Who is the manufacturer of qER-Quant?

Qure.Ai Technologies filed the 510(k) submission for qER-Quant (K211222).

What is the FDA product code for qER-Quant?

QIH. It is classified under 21 CFR 892.2050 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for qER-Quant?

510(k) clearance (submission type: Traditional).

Does qER-Quant use AI or machine learning?

Yes. qER-Quant (K211222) is flagged as an AI/ML-enabled medical device in the FDA's AI/ML device list.

Who can help prepare an FDA 510(k) submission like qER-Quant?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

Can an AI/ML device like qER-Quant get a guaranteed FDA 510(k) clearance?

Yes. Innolitics offers a guaranteed FDA 510(k) clearance engagement for AI/ML Software as a Medical Device — combining engineering and regulatory execution under one agreement with a guaranteed clearance outcome. Eligibility: the device must be an AI/ML Software as a Medical Device (SaMD), and its performance must meet the criteria Innolitics determines during an initial regulatory strategy engagement to be necessary for FDA clearance. See https://innolitics.com/services/end-to-end-samd/ or contact https://innolitics.com/contact.

qER-Quant

K211222 · Qure.Ai Technologies · QIH · Jul 30, 2021 · Radiology

Device Facts

Record ID	K211222
Device Name	qER-Quant
Applicant	Qure.Ai Technologies
Product Code	QIH · Radiology
Decision Date	Jul 30, 2021
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2050
Device Class	Class 2
Attributes	AI/ML, Software as a Medical Device

Intended 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 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.

Device Story

qER-Quant is a standalone software device processing non-contrast head CT (NCCT) scans to identify, label, and quantify brain structures. It integrates with hospital PACS to receive DICOM inputs; processes images using pre-trained convolutional neural networks (CNNs); and returns results to PACS. Outputs include a PDF report with volumetric tables and preview images, and a DICOM series with labeled overlays. For longitudinal cases, it provides comparative analysis of volume changes over time. Used in clinical settings by radiologists/clinicians to automate manual segmentation tasks, potentially improving workflow efficiency and providing objective quantitative data for clinical decision-making.

Clinical Evidence

Bench testing only. Performance evaluated on head CT scans with expert-labeled ground truth. Metrics included absolute error (volume in ml, shift in mm) and Dice scores. Intracranial hyperdensity (n=183) mean error 6.56ml, Dice 0.75. Midline shift (n=188) mean error 1.37mm. Lateral ventricles (n=210) mean error ~2.1ml, Dice 0.75-0.79. Reproducibility tested on 20% subset. All results met preset acceptance criteria.

Technological Characteristics

Standalone software; operates on off-the-shelf hardware; DICOM compatible. Core processing uses supervised voxel classification via Convolutional Neural Networks (CNNs). Outputs include PDF reports and annotated DICOM overlays.

Indications for Use

Indicated for automatic labeling, visualization, and quantification of intracranial hyperdensities, lateral ventricles, and midline shift in non-contrast head CT (NCCT) images for patients requiring brain structure volume analysis.

Regulatory Classification

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.

Special Controls

*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).

Predicate Devices

Icobrain (K181939)

Related Devices

K231929 — iQ-solutions · Sydney Neuroimaging Analysis Centre Pty, Ltd. · Dec 18, 2023
K050703 — QBRAIN · Medis Medical Imaging Systems BV · Apr 21, 2005
K192130 — Icobrain · Icometrix NV · Dec 13, 2019
K220437 — Neurophet AQUA · Neurophet., Inc. · May 10, 2023
K192531 — QyScore Software · Qynapse · Dec 13, 2019

Submission Summary (Full Text)

{0}------------------------------------------------ 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 {1}------------------------------------------------ 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 {2}------------------------------------------------ # Indications for Use 510(k) Number (if known) K211222 Device Name qER-Quant ### Indications for Use (Describe) 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. 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) ### CONTINUE ON A SEPARATE PAGE IF NEEDED. This section applies only to requirements of the Paperwork Reduction Act of 1995. ### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.* The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: > Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." {3}------------------------------------------------ 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 {4}------------------------------------------------ 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 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. 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. {5}------------------------------------------------ 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 Device Subject 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 | | Regulation Description | Medical image management and processing system | Picture archiving and communications system | | Device type | Automated Radiological Image Processing Software | Radiological Image Processing System | | Manufacturer | Qure.ai Technologies | icometrix NV | | Overview of Similarities between qER-Quant and the Predicate Device | | | | | qER-Quant Device Subject Device | Predicate Device | | 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 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: Abnormal Intracranial Hyperdensities, Lateral Ventricles and Midline Shift. | The Icobrain device is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR or NCCT images. This software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on MR or NCCT images. Icobrain consists of two distinct image processing pipelines: icobrain cross and icobrain long. icobrain cross is intended to provide volumes from MR or NCCT images acquired at a single time point. icobrain long is intended to provide changes in volumes between two MR images that were acquired on the same scanner, with the same image acquisition protocol and with same contrast at two different timepoints. The results of icobrain cross cannot be compared with the results of icobrain long. | | Technological Characteristics | - Software package - Operates on off-the-shelf hardware (multiple vendors) - DICOM compatible - Segmentation by deep learning (supervised voxel classification with Convolutional Neural Networks) | - Software package - Operates on off-the-shelf hardware (multiple vendors) - DICOM compatible - Segmentation by classical machine learning and deep learning (supervised voxel classification with Convolutional Neural Networks) | | Output | Multiple electronic reports with volumetric information of brain structures and midline shift AND Annotated DICOM Images | Multiple electronic reports with volumetric information of brain structures and midline shift AND Annotated DICOM Images | | Reference Standard for Performance testing | Accuracy Manually labeled images for all structures | Accuracy Manually labelled or simulated ground truth for MRI images Manually labeled images (lesions and midline shift) and images labeled by previously cleared Icobrain MRI software for CT images (lateral ventricles and whole brain) | | | qER-Quant Device Subject Device | Predicate Device | | | Test-retest images | MRI measurement changes compared on test-retest images; simulation study used for CT measurements | | Comparison of Differences between qER-Quant and the predicate device | | | | Input Images | Non-contrast CT from a single or multiple time points | T1-weighted and fluid-attenuated inversion recovery (FLAIR) MR images from a single or multiple time points and/or Non-contrast CT from a single time point | | Target structures analyzed on NCCT scans | Intracranial hyperdensities, lateral ventricles and midline shift | Intracranial hyperdensities, lateral ventricles, basal cisterns and midline shift | Table 1: Comparison between qER-Quant and the Predicate Device {6}------------------------------------------------ {7}------------------------------------------------ #### 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 (Standard Deviation) | 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 {8}------------------------------------------------ | 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.