algorithm's standalone performance or its semi-automated function.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone study was done. The performance data provided (e.g., classification accuracies, MAE, centerline performance, mask performance) describes the performance of the machine learning algorithms themselves (the "semi-automated machine learning algorithms"), rather than human-AI team performance. The mention of "semi-automatic contouring" and "semi-automatic placement of centerline" implies that the AI assists, but the reported metrics appear to be related to the accuracy of the algorithm's output.

7. Type of Ground Truth Used

The type of ground truth used is not explicitly stated in detail for the validation set. Given the context of "semi-automatic contouring" and "classification accuracy," it is highly probable that the ground truth for contouring (e.g., for heart chambers) would have been established by expert manual segmentation, and for classifications (e.g., calcium presence), it would be based on expert review or established clinical criteria. However, explicit details like "expert consensus" or "pathology" are not mentioned.

8. The Sample Size for the Training Set

The document states: "The data used to train these machine learning algorithms were sourced from multiple clinical sites from urban centers and from different countries." However, the specific sample size (number of images or patients) used for the training set is not provided in the given text.

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

The document mentions that training data was "sourced from multiple clinical sites" and that "the importance of model generalization was considered and data was selected such that a good distribution of patient demographics, scanner, and image parameters were represented." It also differentiates between training and validation datasets by ensuring "no overlap between the two sets."
While it broadly states that data was selected considering generalization, it does not explicitly detail how the ground truth for the training set was established (e.g., expert annotation, clinical reports, etc.).

Summary

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" in a square and the words "U.S. Food & Drug Administration".

Circle Cardiovascular Imaging, Inc. % Sydney Toutant Regulatory Affairs Lead Suite 1100 - 800 5th Ave. SW Calgary, Alberta T2P 3T6 CANADA

7/28/2022

Re: K213998

Trade/Device Name: cvi42 Auto Imaging Software Application Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH, LLZ Dated: June 27, 2022 Received: June 28, 2022

Dear Sydney Toutant:

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 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-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. 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) K213998

Device Name cvi42 Auto Imaging Software Application

Indications for Use (Describe)

The target population for cvi42 Auto's manual workflows is not restricted; however, cvi42 Auto's semi-automated machine learning algorithms are intended for an adult population.

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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Image /page/3/Picture/1 description: The image shows the logo for Circle Cardiovascular Imaging. The logo features a stylized green circle that is not fully closed, with the color transitioning to yellow in the open section. Below the circle, the word "circle" is written in a gray, sans-serif font, and beneath that, the words "CARDIOVASCULAR IMAGING" are written in a smaller, sans-serif font, stacked on top of each other.

The following 510(k) summary of safety and effectiveness information is submitted in accordance with the requirements of the Safe Medical Device Act 1990 and 21 CFR 807.92(c).

l. SUBMITTER

Submitter's Name:	Circle Cardiovascular Imaging Inc.
Address:	Suite 1100 – 800 5th Ave SW, Calgary, AB, Canada, T2P 3T6
Date Prepared:	July 25 2022
Telephone Number:	+1 587 747 4692
Contact Person:	Sydney Toutant
Email:	sydney.toutant@circlecvi.com

II. DEVICE

Name of the Device:	cvi42 Auto Imaging Software Application
Short Brand Name:	cvi42 Auto
Common or Usual Name:	Automated Radiological Image Processing System
Classification Name:	Medical image management and processing system
Proposed Classification:	Device Class: II
	Primary Product Code: QIH
	Secondary Product Code: LLZ
Regulation Number:	21 CFR 892.2050

lll. PREDICATE DEVICES

The primary predicate is cm42 manufactured by Circle Cardiovascular Imaging Inc. under K082628. cvi42, manufactured by Circle Cardiovascular Imaging Inc. under K141480, is used as a secondary predicate device and ct4, manufactured by Circle Cardiovascular Imaging Inc. under K111373, is used as a tertiary predicate device. The predicate devices have not been subject to a design-related recall.

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IV. DEVICE DESCRIPTION

cvi42 Auto uses machine learning techniques to aid in semi-automatic contouring of regions of interest of cardiac magnetic resonance (MR) or computed tomography (CT) images as follows:

1. Cardiac Function: semi-automatic contouring of the four heart chambers (including left ventricle, left atrium, right ventricle, right atrium) in MR images.
1. Calcium Assessment: using pixel intensity technique, identify calcified plaque in major coronary arteries in non-contrast enhanced CT images.
1. Coronary Analysis: semi-automatic placement of centerline in coronary vessels to visualize the coronary arteries and assess stenosis in non-contrast enhanced CT images.

Additionally, the software is designed to generate 3D view of the heart in CT images for qualitative assessment of the coronary artery. No quantitative assessment can be made from the 3D image.

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> INDICATIONS FOR USE

cvi42 Auto is intended to be used for viewing, post-processing, qualitative and quantitative evaluation of cardiovascular magnetic resonance (MR) and computed tomography (CT) images in a Digital Imaging and Communications in Medicine (DICOM) Standard format.

It enables a set of tools to assist physicians in qualitative assessment of cardiac images and quantitative measurements of the heart and adjacent vessels; perform calcium scoring; and to confirm the presence or absence of physician-identified lesion in blood vessels.

The target population for cvi42 Auto's manual workflows is not restricted; however, cvi42 Auto's semi-automated machine learning algorithms are intended for an adult population.

VI. COMPARISON WITH PREDICATE DEVICES

The detailed analysis of the subject device and the primary and secondary predicate devices (shown in Table 1 and Table 2) demonstrates that the subject device is substantially equivalent in indications for use, intended use, technological characteristics, functionality, and operating principles with the primary predicate (K082628) and substantially equivalent in intended use and technological characteristics with the secondary predicate (K141480) and tertiary predicate (K111373). Of the three characteristics (technical, biological, and clinical) required for the demonstration of equivalence, biological characteristics are not applicable since the subject device and the predicate devices are all software as a medical device applications with no tangible component interfacing with the body.

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	Subject Device	Primary Predicate	Secondary Predicate	Tertiary Predicate
	cvi42 Auto (K213998)	cmr42 (K082628)	cvi42 (K141480)	ct42 (K111373)
	Manufactured by Circle	Manufactured by Circle	Manufactured by Circle	Manufactured by Circle
Intended Use	Viewing, post-processing,qualitative and quantitativeevaluation of blood vessels andcardiovascular MR and CT imagesin DICOM format.	Viewing, post-processing,qualitative and quantitativeevaluation of cardiovascular MRimages in DICOM format.	Viewing, post-processing,qualitative and quantitativeevaluation of blood vessels andcardiovascular MR and CT imagesin DICOM format.	Viewing, post-processing,qualitative and quantitativeevaluation of cardiovascular CTimages in DICOM format.
Indications forUse	cvi42 Auto is intended to be usedfor viewing, post-processing,qualitative and quantitativeevaluation of cardiovascularmagnetic resonance (MR) andcomputed tomography (CT) imagesin a Digital Imaging andCommunications in Medicine(DICOM) Standard format.It enables a set of tools to assistphysicians in qualitativeassessment of cardiac images andquantitative measurements of theheart and adjacent vessels;perform calcium scoring; and toconfirm the presence or absence ofphysician-identified lesion in bloodvessels.The target population for cvi42Auto's manual workflows is notrestricted; however, cvi42 Auto'ssemi-automated machine learningalgorithms are intended for an adultpopulation.cvi42 Auto shall be used only forcardiac images acquired from anMR or CT scanner. It shall be usedby qualified medical professionals,experienced in examining and	cmr42 is intended to be used forviewing, post-processing andquantitative evaluation ofcardiovascular magnetic resonance(MR) images in a Digital Imagingand Communications in Medicine(DICOM) Standard format.It enables:• Importing Cardiac MR Images inDICOM format• Supporting clinical diagnostics byqualitative analysis of the cardiacMR images using displayfunctionality such as panning,windowing, zooming, navigationthrough series/slices and phases.• Supporting clinical diagnostics byquantitative measurement of theheart and adjacent vessels incardiac MR images, specificallydistance, area, volume and mass• Supporting clinical diagnostics byusing area and volumemeasurements for measuring LVfunction and derived parameterscardiac output and cardiac index inlong axis and short axis cardiac MRimages.• Flow quantifications based onvelocity encodes images	cvi42 vascular analysis add-on isan image analysis softwarepackage add-on for evaluating CTand MR images of blood vessels.Combining digital image processingand visualization tools such asmultiplaner reconstruction (MPR),thin/think maximum intensityprojection (MIP) thin and think,inverted MIP thin and think, volumerendering technique (VRT), curvedplanner reformation, processingtools such as bone removal (basedon both single energy and dualenergy) table removal andevaluation tools (vessel centerlinecalculation, lumen calculation,stenosis calculation) and reportingtools (lesion location, lesioncharacteristics) and key images),the software package is designedto support the physician inconforming the presence orabsence of physician identifiedlesion in blood vessels andevaluation, documentation andfollow up of any such lesions.It shall be used by qualified medicalprofessionals, experienced inexamining and evaluatingcardiovascular CT or MR images	ct42 is intended to be used forviewing, post-processing andquantitative evaluation ofcardiovascular computedtomography (CT) images in aDigital Imaging andCommunications in Medicine(DICOM) Standard format.It enables:• Importing Cardiac CT Images inDICOM format• Supporting clinical diagnostics byqualitative analysis of the cardiacCT images using displayfunctionality such as panning,windowing, zooming, navigationthrough series/slices and phases,3D reconstruction of imagesincluding multi-lannerreconstructions of the images.• Supporting clinical diagnostics byquantitative measurement of theheart and adjacent vessels incardiac CT images, specificallydistance, area, volume and mass• Supporting clinical diagnostics byusing area and volumemeasurements for measuring LVfunction and derived parameterscardiac output and cardiac index inlong axis and short axis cardiac CTimages
Subject Device	Primary Predicate	Secondary Predicate	Tertiary Predicate
cvi42 Auto (K213998)	cmr42 (K082628)	cvi42 (K141480)	ct42 (K111373)
Manufactured by Circle	Manufactured by Circle	Manufactured by Circle	Manufactured by Circle
evaluating cardiovascular MR orCT images, for the purpose ofobtaining diagnostic information aspart of a comprehensive diagnosticdecision-making process.	It shall be used by qualified medicalprofessionals, experienced inexamining and evaluatingcardiovascular MR images, for thepurpose of obtaining diagnosticinformation as part of acomprehensive diagnostic decision-making process. cmr42 is a softwareapplication that can be used as astand-alone product or in anetworked environment.	for the purpose of obtainingdiagnostic information as part of acomprehensive diagnostic decision-making process. cvi42 is a softwareapplication that can be used as astand-alone product or in anetworked environment.The target population for the cvi42is not restricted.	• Supporting clinical diagnostics byquantitative measurements ofcalcified plaques in the coronaryarteries (calcium scoring),specifically Agatston and volumeand mass calcium scoresIt shall be used by qualifiedmedical professionals, experiencedin examining and evaluatingcardiovascular CT images, for thepurpose of obtaining diagnostic
	The target population for the cmr42is not restricted, however the imageacquisition by a cardiac magneticresonance scanner may limit theuse of the device for certain sectorsof the general public.		information as part of acomprehensive diagnosticdecision-making process. ct42 is asoftware application that can beused as a stand-alone product orin a networked environment.
	cmr42 shall not be used to view oranalyze images of any part of thebody except the cardiac magneticresonance images acquired from acardiovascular magnetic resonancescanner.		The target population for the ct42 isnot restricted, however the imageacquisition by a cardiac CTscanner may limit the use of thedevice for certain sectors of thegeneral public.
			ct42 shall not be used to view oranalyze images of any part of thebody except the cardiac CTimages acquired from acardiovascular CT scanner.
Feature	Subject Devicecvi42 Auto (K213998)Manufactured by Circle	Primary Predicatecmr42 (K082628)Manufactured by Circle	Secondary Predicatecvi42 (K141480)Manufactured by Circle	Tertiary Predicatect42 (K111373)Manufactured by Circle
Device Class	II	II	II	II
Device Classification	QIH, LLZ	LLZ	LLZ	LLZ
Regulation Name	Medical image managementand processing system	Picture Archiving andCommunications System	Picture Archiving andCommunications System	Picture Archiving andCommunications System
Regulation Number	21 CFR 892.2050	21 CFR 892.2050	21 CFR 892.2050	21 CFR 892.2050
Imaging Modalities	MR and CT	MR	MR and CT	CT
DICOM Compliant	Yes	Yes	N/A	N/A
Import and displayMR/CT images	Yes	Yes (MR only)	Yes	Yes (CT only)
Post process CMR/CCTimages	Yes	Yes (MR only)	Yes	Yes (CT only)
Images can bedisplayed by study andseries	Yes	Yes	N/A	N/A
Store images	Yes	Yes	N/A	N/A
2D Imaging	Yes	Yes	N/A	N/A
3D Imaging	Yes	No	Yes	Yes
Multiplanar Reformat(MPR)	Yes	No	Yes	Yes
Navigation Tools	Panning,Windowing,ZoomingSeries/slices and phases	Panning,Windowing,ZoomingSeries/slices and phases	N/A	N/A
Measurements	DistancePerimeterAreaSignal IntensityVolume	DistancePerimeterAreaSignal IntensityVolume	N/A	N/A
Quantitative assessmentof cardiac function	Manual segmentation, andsemi-automatic segmentationusing Machine Learningtechnique of four heartchambers in long and short-axis views	Manual segmentation of fourheart chambers in long andshort-axis views	Manual segmentation,and semi-automaticsegmentation of fourheart chambers in longand short-axis views	Manual segmentation,and semi-automaticsegmentation of fourheart chambers in short-axis views
Feature	Subject Device	Primary Predicate	Secondary Predicate	Tertiary Predicate
	cvi42 Auto (K213998)	cmr42 (K082628)	cvi42 (K141480)	ct42 (K111373)
	Manufactured by Circle	Manufactured by Circle	Manufactured by Circle	Manufactured by Circle
Centerline placement incoronary vessels	Manual and semi-automaticusing Machine Learningtechnique	Manual	Manual and semi-automatic	Manual and semi-automatic
Calcium Scoring	Yes, using ML methodology	No	No	Yes, using non-MLmethodology
Workstation operating	macOS,Microsoft Windows	macOS,Microsoft Windows	macOS,Microsoft Windows	macOS,Microsoft Windows
system

Table 1. Comparison to the predicate devices.

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Table 2. Feature comparison table of cvi42 Auto with the predicate devices. Cells marked as N/A are features already supported by the primary predicate.

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VII. PERFORMANCE DATA AND TESTING

Performance testing was conducted to verify compliance with specified design requirements in accordance with ISO 13485:2016, IEC 62304:2015, ISO 14971:2019 and NEMA 3.1-3.20 (2016) DICOM standards.

Verification and validation testing were conducted to ensure specifications and performance of the device and were performed per the FDA Guidance 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".

cvi42 Auto has been tested according to the specifications that are documented in a Master Software Test Plan. Testing is an integral part of Circle Cardiovascular Imaging Inc software development process as described in the company's product development process.

Validation of Machine Learning Derived Outputs

The machine learning algorithms of cvi42 Auto (MR-CMR Function, CT-Coronary, and CT-Calcium) have been trained and tested on images acquired from major vendors of MR and CT imaging devices. All data used for validation were not used during the development of the training algorithms.

Across all MR and CT machine manufacturers, n = 235 anonymized patient images were used for the validation of cvi42 Auto. This translates into 70 samples for Coronary Analysis, 102 samples for Calcium analysis, 63 samples for SAX Function contouring, 63 for each of 2-CV, 3-CV, and 4CV LAX function contouring, and 252 samples for Function Classification. Image information for all samples was anonymized and limited to ePHI-free DICOM headers. At least 50% of the data came from a U.S. population.

All performance testing results met Circle's pre-defined acceptance criteria.

For CMR function analysis, the performance acceptance criteria were pre-defined to . evaluate the performance of the ML model based on classification accuracy defined by true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). Mean volume prediction error (Mean Absolute Error, or MAE) was also calculated. Series classification performance results were between 97 % - 100%. Volumetric MAE for SAX were between 7% - 10%, and volumetric MAE for LAX were between 5% - 9%.
. For Calcium analysis, the performance acceptance criteria were pre-defined to evaluate the performance of the ML model based on classification accuracy defined by TP. TN, FP, and FN. Classification performance results were between 86% - 99%.
. For Coronary analysis, the performance acceptance criteria were pre-defined to evaluate the centerline quality and performance (based on TP and FN), and success rate for relevant masks. Centerline performance results were between 82% - 94%. Mask performance results were between 98% - 100%.

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VIII. CONCLUSIONS

The information submitted in this premarket notification, including the performance testing and predicate device comparisons, supports the safety and effectiveness of cvi42 Auto as compared to the predicate devices when used for the defined intended use.

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).