The qER-Quant device is intended for automatic labeling, visualization of segmentable brain structures from a set of Non-Contrast head CT (NCCT) images. The software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on NCCT images. qER-Quant provides volumes from NCCT images acquired at a single time point and provides a table with comparative analysis for two or more images that were acquired on the same image acquisition protocol for the same individual at multiple time points.

The qER-Quant software is indicated for use in the following structures: Intracranial Hyperdensities, Lateral Ventricles and Midline Shift.

Device Description

qER-Quant is a standalone software device that processes non-contrast head CT scans to outline and quantify the structures described in the intended use. The qER-Quant software interacts with the user's picture archiving and communication system (PACS) to receive scans and returns the results to the same destination.

The analysis module of the qER-Quant software contains of a set of pre-trained convolutional neural networks (CNNs), that form the core processing component shown in Figure 1. This core processing component is coupled with a pre-processing module to prepare input digital imaging and communications in medicine (DICOMs) for processing by the CNNs and a post-processing module to convert the output into visual and tabular output for users.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for the qER-Quant device, based on the provided text:

qER-Quant Device Performance Study Details

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria were defined based on the accuracy of the qER-Quant system when compared against manually labeled ground truth. The reported device performance met these pre-set criteria.

Metric	Acceptance Criteria (Implied / Context)	Reported Device Performance (Mean ± SD / Mean (95% CI) / Median (10th-90th Percentile))
Intracranial Hyperdensity
Absolute Error (Volume)	Exceeds preset acceptance criteria	6.56 (7.33) ml (Mean ± SD) 3.98 (0.52 - 17.35) ml (Median (10th - 90th percentile))
Dice Score (Segmentation Accuracy)	Exceeds preset acceptance criteria	0.75 (0.72 - 0.78) (Mean (95% CI))
Midline Shift
Absolute Error (Shift)	Exceeds preset acceptance criteria	1.37 (1.23) mm (Mean ± SD) 1.15 (0.23 - 2.59) mm (Median (10th - 90th percentile))
Dice Score (Segmentation Accuracy)	Not Applicable	Not applicable
Left Lateral Ventricle
Absolute Error (Volume)	Exceeds preset acceptance criteria	2.09 (1.88) ml (Mean ± SD) 1.60 (0.29 - 4.24) ml (Median (10th - 90th percentile))
Dice Score (Segmentation Accuracy)	Exceeds preset acceptance criteria	0.79 (0.78 - 0.81) (Mean (95% CI))
Right Lateral Ventricle
Absolute Error (Volume)	Exceeds preset acceptance criteria	2.18 (1.72) ml (Mean ± SD) 1.88 (0.40 - 4.53) ml (Median (10th - 90th percentile))
Dice Score (Segmentation Accuracy)	Exceeds preset acceptance criteria	0.75 (0.73 - 0.77) (Mean (95% CI))

2. Sample Size and Data Provenance

Test Set Sample Sizes:
- Intracranial Hyperdensity: 183 scans
- Midline Shift: 188 scans
- Left Lateral Ventricle: 210 scans
- Right Lateral Ventricle: 210 scans
- Reproducibility testing was done on 20% of these CT scans.
Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It uses "a set of head CT scans."

3. Number of Experts and Qualifications for Ground Truth Establishment

Number of Experts: The document states "experts" (plural) were used but does not specify the exact number.
Qualifications of Experts: Not specified beyond being "experts" in the context of manually labeling CT scans.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). It only mentions that the ground truth was established by "manually labeled by experts."

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

No, an MRMC comparative effectiveness study was not reported. The performance testing was a "standalone" evaluation of the device's accuracy against expert-generated ground truth.

6. Standalone Performance (Algorithm Only)

Yes, a standalone performance study was conducted. The document states: "Qure.ai performed standalone consisted of a set of head CT scans with the outlines of the target structures manually labeled by experts." The results detailed in Table 2 are of this standalone performance.

7. Type of Ground Truth Used

The ground truth used was expert consensus / manual labeling. The document clearly states: "manually labeled by experts."

8. Sample Size for the Training Set

The document does not provide the sample size for the training set. It only describes the architecture of the analysis module as "a set of pre-trained convolutional neural networks (CNNs)."

9. How Ground Truth for the Training Set Was Established

The document does not explicitly state how the ground truth for the training set was established. It describes the CNNs as "pre-trained," which implies a training phase using labeled data, but the method of ground truth establishment for that specific data is not detailed.

Summary

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July 30, 2021

Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

Qure.ai Technologies % Pooja Rao Head, Research and Development Level 7, Commerz II, International Business park, Oberoi Garden City, Goregaon(E) Mumbai, Maharashtra 400063 INDIA

Re: K211222

Trade/Device Name: qER-Quant Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing systems Regulatory Class: Class II Product Code: QIH Dated: June 30, 2021 Received: July 1, 2021

Dear Pooja Rao:

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,

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) K211222

Device Name qER-Quant

Indications for Use (Describe)

same individual at multiple time points.

The qER-Quant software is indicated for use in the following structures: Intracranial Hyperdensities, Lateral Ventricles and Midline Shift.

Type of Use (Select one or both, as applicable)X Prescription Use (Part 21 CFR 801 Subpart D)

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

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K211222

510(k) SUMMARY

Qure.ai's qER-Quant

1.1 Submitter

Qure.ai Technologies Level 7, Commerz II, International Business Park Oberoi Garden City, Goregaon (E), Mumbai 400 063 Phone: +91-9820474098 Contact Person: Pooja Rao

Date Prepared: April 20, 2021

1.2 Device

Name of Device:	qER-Quant
Common or Usual Name:	Automated Radiological Image Processing Software
Classification Name:	Medical image management and processing system
Regulatory Class:	Class II
Regulation Number:	21 CFR 892.2050
Product Code:	QIH

1.3 Predicate Device

Name of Device:	Icobrain
Manufacturer:	Icometrix NV
510(k) Number:	K181939

Intended Use / Indications for Use:

The qER-Quant device is intended for automatic labeling, visualization and quantification of segmentable brain structures from a set of Non-Contrast head CT (NCCT) images. The software is

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intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on NCCT images.

qER-Quant provides volumes from NCCT images acquired at a single time point and provides a table with comparative analysis for two or more images that were acquired on the same scanner with the same image acquisition protocol for the same individual at multiple time points.

The qER-Quant software is indicated for use in the analysis of the following structures: Intracranial Hyperdensities, Lateral Ventricles and Midline Shift.

1.4 Device Description

Image /page/4/Figure/6 description: The image shows a flowchart of an analysis process. The process starts with DICOM head CT scans, which are then pre-processed, core processed, and post-processed. The post-processing step leads to two outputs: a PDF containing a quantification table and preview images, and a DICOM overlay with labeled outlines.

Figure 1: Schematic showing qER-Quant design and workflow

CT scans are sent to qER-Quant by means of transmission functions within the user's PACS system. Upon completion of processing, the qER-Quant device returns results to the user's PACS or other user-specified radiology software system or database.

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The inputs to qER-Quant are non-contrast head CT scans in DICOM format. The plain axial series of the input DICOM file is used for processing. The qER-Quant device produces PDF and DICOM format outputs that enable users to view the quantification results in visual and table form.

PDF format output consists of a table with volumes of the quantified structure and selected preview images showing representative CT scan slices. If more than one CT scans from the same subject and the same scanner is available, qER-Quant performs a comparison between the scans, and returns a longitudinal comparison with a graphic illustrating the changes in absolute volume and size of the quantified structures over time.

DICOM format output consists of a complete additional series with labeled overlays indicating the location and extent of the quantified structures.

1.5 Comparison with Predicate Device

Like the predicate device, qER-Quant is intended for automatic labeling, visualization and quantification of segmentable brain structures. The devices both take DICOM format 3D images of the brain as input and generate an electronic report in PDF and DICOM formats with similar quantitative information. The primary difference is that qER-Quant operates only on NCCT scans, while the predicate device operates on both MRI and NCCT scans. The table below compares qER-Quant with the predicate device, and lists the similarities and differences between them. The minor differences do not raise any new questions of safety or effectiveness.

	qER-Quant DeviceSubject Device	Predicate Device
Device Name	qER-Quant	Icobrain
510(k) Number	K211222	K181939
Regulation	21 CFR 892.2050	21 CFR 892.2050
Product Code	QIH	LLZ
RegulationDescription	Medical image management andprocessing system	Picture archiving and communicationssystem
Device type	Automated Radiological ImageProcessing Software	Radiological Image Processing System
Manufacturer	Qure.ai Technologies	icometrix NV
Overview of Similarities between qER-Quant and the Predicate Device
	qER-Quant DeviceSubject Device	Predicate Device
Intended Use/Indications for Use	The qER-Quant device is intended forautomatic labeling, visualization andquantification of segmentable brainstructures from a set of Non-Contrasthead CT (NCCT) images. The software isintended to automate the currentmanual process of identifying, labelingand quantifying the volume ofsegmentable brain structures identifiedon NCCT images.qER-Quant provides volumes from NCCTimages acquired at a single time pointand provides a table with comparativeanalysis for two or more images thatwere acquired on the same scanner withthe same image acquisition protocol forthe same individual at multiple timepoints.The qER-Quant software is indicated foruse in the analysis of the followingstructures: Abnormal IntracranialHyperdensities, Lateral Ventricles andMidline Shift.	The Icobrain device is intended forautomatic labeling, visualization andvolumetric quantification of segmentablebrain structures from a set of MR or NCCTimages. This software is intended toautomate the current manual process ofidentifying, labeling and quantifying thevolume of segmentable brain structuresidentified on MR or NCCT images.Icobrain consists of two distinct imageprocessing pipelines: icobrain cross andicobrain long.icobrain cross is intended to providevolumes from MR or NCCT images acquiredat a single time point.icobrain long is intended to provide changesin volumes between two MR images thatwere acquired on the same scanner, withthe same image acquisition protocol andwith same contrast at two differenttimepoints.The results of icobrain cross cannot becompared with the results of icobrain long.
TechnologicalCharacteristics	- Software package- Operates on off-the-shelf hardware(multiple vendors)- DICOM compatible- Segmentation by deep learning(supervised voxel classification withConvolutional Neural Networks)	- Software package- Operates on off-the-shelf hardware(multiple vendors)- DICOM compatible- Segmentation by classical machinelearning and deep learning (supervisedvoxel classification with ConvolutionalNeural Networks)
Output	Multiple electronic reports withvolumetric information of brainstructures and midline shift ANDAnnotated DICOM Images	Multiple electronic reports with volumetricinformation of brain structures and midlineshift ANDAnnotated DICOM Images
ReferenceStandard forPerformancetesting	AccuracyManually labeled images for allstructures	AccuracyManually labelled or simulated ground truthfor MRI imagesManually labeled images (lesions andmidline shift) and images labeled bypreviously cleared Icobrain MRI software forCT images (lateral ventricles and wholebrain)
	qER-Quant DeviceSubject Device	Predicate Device
	Test-retest images	MRI measurement changes compared ontest-retest images; simulation study usedfor CT measurements
Comparison of Differences between qER-Quant and the predicate device
Input Images	Non-contrast CT from a single ormultiple time points	T1-weighted and fluid-attenuated inversionrecovery (FLAIR) MR images from a single ormultiple time points and/orNon-contrast CT from a single time point
Target structuresanalyzed on NCCTscans	Intracranial hyperdensities, lateralventricles and midline shift	Intracranial hyperdensities, lateralventricles, basal cisterns and midline shift

Table 1: Comparison between qER-Quant and the Predicate Device

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1.6 Testing

Software

Software verification and validation testing were conducted and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software for this device has a Moderate level of concern.

Performance Testing

Qure.ai performed standalone consisted of a set of head CT scans with the outlines of the target structures manually labeled by experts. Volume or shift measurement accuracy and segmentation accuracy were reported for the target structures. Reproducibility testing was performed using 20% of these CT scans, labeled similarly, with accuracy measured using similar metrics. For all target structures, the standalone performance exceeded the preset acceptance criteria. The table below shows a summary of the results of performance testing.

	Absolute error versus ground truth(volume in ml or shift in mm)		Dice Score
Structure (Number of Scans)	Mean (StandardDeviation)	Median (10th -90th percentile)	Mean (95%confidence interval)
Intracranial Hyperdensity (183)	6.56 (7.33) ml	3.98 (0.52 - 17.35)ml	0.75 (0.72 - 0.78)
Midline Shift (188)	1.37 (1.23) mm	1.15 (0.23 - 2.59)mm	Not applicable

Table 2: Results of performance testing

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Left Lateral Ventricle (210)	2.09 (1.88) ml	1.60 (0.29 - 4.24)ml	0.79 (0.78 - 0.81)
Right Lateral Ventricle (210)	2.18 (1.72) ml	1.88 (0.40 - 4.53)ml	0.75 (0.73 - 0.77)

qER-Quant also passed software validation and system verification checks.

1.7 Conclusion

The comparison in Table 1 and the software and performance testing presented above demonstrate that the qER-Quant device is substantially equivalent to the predicate device.

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).