The MedCognetics (CogNet) QmTRIAGE™ software is a passive notification-only, parallel-workflow software tool used by MQSA qualified interpreting physicians to prioritize patients with suspicious findings in the medical care environment. QmTRIAGE™ utilizes an artificial intelligence algorithm to analyze 2D FFDM screening mammograms and flags those that are suggestive of the presence of at least one suspicious finding at the exam level. QmTRIAGE™ produces an exam level output to a PACS/Workstation for flagging the suspicious study and allows for worklist prioritization.

MQSA qualified interpreting physicians are responsible for reviewing each exam on a display approved for use in mammography, according to the current standard of care. The OmTRIAGE™ device is limited to the categorization of exams, does not provide any diagnostic information beyond triage and prioritization, does not remove images from the interpreting physician's worklist, and should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis.

The QmTRIAGE™ device is intended for use with complete 2D FFDM mammography exams acquired using validated FFDM systems only.

Device Description

The MedCognetics (CogNet) QmTRIAGE is a non-invasive computer-assisted triage and notification software as a medical device (SaMD) that analyzes 2D FFDM screening mammograms using a machine learning algorithm and notifies a PACS/workstation of the presence of findings suspicious of cancer in a study. The passive-notification enables radiologists to prioritize their worklist and assists them in viewing prioritized studies using the standard PACS or workstation viewing software. The device aim is to aid in the prioritization and triage of radiological medical images only. It is a software tool for MQSA interpreting physicians reading mammograms and does not replace complete evaluation according to the standard of care.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study details for the MedCognetics CogNet QmTRIAGE device, based on the provided text:

Acceptance Criteria and Reported Device Performance

Criteria	Acceptance Criteria (Implied)	Reported Device Performance
AUROC	High AUROC value for overall performance.	0.9569 (95% CI: 0.9364-0.9738)
Sensitivity	Exceed the standard of care (e.g., BCSC study reported values).	87%
Specificity	Exceed the standard of care (e.g., BCSC study reported values).	89%
Triage Accuracy	Demonstrated accuracy across various cohorts (lesion type, breast density, age, race).	Measured and validated. Specific values not provided in this summary, but the study notes confounding factors were considered.

Note: The document explicitly states the reported Sensitivity (87%) and Specificity (89%) "exceeded the standard of care as reported in the Breast Cancer Surveillance Consortium (BCSC) study," implying this was the acceptance benchmark.

Study Details

Sample Size used for the test set and data provenance:
- Sample Size: 800 anonymized 2D FFDM mammograms.
- Data Provenance: Retrospective cohort from various countries, specifically mentioned USA and Germany. (Training data was obtained from various sites worldwide including North America, South America, Europe, Africa, and Southeast Asia, but the test set is specified as USA and Germany.)
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- The document does not specify the number of experts used to establish ground truth for the test set.
- Qualifications: The ground truth for cancer cases was based on biopsy confirmation, and for negative cases (BI-RADS 1 and 2), it was established by a two-year follow-up of a negative diagnosis. While MQSA qualified interpreting physicians are mentioned in the intended use for reviewing exams, their direct role in establishing ground truth for the test set is not explicitly detailed.
Adjudication method for the test set:
- The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). The ground truth relies on objective medical outcomes: biopsy confirmation for positive cases and 2-year follow-up for negative cases.
If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:
- No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not described. The study focuses on the standalone performance of the AI algorithm for triage. The device is described as a "passive notification-only, parallel-workflow software tool" to "prioritize patients," not as an aid that directly impacts a reader's diagnostic performance measured against a baseline.
If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance study was done. The reported AUROC, Sensitivity, and Specificity values are for the algorithm's performance independent of human-in-the-loop interaction. The device's role is to "prioritize their worklist and assists them in viewing prioritized studies," implying its standalone functionality in flagging.
The type of ground truth used:
- Pathology/Biopsy: For positive cases (399 cases positive for cancer), ground truth was established by biopsy confirmation.
- Outcomes Data/Clinical Follow-up: For negative cases (401 cases negative for breast cancer, BI-RADS 1 and 2), ground truth was established by a two-year follow-up of a negative diagnosis.
The sample size for the training set:
- The exact sample size for the training set is not provided in the summary. It only states that "Data sets used for training the algorithm were independent of the testing datasets and were obtained from various sites worldwide."
How the ground truth for the training set was established:
- The document does not explicitly detail how the ground truth for the training set was established. It only mentions the training data was obtained from various sites worldwide and was independent of the testing dataset. Given the nature of breast cancer screening AI, it can be inferred that similar methods (biopsy, follow-up, or expert review) would have been used, but it's not stated.

Summary

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September 29, 2022

Image /page/0/Picture/1 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".

MedCognetics, Inc. % Diane Rutherford Regulatory Affairs Manager 17217 Waterview Parkway Suite 1.202E DALLAS TX 75252

Re: K220080

Trade/Device Name: CogNet OmTRIAGE Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: August 30, 2022 Received: August 30, 2022

Dear Diane Rutherford:

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

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801); medical device reporting of medical device-related adverse events) (21 CFR 803) 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,

Yanna Kang, Ph. D.

Acting Assistant Director Mammography and Ultrasound Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2023 See PRA Statement below.

510(k) Number (if known) K220080

Device Name CogNet QmTRIAGETM

Indications for Use (Describe)

The QmTRIAGE™ device is intended for use with complete 2D FFDM mammography exams acquired using validated FFDM systems only.

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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5. 510(k) SUMMARY

a)

Submitter:	MedCognetics, Inc.Mr. Debasish “Ron” NagChief Executive Officer & President17217 Waterview Parkway, Suite 1.202EDallas, TX 75252 USAron@medcognetics.com
Contact Person:	MedCognetics, Inc.Ms. Diane RutherfordRegulatory Affairs Manager17217 Waterview Parkway, Suite 1.202EDallas, TX 75252 USATEL: 214-390-6569diane@medcognetics.com
Date Prepared:	August 30, 2022 (revised)
b)
Trade Name:	CogNet QmTRIAGE
Common Name:	Radiological computer aided triage and notification software
Classification Name:	Radiological computer aided triage and notification software
Product Code:	QFM	Class 2	Regulation Number 892.2080
c)
Predicate Devices:	K200905 Zebra Medical Vision Ltd. HealthMammo Cleared July 16, 2020
d)Device Description:	The MedCognetics (CogNet) QmTRIAGE is a non-invasive computer-assistedtriage and notification software as a medical device (SaMD) that analyzes 2DFFDM screening mammograms using a machine learning algorithm and notifies aPACS/workstation of the presence of findings suspicious of cancer in a study. Thepassive-notification enables radiologists to prioritize their worklist and assists themin viewing prioritized studies using the standard PACS or workstation viewingsoftware. The device aim is to aid in the prioritization and triage of radiologicalmedical images only. It is a software tool for MQSA interpreting physicians readingmammograms and does not replace complete evaluation according to the standardof care.Data sets used for training the algorithm were independent of the testing datasetsand were obtained from various sites worldwide including North America, SouthAmerica, Europe, Africa, and Southeast Asia.
e)Statement ofIntended Use:	The MedCognetics (CogNet) QmTRIAGE™ software is a passive notification forprioritization-only, parallel-workflow software tool used by MQSA qualifiedinterpreting physicians to prioritize patients with suspicious findings in the medicalcare environment. QmTRIAGE™ utilizes an artificial intelligence algorithm toanalyze 2D FFDM screening mammograms and flags those that are suggestive of

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the presence of at least one suspicious finding at the exam level. QmTRIAGE™ produces an exam level output to a PACS/Workstation for flagging the suspicious study and allows for worklist prioritization.

MQSA qualified interpreting physicians are responsible for reviewing each exam on a display approved for use in mammography, according to the current standard of care. The QmTRIAGE™ device is limited to the categorization of exams, does not provide any diagnostic information beyond triage and prioritization, does not remove images from the interpreting physician's worklist, and should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis.

The QmTRIAGE™ device is intended for use with complete 2D FFDM mammography exams acquired using validated FFDM systems only.

Summary of Technological Characteristics:

CogNet QmTRIAGE shares technological characteristics with the predicate device. Both are classified under the same product code and regulation, have the same intended use, flag suspicious images at the study/exam level, are parallel workflow processes that do not alter the original image, are non-contact, and are SaMD using AI for analysis.

The proposed device also has minor differences in technological characteristics from that of the predicate device. While both the proposed device and the predicate offer cloud-based analysis, the predicate also offers an on-premise option. The differences in the technological characteristics are minor and reflect market strategy and/or perceived user preferences and do not impact the safety, effectiveness, or substantial equivalence of the device.

TechnologicalCharacteristics	New Device [K220080]CogNet QmTRIAGE - MedCognetics	Predicate Device [K200905]HealthMammo - Zebra Medical Vision Ltd.	Status
Indication for Use/ Intended Use	The MedCognetics (CogNet) QmTRIAGEsoftware is a passive notification forprioritization-only, parallel-workflow softwaretool used by MQSA qualified interpretingphysicians to prioritize patients with suspiciousfindings in the medical care environment.QmTRIAGE utilizes an artificial intelligencealgorithm to analyze 2D FFDM screeningmammograms and flags those that aresuggestive of the presence of at least onesuspicious finding at the exam level.QmTRIAGE produces an exam level output to aPACS/Workstation for flagging the suspiciousstudy and allows for worklist prioritization.MQSA qualified interpreting physicians areresponsible for reviewing each exam on adisplay approved for use in mammography,according to the current standard of care. TheQmTRIAGE device is limited to thecategorization of exams, does not provide anydiagnostic information beyond triage andprioritization, does not remove images fromthe interpreting physician's worklist, andshould not be used in lieu of full patientevaluation, or relied upon to make or confirmdiagnosis.The QmTRIAGE device is intended for use withcomplete 2D FFDM mammography examsacquired using validated FFDM systems only.	The Zebra HealthMammo is a passivenotification for prioritization-only, parallel-workflow software tool used by MQSA-qualified interpreting physicians to prioritizepatients with suspicious findings in themedical care environment. HealthMammoutilizes an artificial intelligence algorithm toanalyze 2D FFDM screening mammogramsand flags those that are suggestive of thepresence of at least one suspicious finding atthe exam-level. HealthMammo produces anexam level output to a PACS/Workstation forflagging the suspicious case and allowsworklist prioritization.MQSA-qualified interpreting physicians areresponsible for reviewing each exam on adisplay approved for use in mammographyaccording to the current standard of care.HealthMammo device is limited to thecategorization of exams, does not provide anydiagnostic information beyond triage andprioritization, does not remove images fromthe interpreting physician's worklist, andshould not be used in lieu of full patientevaluation, or relied upon to make or confirmdiagnosis.The HealthMammo device is intended for usewith complete 2D FFDM mammographyexams acquired using validated FFDM systemsonly.	Same
Notification Only	Yes	Yes	Same
Parallel Workflow	Yes	Yes	Same
User	Interpreting physician	Interpreting physician	Same
Alert to finding	Yes.Passive notification flagged for review	Yes.Passive notification flagged for review	Same

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K220080

TechnologicalCharacteristics		New Device [K220080]CogNet QmTRIAGE - MedCognetics	Predicate Device [K200905]HealthMammo - Zebra Medical Vision Ltd.	Status
Independent ofSoC workflow		Yes.No cases are removed from worklist	Yes.No cases are removed from worklist	Same
Modality		FFDM screening mammograms	FFDM screening mammograms	Same
FFDMManufacturer		Hologic	Hologic	Same
Body Part		Breast	Breast	Same
Al algorithm		Yes	Yes	Same
Limited to analysisof imaging data		Yes	Yes	Same
Performance Study	InclusionCriteria	Standard 2D FFDM screeningmammogramsBiopsy proven cancer studies studies (softtissues and microcalcifications)BIRADS 1 and 2 normal/benign cases with2-year follow-up of a negative diagnosisFemale patients 22 and olderBilateral Studies with 4 standard views(LCC, LMLO, RCC, RMLO)	2D FFDM screeningmammogramsBiopsy proven cancer studies(soft tissues and microcalcifications)BIRADS 1 and 2 normal cases with 2 yearfollow-upStudies with 4 standard views (LCC,LMLO, RCC, RMLO) BIRADS 1 and 2normal cases with 2 year follow-up	Different
	ExclusionCriteria	Digital breast tomosynthesis images2D synthetic views from tomosynthesis	Digital Breast tomosynthesis studies3D studiesStudies that did not include all four viewsStudies that do not comply with theinclusion criteria	Different
	MultipleOperatingpoints	Not Applicable	Yes. Three optional operating points	Different
Aids in promptidentification ofcases with indicatedfindings		Yes	Yes	Same
Results Preview		The device operates in parallel with thestandard of care, which remains the defaultoption for all cases. Encapsulated PDF storedwith original DICOM study and may bedownloaded and viewed as a PDF.	Presentation of notification and preview ofthe study for initial assessment not meant fordiagnostic purposes. The device operates inparallel with the standard of care, whichremains the default option for all cases.	Equivalent
Deployment		Cloud based	Cloud basedOn-premise option	Different
Where results arereceived		PACS / Workstation	PACS / Workstation	Same

Summary of Performance Testing:

The software was developed and validated in accordance with design controls and software documentation requirements for medical devices.

CogNet QmTRIAGE utilizes an artificial intelligence (AI) algorithm. The validation of the performance of MedCognetics' QmTRIAGE algorithm for triage of 2D FFDM achieved an overall Area Under Receiver Operating Characteristics (AUROC) of 0.9569 with 95% CI: 0.9364-0.9738 across the entire test dataset, without subgroup breakdown.

Also validated was Sensitivity and Specificity, achieving an overall Sensitivity of 87% and a Specificity of 89% across the entire test dataset, without subgroup breakdown, which exceeded the standard of care as reported in the Breast Cancer Surveillance Consortium (BCSC) study.

The performance of the MedCognetics' QmTRIAGE has been validated for triage of 2D FFDM in mammogram cases. The study data included a retrospective cohort of 800 anonymized 2D FFDM mammograms from the USA and Germany, including 399 cases positive for cancer with biopsy confirmation and 401 cases negative for breast cancer (BI-RADS1 and BI-RADS2) with a two-year follow-up of a negative diagnosis. The test dataset excludes screening BI-RADS 0 cases that were determined to be benign after diagnostic workup.

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The mammogram cases were all from female patients, ages 22 to 80+ with the median age group being the 50-59 group. Ethnicities represented included American Indian, Asian, Black (non-Hispanic), Hispanic, and White (non-hispanic).

Imaging from Hologic, GE, Siemens, and Fujifilm, were used in the performance study and design validation but the complete vendor/model information was not provided with all images. The Hologic Selenia Dimension has been identified as the vendor/model for the initial release.

The performance test was constructed to ensure that confounding factors that are present in the population are addressed in the data such that it is consistent with the population of women undergoing breast cancer screening examination. The confounding factors that were considered include 1) lesion type, 2) breast density 3) age and 4) race. The triage accuracy was measured for these cohorts against the ground-truth.

Independence of test and training data was ensured by storing testing data in an isolated storage location. Once the relevant clinical sites had been identified for inclusion in the test set, all data from these sites were isolated into a controlled storage space. Data in this controlled storage space is only made available when conducting the performance test, ensuring total independence of the test set. Secondary software checks are also implemented against the list of cases in the training and test sets to further guarantee test set independence.

MedCognetics considers CogNet QmTRIAGE to be substantially equivalent to the Conclusion: predicate device listed above. This conclusion is based on the similarities in primary intended use, principles of operation, functional design, and established medical use and performance testing of CogNet OmTRIAGE which demonstrated adequate performance for the device in line with its intended use.

Regulation Number and Section

§ 892.2080 Radiological computer aided triage and notification software.

(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.