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.

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.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for qXR-LN are not explicitly stated as distinct numerical targets in the same way performance criteria often are. Instead, the document compares its performance to a predicate device (Samsung Auto Lung Nodule Detection) and demonstrates non-inferiority or improvement. The core principle for acceptance is "substantial equivalence" to the predicate, with performance metrics being a key factor in proving this equivalence.

Based on the provided information, the implicit acceptance criteria are framed around demonstrating performance at least equivalent to the predicate, particularly in terms of improving human reader performance and achieving a competitive standalone sensitivity for nodule detection.

Criteria Category	Acceptance Criteria (Implicit from Predicate & Study Goals)	Reported qXR-LN Device Performance
Standalone Performance	Nodule Level Sensitivity: Comparable to or better than predicate (80.69%).	84.1% (95% CI: 77.97-97.24)
	False Positives Per Image (FPPI): Low, comparable or better than predicate (+0.019).	0.18 (95% CI: 0.14 - 0.22). Compared to predicate aided-unaided: -0.0078
	Scan Level AUC: High	94.51 (95% CI: 92.64 - 96.66)
	Scan Level Sensitivity: High	93.83 (95% CI: 88.94 – 97)
	Scan Level Specificity: High	81.09 (95% CI: 76.30 – 85)
Human-in-the-loop Performance	AFROC: Statistically significant improvement in reader performance with AI assistance (predicate showed 7.8 (p=0.0003)).	AFROC (aided – unaided): 0.07621 (95% CI: 0.0497 – 0.1026), p < 1x10-5
	Nodule Level Sensitivity Improvement: Positive assistance to human readers (predicate showed 10.8%).	11.96% improvement in nodule level sensitivity (aided – unaided).

Study Details Proving Acceptance

2. Sample Size Used for the Test Set and Data Provenance

Test Set Sample Size: The exact number of cases in the test set is not explicitly stated. However, the study "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." The document mentions a "diverse dataset of 2.5 million scans" for training, implying a distinct, smaller, and well-characterized dataset for testing.
Data Provenance:
- Country of Origin: United States (8 states, 40 sites).
- Retrospective or Prospective: Retrospective. The study states "A fully crossed multi-case, multi-reader, retrospectively study design was utilized."

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

Number of Experts: 5 ABR (American Board of Radiology) certified ground truthers.
Qualifications: ABR certified radiologists. No specific years of experience are mentioned, but ABR certification implies a certain level of expertise.

4. Adjudication Method for the Test Set

The document states, "The standalone study was performed to compare qXR-LN's performance against a ground truth determined by 5 ABR certified ground truthers." It further specifies that "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 X-ray." This strongly suggests a consensus-based adjudication method among the 5 experts, likely with a pre-defined process for resolving discrepancies, although the specific "2+1" or "3+1" type is not detailed. The use of accompanying CT scans and reports likely aided in this consensus.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its effect size

Was it done? Yes, a "fully crossed multi-case, multi-reader, retrospectively study design was utilized."
Effect Size of Human Reader Improvement with AI vs Without AI Assistance:
- AFROC (Area Under the Free-Response Receiver Operating Characteristic) Improvement: The AFROC of readers was improved by 0.07621 (95% CI: 0.0497 – 0.1026) when aided by qXR-LN compared to unaided, which was statistically significant (P < 1x10-5).
- Nodule Level Sensitivity Improvement: qXR-LN indicated a 11.96% improvement for nodule level sensitivity when readers were aided.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was done

Was it done? Yes, "A standalone study to assess device performance was conducted."

7. The Type of Ground Truth Used

The ground truth for the test set was established by expert consensus (5 ABR certified radiologists) leveraging multiple modalities:

Chest X-rays
Accompanying CT scans
Reports

The ground truth was specifically "based on the nodules visible on the Chest X-ray," confirmed/localized with CT data. This suggests a blend of expert consensus and validation against a higher fidelity imaging modality (CT), with clinical reports providing further context.

8. The Sample Size for the Training Set

Training Set Sample Size: "a large and diverse dataset of 2.5 million scans from 5 countries across the world."

9. How the Ground Truth for the Training Set was Established

The document does not explicitly detail how the ground truth for the training set was established. It only mentions the size and diversity of the dataset. Given the scale (2.5 million scans), it's highly probable that a combination of methods was used, potentially including:

Automated extraction from radiology reports.
"Weak supervision" techniques where general labels infer detailed annotations.
A subset of manual expert review.
Potentially, also leveraging CT scans and reports as done for the test set, but for a smaller, expert-annotated subset used for ground-truthing in training.

However, since this information is not provided in the text, it remains an unknown based on the supplied document.

Summary

{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)

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}------------------------------------------------

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

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 DeviceAuto Lung Nodule Detection	Subject DeviceqXR-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 / Indicationsfor Use	The Auto Lung Nodule Detectionis computer-aided detectionsoftware to identify and markregions in relation to suspectedpulmonary nodules from 10 to 30mm in size. It is designed to aidthe physician to review the PAchest radiographs of adults as asecond reader and be used as partof S-Station, which is operationsoftware installed on SamsungDigital X-ray Imaging systems.Auto Lung Nodule Detectioncannot be used on the patientswho have lung lesions other thanabnormal nodules.	The qXR-LN (qXR_Lung_nodule) iscomputer-aided detection softwareto identify and mark regions inrelation to suspected pulmonarynodules from 6 to 30 mm in size. Thedevice is intended to be used in theincidental adult population. It isdesigned to aid the physician toreview the frontal (AP/PA) chestradiographs of adults acquired ondigital radiographic systems as asecond reader and be used with anyDICOM viewer or PACS. qXR-LNprovides adjunctive information onlyand is not a substitute for the originalchest radiographic image.
Intended User	Physicians.	Radiologists, emergency roomphysicians or pulmonologists whoregularly review chest X-rays as partof 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 duplicatedinput image.	ROI marked on the duplicated inputimage

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 < 1 x 10-5)
	FPPI (aided - unaided) +0.019	FPPI (aided – unaided) -0.0078
	Nodule level Sensitivity (aided- unaided) 10.8	Nodule level Sensitivity (aided – unaided) 11.96

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 - Clinical

A standalone study to assess device performance was conducted. 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.

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

{7}------------------------------------------------

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.

Modality	AFROC Estimate (95% CI)	AFROC (aided-unaided)	P - Value
Aided	0.8095 (0.7695-0.8494)	0.07621 (0.0497 – 0.1026)	P <1×10-5
Unaided	0.7333 (0.6892-0.7774)

Table 2 Performance of readers aided vs unaided by qXR-LN

As secondary metrics, AUC and nodule level sensitivity were estimated. The AUC of the readers improved by an estimate of 0.0697 (0.442 – 0.0953) and this result was significant. qXR-LN indicated 11.96% improvement for nodule level sensitivity. Subgroup analyses was performed and presented for relevant subgroups including age, gender, race, nodule characteristics, manufacturer, reader type, reader experience,location of nodule, scan view and confounders.

8 CONCLUSION

The comparison in Table 1 and the software and performance testing presented above demonstrate that the qXR-LN device is substantially equivalent to the predicate device. The qXR-LN is a software only device, similar to the predicate (Samsung Auto Lung Nodule Detection). The qXR-LN has similar indications, technological characteristics, and principles of operation as the predicate device. There are minor technological differences between the subject and predicate devices. Samsung Auto Lung Nodule Detection detects and marks nodules sized 10-30mm, whereas, qXR-LN can detect and mark nodules sized 6 mm to 30 mm. The qXR-LN device can be used to detect and localize smaller nodules than the predicate and the performance of qXR-LN has been demonstrated. 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. This does not pose any additional risks. Both devices operate as second reads in the standard workflow. The performance testing demonstrates that the qXR-LN device performs as intended and is therefore substantially equivalent to the predicate. Software and Clinical testing supports that the device performs in according with the device requirements.

Regulation Number and Section

§ 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.