(185 days)
Not Found
Yes
The document explicitly states that the device utilizes a "deep learning algorithm" and mentions "AI algorithms" in the context of the MRMC study. Deep learning is a subset of machine learning and is considered a form of AI.
No.
This device is designed to aid in the detection of lung nodules for diagnostic purposes, not to provide therapy or treatment.
Yes
The device is described as "computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules" and is "designed to aid the physician to review the frontal (AP/PA) chest radiographs...as a second reader". This clearly indicates its role in helping diagnose conditions (pulmonary nodules).
Yes
The device is described as "computer-aided detection software" and a "Computer-Aided Detection (CADe) device" that processes digital images (DICOM format) and produces digital outputs (DICOM format). There is no mention of any accompanying hardware components being part of the device itself.
Based on the provided information, the qXR-LN device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The qXR-LN analyzes medical images (Chest X-rays), which are not biological specimens.
- IVDs are used to provide information about a physiological state, health, or disease. While the qXR-LN aids in identifying potential lung nodules, it does so by analyzing images, not by directly testing biological samples for markers of disease.
- The intended use and device description clearly state that the device processes medical images (Chest X-rays) to identify potential lung nodules. This is a form of medical image analysis, not in vitro testing.
The qXR-LN is a Computer-Aided Detection (CADe) device intended to assist physicians in reviewing medical images. This falls under the category of medical devices, but specifically those that process and analyze images, not those that perform tests on biological samples.
No
The input explicitly states "Control Plan Authorized (PCCP) and relevant text Not Found," and there is no text indicating that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
The qXR-LN (qXR Lung nodule) is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 6 to 30 mm in size. The device is intended to be used in the incidental adult population. It is designed to aid the physician to review the frontal (AP/PA) chest radiographs of adults acquired on digital radiographic systems as a second reader and be used with any DICOM viewer or PACS . qXR-LN provides adjunctive information only and is not a substitute for the original chest radiographic image.
Product codes
MYN
Device Description
qXR-LN is a Computer-Aided Detection (CADe) device that is designed to perform CAD processing in frontal (PA or AP view) Chest X-ray images for indication of locations for high nodule probability, which has an effective detection size from 6 mm to 30 mm. The device is intended to be a secondary aid to the qualified intended user to identify incidental pulmonary lung nodules chest x-ray images.
The device utilizes a deep learning algorithm. The qXR-LN was trained on a large and diverse dataset of 2.5million scans from 5 countries across the world. The training dataset was from more than 25 manufacturers.
Chest X-rays are sent to qXR-LN by the means of transmission functions within the user's image storage system (e.g., Picture Archiving and Communication System (PACS)) or other radiological imaging equipment (e.g., X-ray systems) and processed by the qXR-LN to detect and localise lung nodules. Following receipt of chest radiographs, the software device automatically analyses each image to detect and localise lung nodules.
qXR-LN receives chest x-ray images in digital imaging and communications in medicine (DICOM) as input. The qXR-LN device produces DICOM format outputs that enable users to view the presence and location of lung nodules.
This device is intended to aid the intended user in review of chest x-rays and detect and localise lung nodules as a secondary reader. The results are not intended to be used on a standalone basis for clinical decision-making nor is it intended to rule out the target conditions or otherwise preclude clinical assessment of X-ray cases.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Chest X-ray
Anatomical Site
Lung
Indicated Patient Age Range
adult population
Intended User / Care Setting
physician; Radiologists, emergency room physicians or pulmonologists who regularly review chest X-rays as part of their daily practice.
Description of the training set, sample size, data source, and annotation protocol
The qXR-LN was trained on a large and diverse dataset of 2.5million scans from 5 countries across the world. The training dataset was from more than 25 manufacturers.
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Standalone study:
These scans were obtained from 8 states (Ohio, New York, South Carolina, Iowa, Wisconsin, Texas, Oklahoma and Maryland) and 40 sites (each state had multiple sites) across the US. There were 55.48% females. Most scans (90.92%) were between 22 and 85 years of age. 31.43% scans were obtained from portable X-ray machines. The scans were sourced from more than 10 manufacturers. The data also consisted of commonly existing confounding conditions (mimickers, hardware or others). The standalone study was performed to compare qXR-LN's performance against a ground truth determined by 5 ABR certified ground truthers. They read the Chest X-rays with the accompanying CT scans and reports and the ground truth was based on the nodules visible on the Chest Xray. qXR-LN achieved a nodule-level sensitivity of 84.1% which we believe is substantially equivalent to the predicate's performance. The overall FPPI was 0.18 (0.14 - 0.22). As secondary metrics, scan level analyses were performed. qXR-LN achieved a scan level AUC of 94.51 (92.64 - 96.66). Sensitivity of 93.83 (88.94 – 97) and specificity of 81.09 (76.30 – 85) was achieved. Subgroup analyses was performed and presented for relevant subgroups including age, gender, race, nodule characteristics, manufacturer, location of nodule, scan view and confounders.
MRMC study:
A fully crossed multi-case, multi-reader, retrospectively study design was utilized. The MRMC study was conducted in a sequential study design as a second read aid. An image viewer with AI algorithms for lung nodule detection was used for chest X-ray scans. The ground truth was previously established by board-certified radiologists in the standalone study. The readers consisted of radiologists, pulmonologists and ER physicians who had experience of less 3, 3-7 and more than 7 years. The multi-reader multi-case demonstrated that aided readers performance for lung nodule detection was improved with statistical significance compared to unaided. We have performed subgroup analyses for several key subgroups to demonstrate generalizability.
Results Aided vs Unaided:
AFROC:
Aided: 0.8095 (0.7695-0.8494)
Unaided: 0.7333 (0.6892-0.7774)
AFROC (aided-unaided): 0.07621 (0.0497 – 0.1026)
P-Value: P
§ 892.2070 Medical image analyzer.
(a)
Identification. Medical image analyzers, including computer-assisted/aided detection (CADe) devices for mammography breast cancer, ultrasound breast lesions, radiograph lung nodules, and radiograph dental caries detection, is a prescription device that is intended to identify, mark, highlight, or in any other manner direct the clinicians' attention to portions of a radiology image that may reveal abnormalities during interpretation of patient radiology images by the clinicians. This device incorporates pattern recognition and data analysis capabilities and operates on previously acquired medical images. This device is not intended to replace the review by a qualified radiologist, and is not intended to be used for triage, or to recommend diagnosis.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithms including a description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing methods and dataset(s) used to assess whether the device will improve reader performance as intended and to characterize the standalone device performance. Performance testing includes one or more standalone tests, side-by-side comparisons, or a reader study, as applicable.
(iii) Results from performance testing that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results; and cybersecurity).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the intended reading protocol.
(iii) A detailed description of the intended user and user training that addresses appropriate reading protocols for the device.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) Device operating instructions.
(viii) A detailed summary of the performance testing, including: test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
0
December 22, 2023
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" in a square and the words "U.S. FOOD & DRUG ADMINISTRATION".
Qure.ai Technologies % Sri Anusha Matta Senior Regulatory Affairs Manager Level 7, Commerz II, International Business Park Oberoi Garden City, Goregaon(E) Mumbai, Maharashtra 400063 INDIA
Re: K231805
Trade/Device Name: qXR-LN Regulation Number: 21 CFR 892.2070 Regulation Name: Medical Image Analyzer Regulatory Class: Class II Product Code: MYN Dated: June 19, 2023 Received: June 20, 2023
Dear Sri Anusha Matta:
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 (the 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 available 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.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
1
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 801; medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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,
Lu Jiang
Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
2
Indications for Use
510(k) Number (if known) K231805
Device Name qXR-LN
Indications for Use (Describe)
The qXR-LN (qXR Lung nodule) is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 6 to 30 mm in size. The device is intended to be used in the incidental adult population. It is designed to aid the physician to review the frontal (AP/PA) chest radiographs of adults acquired on digital radiographic systems as a second reader and be used with any DICOM viewer or PACS . qXR-LN provides adjunctive information only and is not a substitute for the original chest radiographic image.
| 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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3
510(k) SUMMARY
Qure.ai's qXR-LN
1 SUBMITTER
Qure.ai Technologies Level 7, Commerz II, International Business Park Oberoi Garden City, Goregaon (E), Mumbai 400 063 Phone: +91-9768123013 Primary Contact Person: Sri Anusha Matta Secondary contact person: Bunty Kundnani
Date Prepared: June 16, 2023
2 DEVICE
| Name of Device: | qXR-LN |
|---|---|
| Common or Usual Name: | Analyzer, Medical Image |
| Classification Name: | Medical image analyzer |
| Regulatory Class: | Class II |
| Regulation Number: | 21 CFR 892.2070 |
| Product Code: | MYN |
3 PREDICATE DEVICE
| Name of Device: | Auto Lung Nodule Detection |
|---|---|
| Manufacturer: | Samsung Electronics Co., Ltd |
| 510(k) Number: | K201560 |
INTENDED USE / INDICATIONS FOR USE: 4
The qXR-LN (qXR_Lung_nodule) is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 6 to 30 mm in size. The device is intended to be used in the incidental adult population. It is designed to aid the physician to review the frontal (AP/PA) chest
4
radiographs of adults acquired on digital radiographic systems as a second reader and be used with any DICOM viewer or PACS . qXR-LN provides adjunctive information only and is not a substitute for the original chest radiographic image.
5 DEVICE DESCRIPTION
qXR-LN is a Computer-Aided Detection (CADe) device that is designed to perform CAD processing in frontal (PA or AP view) Chest X-ray images for indication of locations for high nodule probability, which has an effective detection size from 6 mm to 30 mm. The device is intended to be a secondary aid to the qualified intended user to identify incidental pulmonary lung nodules chest x-ray images.
The device utilizes a deep learning algorithm. The qXR-LN was trained on a large and diverse dataset of 2.5million scans from 5 countries across the world. The training dataset was from more than 25 manufacturers.
Chest X-rays are sent to qXR-LN by the means of transmission functions within the user's image storage system (e.g., Picture Archiving and Communication System (PACS)) or other radiological imaging equipment (e.g., X-ray systems) and processed by the qXR-LN to detect and localise lung nodules. Following receipt of chest radiographs, the software device automatically analyses each image to detect and localise lung nodules.
qXR-LN receives chest x-ray images in digital imaging and communications in medicine (DICOM) as input. The qXR-LN device produces DICOM format outputs that enable users to view the presence and location of lung nodules.
This device is intended to aid the intended user in review of chest x-rays and detect and localise lung nodules as a secondary reader. The results are not intended to be used on a standalone basis for clinical decision-making nor is it intended to rule out the target conditions or otherwise preclude clinical assessment of X-ray cases.
б COMPARISON OF THE PREDICATE DEVICE
qXR-LN is technologically similar to the predicate device, Samsung Electronics Auto Lung Nodule Detection in regard to the intended use and technological characteristics. Both are medical image analysers intended to read chest X-rays to detect and localise lung nodules. The algorithms function similarly and with the same purpose of detection and localization of lung nodules. There are minor technological differences between the subject and predicate devices. Samsung Auto Lung Nodule Detects and marks nodules sized 10-30mm, whereas, qXR-LN can detect and mark nodules sized 6-30mm. The device is intended to be used in the incidental adult population. Performance testing demonstrates that even with the difference in nodule size detection the device performance is substantially equivalently to the predicate device. The intended users of Samsung Auto Lung Nodule Detection are physicians, whereas, the intended users of the qXR-LN are radiologists, emergency room physicians or pulmonologists. The difference in intended users does not raise different questions of safety or effectiveness.
In terms of establishing substantial equivalence, the subject and predicate device have intended use, as a medical image analyser that detects and localises lung nodules and produces case-level output.
5
| | | Predicate Device
Auto Lung Nodule Detection | Subject Device
qXR-LN | |
|--|---------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--|
| | Device Name | Auto Lung Nodule Detection | qXR-LN | |
| | 510(k) Number | K201560 | K231805 | |
| | Regulation | 21 CFR 892.2070 | 21 CFR 892.2070 | |
| | Regulation Description | Medical Image Analyser | Medical Image Analyser | |
| | Product Code | MYN | MYN | |
| | Device type | Medical Image Analyser | Medical Image Analyser | |
| | Manufacturer | Samsung Electronics Co., Ltd | Qure.ai Technologies | |
| | Intended use / Indications
for Use | The Auto Lung Nodule Detection
is computer-aided detection
software to identify and mark
regions in relation to suspected
pulmonary nodules from 10 to 30
mm in size. It is designed to aid
the physician to review the PA
chest radiographs of adults as a
second reader and be used as part
of S-Station, which is operation
software installed on Samsung
Digital X-ray Imaging systems.
Auto Lung Nodule Detection
cannot be used on the patients
who have lung lesions other than
abnormal nodules. | The qXR-LN (qXR_Lung_nodule) is
computer-aided detection software
to identify and mark regions in
relation to suspected pulmonary
nodules from 6 to 30 mm in size. The
device is intended to be used in the
incidental adult population. It is
designed to aid the physician to
review the frontal (AP/PA) chest
radiographs of adults acquired on
digital radiographic systems as a
second reader and be used with any
DICOM viewer or PACS. qXR-LN
provides adjunctive information only
and is not a substitute for the original
chest radiographic image. | |
| | Intended User | Physicians. | Radiologists, emergency room
physicians or pulmonologists who
regularly review chest X-rays as part
of their daily practice. | |
| | Modality | Chest X-ray | Chest X-ray | |
| | Target clinical conditions | Lung Nodules on PAview Chest X-
rays | Lung Nodules on PA/AP view Chest X-
rays | |
| | Technology | Machine learning | Deep/Machine Learning | |
| | Input format | DICOM | DICOM | |
| | Output | ROI marked on the duplicated
input image. | ROI marked on the duplicated input
image | |
Table 1 Comparison between qXR-LN and the Predicate Device
6
| Predicate Device | Subject Device | |
|---|---|---|
| Auto Lung Nodule Detection | qXR-LN | |
| Performance Metrics | ||
| Performance metrics used | Sensitivity, FPPI and JAFROC were calculated | Sensitivity, FPPI and AFROC were calculated |
| Nodule level sensitivity 80.69% | Nodule level sensitivity 84.10 (77.97-97.24) | |
| JAFROC (aided – unaided) 7.8 (p=0.0003) | AFROC (aided - unaided) 7.62 (p |