(98 days)
Not Found
Yes
The device description explicitly states that the analysis module contains "a set of pre-trained convolutional neural networks (CNNs)," which are a type of deep learning model, a subset of machine learning and AI.
No.
The device's intended use is for automatic labeling, visualization, and quantification of segmentable brain structures from NCCT images, providing diagnostic information rather than directly treating a disease or condition.
Yes
The device is intended for "automatic labeling, visualization of segmentable brain structures" and "quantifying the volume of segmentable brain structures," which suggests it provides information used for diagnosis. It is "indicated for use in the following structures: Intracranial Hyperdensities, Lateral Ventricles and Midline Shift," and these conditions are commonly assessed in medical diagnosis. While it doesn't explicitly state "diagnosis," its function of quantifying anatomical structures relevant to medical conditions points towards a diagnostic purpose.
Yes
The device description explicitly states "qER-Quant is a standalone software device". It processes existing image data and interacts with a PACS system, indicating it does not include any hardware components.
Based on the provided information, this device is NOT an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. They are used to examine specimens outside of the body.
- Device Function: The qER-Quant device processes medical images (Non-Contrast head CT scans). It analyzes these images to identify, label, and quantify structures within the body (brain structures).
- No Specimen Analysis: The device does not analyze any biological specimens or samples taken from the patient. Its input is image data.
Therefore, the qER-Quant device falls under the category of medical image analysis software, not In Vitro Diagnostics.
No
The provided input does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The "Control Plan Authorized (PCCP) and relevant text" section explicitly states "Not Found".
Intended Use / Indications for Use
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.
Product codes
QIH
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.
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.
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Non-Contrast head CT (NCCT) images
Anatomical Site
Brain
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
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.
Summary of Performance Studies
Performance Testing: Standalone performance testing was conducted.
Sample Size:
Intracranial Hyperdensity: 183 scans
Midline Shift: 188 scans
Left Lateral Ventricle: 210 scans
Right Lateral Ventricle: 210 scans
Key Results: For all target structures, the standalone performance exceeded the preset acceptance criteria.
Key Metrics
Absolute error versus ground truth (volume in ml or shift in mm) & Dice Score.
Intracranial Hyperdensity (183):
Mean (Standard Deviation) of Absolute error: 6.56 (7.33) ml
Median (10th - 90th percentile) of Absolute error: 3.98 (0.52 - 17.35) ml
Mean (95% confidence interval) of Dice Score: 0.75 (0.72 - 0.78)
Midline Shift (188):
Mean (Standard Deviation) of Absolute error: 1.37 (1.23) mm
Median (10th - 90th percentile) of Absolute error: 1.15 (0.23 - 2.59) mm
Dice Score: Not applicable
Left Lateral Ventricle (210):
Mean (Standard Deviation) of Absolute error: 2.09 (1.88) ml
Median (10th - 90th percentile) of Absolute error: 1.60 (0.29 - 4.24) ml
Mean (95% confidence interval) of Dice Score: 0.79 (0.78 - 0.81)
Right Lateral Ventricle (210):
Mean (Standard Deviation) of Absolute error: 2.18 (1.72) ml
Median (10th - 90th percentile) of Absolute error: 1.88 (0.40 - 4.53) ml
Mean (95% confidence interval) of Dice Score: 0.75 (0.73 - 0.77)
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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).
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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
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
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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)
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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.
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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 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
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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 (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.