(118 days)
Not Found
Yes
The device description explicitly states that the qXR-BT analysis module consists of a set of pre-trained convolutional neural networks (CNNs), which are a type of deep learning model, a subset of machine learning.
No
The device is an image analysis software intended to facilitate the confirmation of breathing tube position on chest X-rays by providing adjunctive information. It does not directly provide therapy or affect the body's structure or function.
No
The device is intended to generate a secondary digital chest X-ray image that facilitates the confirmation of the position of a breathing tube and an anatomical landmark. It provides adjunctive information and is not a substitute for the original image. While it supports the evaluation process, it helps confirm a position rather than diagnosing a condition or disease.
Yes
The device description explicitly states "qXR-BT is a standalone image analysis software" and details its integration with existing medical imaging software components (PACS, DICOM viewer). It receives digital images as input and produces digital outputs (PDF and DICOM) without mentioning any associated hardware components included with the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze biological samples: In Vitro Diagnostics are designed to examine specimens taken from the human body, such as blood, urine, tissue, etc., to provide information about a person's health.
- This device analyzes medical images: The qXR-BT device processes digital chest X-ray images, which are a form of medical imaging data, not biological samples.
- The intended use is image analysis for anatomical landmark and device position: The purpose of the device is to facilitate the confirmation of the position of a breathing tube and an anatomical landmark on a chest X-ray. This is an image analysis task, not a diagnostic test performed on a biological sample.
Therefore, the qXR-BT device falls under the category of medical image analysis software, not an In Vitro Diagnostic.
No
The letter does not explicitly state that the FDA has either reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
The qXR-BT device is intended to generate a secondary digital chest X-ray image that facilitates confirmation of the position of a breathing tube and an anatomical landmark on adult chest X-rays. This device is intended for use by licensed physicians who are trained in the evaluation of breathing tube placement on chest X-rays. The qXR-BT image provides adjunctive information and is not a substitute for the original PA/AP image.
Product codes
QIH
Device Description
qXR-BT is a standalone image analysis software used during the review of digital chest radiographic images, intended to facilitate determining the position of the breathing tube relative to the carina. Standard of care medical imaging workflows are well established, and include pre-existing software components such as a PACS, DICOM viewer and imaging worklist; qXR-BT is designed to integrate with these components.
X-rays are sent to qXR-BT by means of transmission functions within the user's PACS system. Upon completion of processing, the qXR-BT device returns results to the user's PACS or other user-specified radiology software system or database.
The input to the qXR-BT device is a chest X-ray (AP and PA, referred to as frontal) in digital imaging and communications in medicine (DICOM) format.
The qXR-BT device produces PDF and DICOM format outputs that enable users to view the position of a breathing tube and an anatomical landmark (carina).
The PDF format output contains preview images that show segmented structures outlined with a textual report describing the structures detected. The text report is restricted to the presence or absence of the breathing tubes and the carina as detected by the software device.
The DICOM format output consists of a single complete additional DICOM series for each input scan. This DICOM output contains labeled overlays indicating the location and extent of the segmentable structures, suitable for viewing in the PACS or radiology viewer.
The qXR-BT analysis module consists 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 DICOMs for processing by the CNNs and a post-processing module to convert the output into visual and tabular format for users.
Mentions image processing
Yes
Mentions AI, DNN, or ML
The qXR-BT analysis module consists 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 DICOMs for processing by the CNNs and a post-processing module to convert the output into visual and tabular format for users.
qXR-BT uses pre-trained convolutional neural networks to process the images, and the device highlights the tip of the tube and the carina using markings on the secondary image.
Input Imaging Modality
Digital chest radiograph
Anatomical Site
Chest
Indicated Patient Age Range
Adult
Intended User / Care Setting
licensed physicians who are trained in the evaluation of breathing tube placement on chest X-rays.
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
The number of Chest X-ray images used for this performance testing was 162. The ground truth was based on manual annotation of three radiologists from United States.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Standalone performance testing to test the accuracy of qXR-BT's analysis.
Sample size: 162 Chest X-ray images.
Localization accuracy for 2 target structures: carina and tip of breathing tube.
Accuracy of distance measurement between these 2 structures.
For the target structures, the standalone performance exceeded the preset acceptance criteria.
Overall Results of Accuracy Testing in mm:
Carina (162 scans): Absolute Distance Mean (Standard Deviation) 2.15 (1.25), Median (10th - 90th percentile) 1.86 (0.63 - 4.05), Mean (95% CI) 2.15 (1.96 - 2.35). Success Criteria: Upper bound of 95% CI ≤ 3mm.
Tip of Breathing Tube (162 scans): Absolute Distance Mean (Standard Deviation) 1.97 (1.09), Median (10th - 90th percentile) 1.8 (0.7 - 3.64), Mean (95% CI) 1.97 (1.80 – 2.13). Success Criteria: Upper bound of 95% CI ≤ 3mm.
Distance between tip of breathing tube and carina (162 scans): Absolute Error Mean (Standard Deviation) 1.98 (1.41), Median (10th - 90th percentile) 1.64 (0.27 - 4.12), Mean (95% CI) 1.98 (1.76 – 2.20). Success Criteria: Upper bound of 95% CI ≤ 6mm.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
See "Summary of Performance Studies" for absolute distance and absolute error metrics.
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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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA acronym followed by the full name of the agency on the right. The Department of Health & Human Services logo is a stylized depiction of an eagle. The FDA acronym and the agency's full name are in blue.
Qure.ai Technologies % Bunty Kundnani Head of Regulatory Affairs Level 7, Commerz II, International Business Park Oberoi Garden City, Goregaon (E) Mumbai, Maharashtra 400063 INDIA
Re: K212690
Trade/Device Name: qXR-BT Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: November 22, 2021 Received: November 24, 2021
Dear Bunty Kundnani:
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
1
requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801 and Part 809); 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 (OS) 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,
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) K212690
Device Name qXR-BT
Indications for Use (Describe)
The qXR-BT device is intended to generate a secondary digital chest X-ray image that facilitates confirmation of the position of a breathing tube and an anatomical landmark on adult chest X-rays. This device is intended for use by licensed physicians who are trained in the evaluation of breathing tube placement on chest X-rays. The qXR-BT image provides adjunctive information and is not a substitute for the original PA/AP image.
| 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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510(k) SUMMARY Qure.ai's qXR-BT
1 SUBMITTER
Qure.ai Technologies Level 7, Commerz II, International Business Park Oberoi Garden City, Goregaon (E), Mumbai 400 063 Phone: +91-9768123013 Contact Person: Bunty Kundnani
Date Prepared: November 22, 2021
2 DEVICE
| Name of Device: | qXR-BT |
|---|---|
| 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 |
PREDICATE DEVICE 3
| Name of Device: | ClearRead+Confirm Image Processing System |
|---|---|
| Manufacturer: | Riverain Technologies LLC |
| 510(k) Number: | K123526 |
4 INTENDED USE / INDICATIONS FOR USE:
The qXR-BT device is intended to generate a secondary digital chest X-ray image that facilitates confirmation of the position of a breathing tube and an anatomical landmark on adult chest Xrays. This device is intended for use by licensed physicians who are trained in the evaluation of breathing tube placement on chest X-rays. The qXR-BT image provides adjunctive information and is not a substitute for the original PA/AP image.
4
ട DEVICE DESCRIPTION
qXR-BT is a standalone image analysis software used during the review of digital chest radiographic images, intended to facilitate determining the position of the breathing tube relative to the carina. Standard of care medical imaging workflows are well established, and include pre-existing software components such as a PACS, DICOM viewer and imaging worklist; qXR-BT is designed to integrate with these components.
X-rays are sent to qXR-BT by means of transmission functions within the user's PACS system. Upon completion of processing, the qXR-BT device returns results to the user's PACS or other userspecified radiology software system or database.
The input to the qXR-BT device is a chest X-ray (AP and PA, referred to as frontal) in digital imaging and communications in medicine (DICOM) format.
The qXR-BT device produces PDF and DICOM format outputs that enable users to view the position of a breathing tube and an anatomical landmark (carina).
The PDF format output contains preview images that show segmented structures outlined with a textual report describing the structures detected. The text report is restricted to the presence or absence of the breathing tubes and the carina as detected by the software device.
The DICOM format output consists of a single complete additional DICOM series for each input scan. This DICOM output contains labeled overlays indicating the location and extent of the segmentable structures, suitable for viewing in the PACS or radiology viewer.
The qXR-BT analysis module consists 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 DICOMs for processing by the CNNs and a post-processing module to convert the output into visual and tabular format for users.
Image /page/4/Figure/8 description: This image shows a flowchart of the qXR-BT Analysis process. The process starts with chest x-ray scans in DICOM format, followed by pre-processing, core processing, and post-processing. The output of the post-processing step is then used to generate a secondary image with markers and labels in DICOM format, as well as a report with a preview image and table in PDF format. The title of the flowchart is "qXR-BT Analysis".
Figure 1: Schematic showing the design of qXR-BT device
б COMPARISON WITH PREDICATE DEVICE
Like the potential predicate devices, qXR-BT facilitates the confirmation of placement of a medically inserted tube on chest X-rays. In terms of establishing substantial equivalence, the subject and predicate device have the same intended use, as an image processing tool that generates a secondary digital radiographic image to facilitate the confirmation of the position of
5
medically inserted tube. The indications for use proposed for the subject device are similar to those of the predicate device, with the primary difference being that the predicate device is intended to facilitate the confirmation of multiple types of lines, tubes, and wires, while the subject device is intended to facilitate the confirmation of only one type of tube, viz. breathing tubes.
| | Predicate Device
ClearRead+Confirm
(K123526) | Subject Device
qXR-BT |
|-------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Name | ClearRead+Confirm | qXR-BT |
| 510(k) Number | K123526 | K212690 |
| Regulation | 21 CFR 892.2050 | 21 CFR 892.2050 |
| Regulation Description | Medical image management
and processing system
Formerly: Picture archiving
and communications system | Medical image management and
processing system |
| Product Code | LLZ | QIH |
| Device type | Radiological Image
Processing System | Automated Radiological Image
Processing Software |
| Manufacturer | Riverain Technologies | Qure.ai Technologies |
| Intended use / Indications
for Use | ClearRead+Confirm is
intended to generate an
enhanced, secondary digital
radiographic image of the
chest to facilitate
confirmation of line/tubes.
The enhanced AP or PA
image of the chest provides
improved visibility of lines
and tubes. The ClearRead
+Confirm image provides
adjunctive information and
is not a substitute for the
original PA/AP image. This
device is intended to be used
by trained professionals,
such as physicians,
radiologists, and technicians,
on patients with lines and
tubes and is not intended to
be used on pediatric
patients. | The qXR-BT device is intended to
generate a secondary digital chest X-
ray image that facilitates
confirmation of the position of a
breathing tube and an anatomical
landmark on adult chest X-rays. This
device is intended for use by licensed
physicians who are trained in the
evaluation of breathing tube
placement on chest X-rays. The qXR-
BT image provides adjunctive
information and is not a substitute
for the original PA/AP image. |
| Modality | Digital chest radiograph | Digital chest radiograph |
| Input format | DICOM | DICOM |
| | Predicate Device
ClearRead+Confirm
(K123526) | Subject Device
qXR-BT |
| Output Format | Secondary digital chest X-ray
image | Secondary digital chest X-ray image
and other Multiple electronic reports
with localization information of
segmented structures |
| Intended User | Physicians, radiologists and
technicians | Physicians and radiologists |
| Hardware | No additional hardware;
standalone software device
that integrates with PACS
through DICOM protocols | qXR-BT is standalone software
deployed on-premise or on the
cloud, that integrates with PACS or
other hardware or software imaging
platforms, including digital
radiographic processing systems
through DICOM protocols |
| Comparison of Differences between qXR-BT and the predicate device | | |
| Types of lines and tubes | Various medically inserted
tubes, lines, tubes and wires | Medically inserted breathing tubes
(tracheal tubes) only |
| Internal algorithms used
for image processing and
the mechanism by which
the output is displayed to
the clinician | ClearRead+Confirm uses a
bone suppression
mechanism with overall
image enhancements to
improve the visibility of lines
and tubes and provides no
boxes or markings to the
user. | qXR-BT uses pre-trained
convolutional neural networks to
process the images, and the device
highlights the tip of the tube and the
carina using markings on the
secondary image. |
Table 1: Comparison between qXR-BT and the Predicate Device
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7 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 performance testing to test the accuracy of qXR-BT's analysis. The number of Chest X-ray images used for this performance testing was 162. Table 2 shows localization accuracy for the 2 target structures – carina and tip of breathing tube and the accuracy of distance measurement between these 2 structures. The ground truth was based on manual annotation of three radiologists from United States. For the target structures, the
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standalone performance exceeded the preset acceptance criteria. The table below shows a summary of the results of performance testing.
| Target
Structure
(Number of
scans) | Metric | Mean
(Standard
Deviation) | Median (10th -
90th percentile) | Mean (95% CI) | Success Criteria |
|---------------------------------------------------------------------|----------------------|---------------------------------|------------------------------------|-----------------------|--------------------------------|
| Carina (162) | Absolute
Distance | 2.15 (1.25) | 1.86 (0.63 -
4.05) | 2.15 (1.96 -
2.35) | Upper bound of
95% CI ≤ 3mm |
| Tip of
Breathing
Tube (162) | Absolute
Distance | 1.97 (1.09) | 1.8 (0.7 - 3.64) | 1.97 (1.80 –
2.13) | Upper bound of
95% CI ≤ 3mm |
| Distance
between tip
of breathing
tube and
carina (162) | Absolute
Error | 1.98 (1.41) | 1.64 (0.27 -
4.12) | 1.98 (1.76 –
2.20) | Upper bound of
95% CI ≤ 6mm |
Table 2: Overall Results of Accuracy Testing in mm
Cl = confidence interval
qXR-BT also passed software validation and system verification checks.
8 CONCLUSION
The comparison in Table 2 and the software and performance testing presented above demonstrate that the qXR-BT device is substantially equivalent to the predicate device.