The qCT Lung is a diagnostic image analysis tool intended to be used as a concurrent reader for the analysis of CT scans from lung cancer screening population. qCT Lung assists radiologists and pulmonologists in the detection of pulmonary nodules of size 4-30 mm, provides characterization, and quantification of the region of interest (ROI). Characterization of nodules involves features of texture (solid, part-solid, and ground glass), spiculation status, and calcification status. Quantification includes information on the volume and diameter of the region of interest. qCT Lung provides adjunctive information and is not intended to be used as a replacement for a complete interpreting physician's review or clinical judgment. qCT Lung is also not intended to be used without the original CT series.
Device Story
AI-based software processes non-contrast CT scans to assist radiologists/pulmonologists in detecting/characterizing pulmonary nodules; operates via cloud or on-premises server; integrates with PACS/DICOM workflows. Core processing uses pre-trained CNN; pre-processing prepares DICOM inputs; post-processing generates visual overlays (segmentation, labels). Users verify/edit segmentations; results provided as secondary capture series or web-based visualization. Features include nodule detection, texture classification (solid, part-solid, ground glass), spiculation/calcification status, and quantification (volume, diameter, long/short axis). Device serves as concurrent reader; provides adjunctive information; does not replace clinical judgment or standard of care. Benefits include enhanced diagnostic consistency and efficiency in lung cancer screening.
Clinical Evidence
Standalone study (670 scans) evaluated detection/characterization performance; scan-level AUROC 0.912. MRMC study (300 cases, 16 readers) compared unaided vs. aided performance; wAFROC improved from 0.655 (unaided) to 0.763 (aided). Accuracy for calcification, texture, and spiculation characterization significantly improved with device assistance (14.26% to 14.71% increase).
Technological Characteristics
AI-based software using CNN architecture; pre-trained on CT foundation model. Inputs: DICOM CT images. Outputs: DICOM overlays/secondary captures. Deployment: Cloud or on-premises server. Standards: ISO 13485:2016, IEC 62304:2006+A1:2015. Features: Nodule detection, texture/spiculation/calcification characterization, volume/diameter quantification.
Indications for Use
Indicated for adult lung cancer screening population undergoing CT examinations to assist radiologists and pulmonologists in detecting, characterizing, and quantifying pulmonary nodules (4-30 mm).
Regulatory Classification
Identification
A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.
Special Controls
A radiological computer assisted detection and diagnosis software must comply with the following special controls: Design verification and validation must include: 1. i. A detailed description of the image analysis algorithm, including but not limited to a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable. iii. Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contain a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, 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. Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of but not limited to clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in 1(iii) above. v. Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures. 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 device instructions for use, including the intended reading protocol and how the user should interpret the device output. iii. A detailed description of the intended user, and any user training materials as programs that addresses appropriate reading protocols for the device to ensure that the end user is fully aware of how to interpret and apply the device output. iv. A detailed description of the device inputs and outputs. v. A detailed description of compatible imaging hardware and imaging protocols. vi. 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. 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 anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.
*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 algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
*e.g.,* receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, 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) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
*e.g.,* receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(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 device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) 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) 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 anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.
Lung Nodule Assessment and Comparison Option (LNA) (K162484)
Submission Summary (Full Text)
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**U.S. FOOD & DRUG**
ADMINISTRATION
September 14, 2026
Qure.ai Technologies Pvt., Ltd.
% Smina T P
Senior Technical Lead - Regulatory & Clinical Affairs
6TH FLOOR, WING E, TIMES SQ., ANDHERI- KURLA RD., MAROL
ANDHERI (EAST)
MUMBAI, MAHARASHTRA 400059
INDIA
Re: K261517
Trade/Device Name: qCT Lung (v1.0)
Regulation Number: 21 CFR 892.2090
Regulation Name: Radiological Computer-Assisted Detection And Diagnosis Software
Regulatory Class: Class II
Product Code: QDQ
Dated: August 17, 2026
Received: August 17, 2026
Dear Smina T P:
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.
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device, or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the device, then a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
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).
Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13485 clause 8.3 (Nonconforming product), ISO 13485 clause 8.5.2 (Corrective action), and ISO 13485 clause 8.5.3 (Preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and ISO 13485 clause 7.5) and document changes and approvals in the Medical Device File (ISO 13485 clause 4.2.3).
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 (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-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these
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requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
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-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/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-devices/device-advice-comprehensive-regulatory-assistance/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, Ph.D.
Assistant Director
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
Enclosure
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
# **Indications for Use**
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.
510(k) Number (if known)
K261517
Device Name
qCT Lung (v1.0)
Indications for Use (Describe)
The qCT Lung is a diagnostic image analysis tool intended to be used as a concurrent reader for the analysis of CT scans from lung cancer screening population. qCT Lung assists radiologists and pulmonologists in the detection of pulmonary nodules of size 4-30 mm, provides characterization, and quantification of the region of interest (ROI). Characterization of nodules involves features of texture (solid, part-solid, and ground glass), spiculation status, and calcification status. Quantification includes information on the volume and diameter of the region of interest.
qCT Lung provides adjunctive information and is not intended to be used as a replacement for a complete interpreting physician's review or clinical judgment. qCT Lung is also not intended to be used without the original CT series.
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)
**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
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*"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."*
FORM FDA 3881 (8/23)
Page 1 of 1
PSC Publishing Services (301) 443-6740 EF
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qure.ai
510(k) Summary
# **510(k) SUMMARY**
**qCT LUNG v1.0**
**K261517**
# **1 SUBMITTER**
Qure.ai Technologies Pvt., Ltd.
6th Floor, Wing E, Times Square
Andheri-Kurla Road, Marol, Andheri (East),
Mumbai - 400059, Maharashtra, India.
Phone: +91-9496169480
Mail ID: smina.tp@qure.ai
Primary Contact Person: Smina T P, Senior Technical Lead - Regulatory & Clinical Affairs
Secondary Contact Person: Agnitha Chaturmathi, Manager - Regulatory Affairs
**Date Prepared:** 06 May 2026
# **2 SUBJECT DEVICE**
| **Name of Device:** | qCT Lung (v1.0) |
| --- | --- |
| **Common or Usual Name:** | Radiological Computer-Assisted Detection And Diagnosis Software |
| **Classification Name:** | Radiological Computer-Assisted Detection And Diagnosis Software |
| **Regulatory Class:** | Class II |
| **Regulation Number:** | 21 CFR 892.2090 |
| **Product Code:** | QDQ |
# **3 PREDICATE DEVICE**
| **Name of Device:** | Lunit INSIGHT DBT v1.1 |
| --- | --- |
| **Manufacturer:** | Lunit Inc. |
| **510(k) Number:** | K242652 |
| **Regulatory Class:** | Class II |
| **Regulation Number:** | 21 CFR 892.2090 |
| **Product Code** | QDQ |
# **4 REFERENCE DEVICES**
| **Name of Device:** | ClearRead CT |
| --- | --- |
| **Manufacturer:** | Riverain Technologies, LLC |
| **510(k) Number:** | K161201 |
| **Regulatory Class:** | Class II |
| **Regulation Number:** | 21 CFR 892.2050 |
| **Product Code** | OEB, LLZ |
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510(k) Summary
| **Name of Device:** | Lung Nodule Assessment and Comparison Option (LNA) |
| --- | --- |
| **Manufacturer:** | Philips Medical Systems Nederland B.V. |
| **510(k) Number:** | K162484 |
| **Regulatory Class:** | Class II |
| **Regulation Number:** | 21 CFR 892.2050; 21 CFR 892.1750 |
| **Product Code** | LLZ, JAK |
## 5 INDICATIONS FOR USE
The qCT Lung is a diagnostic image analysis tool intended to be used as a concurrent reader for the analysis of CT scans from lung cancer screening population. qCT Lung assists radiologists and pulmonologists in the detection of pulmonary nodules of size 4-30 mm, provides characterization, and quantification of the region of interest (ROI). Characterization of nodules involves features of texture (solid, part-solid, and ground glass), spiculation status, and calcification status. Quantification includes information on the volume and diameter of the region of interest.
qCT Lung provides adjunctive information and is not intended to be used as a replacement for a complete interpreting physician's review or clinical judgment. qCT Lung is also not intended to be used without the original CT series.
## 6 INTENDED USER
The intended users of the qCT Lung are radiologists and pulmonologists who are trained and experienced in the independent review and interpretation of CT scans.
## 7 INTENDED PATIENT POPULATION
qCT Lung is intended to be used for reviewing CT examinations from adult screening population.
## 8 DEVICE DESCRIPTION
Qure.ai's computed tomography (CT) scan software, the qCT Lung, is an artificial intelligence-based device that can process non-contrast CT scans and assist the radiologists and pulmonologists in detecting the pulmonary nodules, characterizing, and quantifying the region of interest (ROI). Characterization of nodules involves features of texture (solid, part-solid and ground glass), spiculation status (present/absent), and calcification status (present/absent). Quantification of the region of interest will include volume and diameter information.
qCT Lung's algorithm has been trained to process the incoming DICOM images allowing detection of nodules along with characterization and quantification of region of interest. The software works with DICOM CT images and can be deployed on a secure cloud server or on-premises server. qCT Lung consists of a cloud module that can interact with DICOM modality or the user's picture archiving and communication system (PACS) to receive de-identified scans and return the results to the same destination. The core processing module consists of a pre-trained convolutional neural network (CNN). This core processing module is coupled with a pre-processing module that prepares input DICOM files for processing by the CNN and a post-processing module that converts the output of the CNN into visual output in the form of an overlay showing the results.
Pulmonary nodules are segmented by the device initially, after which the user can verify the correctness of the segmentation and labels. If changes are required, they can manually correct the
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510(k) Summary
segmentation and labels using editing tools provided by the application. The segmented region is characterized and quantitatively measured by qCT Lung. The detection, characterization, and quantification results are presented to users as an additional overlay by highlighting and labelling, respectively. Results generated by qCT Lung can be presented in two ways to the users:
a. PACS-based mode: As a new series (secondary capture) which are returned to the originating PACS system with segmentation burnt on the series or as GSPS overlay if supported by PACS.
b. Web-based mode: On Qure's web application where secondary capture and its derived visualisations are shown along with output labels for review.
qCT Lung contains an artificial intelligence-based deep learning algorithm built on a convolutional neural network (CNN) architecture, pre-trained from a CT foundation model and fine-tuned for lung nodule detection, characterization, and quantification on CT images. The algorithm has been trained to detect the pulmonary nodules, characterize and quantify the region of interest on CT scans and is coupled with pre-and post-processing functionality that allows the device to work directly with the radiology workflow. The characterization feature output consists of information on nodule texture, calcification status and spiculation status. The quantitative feature output consists of information on average diameter and volumes of the region of interest. The additional features include long axis diameter (mm) and short axis diameter (mm).
qCT Lung is limited to analysis of imaging data and results generated are meant for information purposes only. The device is not intended for clinical diagnosis of any disease. It does not replace the role of physician or of other testing in the standard of care for lung abnormalities.
**Key Features:**
- Detection and localization of pulmonary nodules
- Characterization of detected pulmonary nodules
- Calcification status
- Spiculation status
- Texture (solid, ground glass, part-solid)
- Quantification of the region of interest
- Average Diameter (mm)
- Volume (mm³)
**Additional Features:**
Computation of the following measurements for each segmented region of interest:
- Long Axis - Longest diameter on an axial slice (mm)
- Short axis - Longest diameter perpendicular to the long axis on the same slice (mm)
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510(k) Summary
# 9 COMPARISON WITH PREDICATE DEVICE
Table 1 Comparison between qCT Lung (v1.0) and the Predicate Devices
| Device Name | Subject Device qCT Lung (v1.0) | Predicate Device Lunit INSIGHT DBT v1.1 | Reference Device 1 ClearRead CT | Reference Device 2 Lung Nodule Assessment and Comparison Option (LNA) |
| --- | --- | --- | --- | --- |
| 510(k) Number | K261517 | K242652 | K161201 | K162484 |
| Manufacturer | Qure.ai Technologies Pvt. Ltd. | Lunit Inc. | Riverain Technologies, LLC | Philips Medical Systems Nederland B.V. |
| Device Class | Class II | Class II | Class II | Class II |
| Device Classification Name | Radiological Computer Assisted Detection/Diagnosis Software For Suspicious Lesions For Cancer | Radiological Computer Assisted Detection/Diagnosis Software For Suspicious Lesions For Cancer | System, Image processing, Radiological | System, Image processing, Radiological |
| Regulation Number | 21 CFR 892.2090 | 21 CFR 892.2090 | 21 CFR 892.2050 | 21 CFR 892.2050 21 CFR 892.1750 |
| Regulation Description | Radiological Computer Assisted Detection And Diagnosis Software | Radiological Computer Assisted Detection And Diagnosis Software | Medical image management and processing system | Medical image management and processing system |
| Product Code | QDQ | QDQ | OEB, LLZ | LLZ, JAK |
| Intended use / Indications for Use | The qCT Lung is a diagnostic image analysis tool intended to be used as a concurrent reader for the analysis of CT scans from lung cancer screening population. qCT Lung assists radiologists and pulmonologists in the detection of pulmonary nodules of size 4-30 mm, provides characterization, and quantification of the region of interest (ROI). Characterization | Lunit INSIGHT DBT is a computer-assisted detection and diagnosis (CADe/x) software intended to be used concurrently by interpreting physicians to aid in the detection and characterization of suspected lesions for breast cancer in digital breast tomosynthesis (DBT) exams from compatible DBT | ClearRead CT™ is comprised of computer assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules during review of CT examinations of the chest on an asymptomatic population. The ClearRead CT requires | The Philips Medical Systems Lung Nodule Assessment and Comparison Option is intended for use as a diagnostic patient-imaging tool. It is intended for the review and analysis of thoracic CT images, providing quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include diameter, volume and volume |
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510(k) Summary
| | Subject Device | Predicate Device | Reference Device 1 | Reference Device 2 |
| --- | --- | --- | --- | --- |
| Device Name | qCT Lung (v1.0) | Lunit INSIGHT DBT v1.1 | ClearRead CT | Lung Nodule Assessment and Comparison Option (LNA) |
| | of nodules involves features of texture (solid, part-solid, and ground glass), spiculation status, and calcification status. Quantification includes information on the volume and diameter of the region of interest. qCT Lung provides adjunctive information and is not intended to be used as a replacement for a complete interpreting physician's review or clinical judgment. qCT Lung is also not intended to be used without the original CT series. | systems. Through the analysis, the regions of soft tissue lesions and calcifications are marked with an abnormality score indicating the likelihood of the presence of malignancy for each lesion. Lunit INSIGHT DBT uses screening mammograms of the female population. Lunit INSIGHT DBT is not intended as a replacement for a complete interpreting physician's review or their clinical judgment that takes into account other relevant information from the image or patient history. | both lungs be in the field of view. ClearRead CT provides adjunctive information and is not intended to be used without the original CT series. | over time. The system automatically performs the measurements, allowing lung nodules and measurements to be displayed. |
| **Intended User** | Radiologists and Pulmonologists | Physicians interpreting screening mammograms | Trained Medical Professionals | Radiologists and Technologist |
| **Target Patient Population** | Adult screening population | Women undergoing mammography | Asymptomatic population | Diagnostic and screening evaluations supporting Low Dose CT Lung Cancer Screening |
| **Target clinical conditions** | Lung Nodule | Suspected lesions for Breast cancer | Lung Nodule | Lung Nodule |
| **Modality** | Non-Contrast CT scan | Digital Breast Tomosynthesis (DBT) images | Thoracic CT scan | Thoracic CT scan |
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510(k) Summary
| | Subject Device | Predicate Device | Reference Device 1 | Reference Device 2 |
| --- | --- | --- | --- | --- |
| Device Name | qCT Lung (v1.0) | Lunit INSIGHT DBT v1.1 | ClearRead CT | Lung Nodule Assessment and Comparison Option (LNA) |
| **Fundamental Technological Basis** | Artificial intelligence-based deep learning algorithm built on a Convolutional Neural Network (CNN) architecture pre-trained using a CT foundation model and fine-tuned | Artificial intelligence / machine learning-based software algorithm | Artificial intelligence / machine learning-based software algorithm | Artificial Intelligence based software algorithm |
| **Input format** | CT images provided in a DICOM format | Digital Breast Tomosynthesis (DBT) images | CT images provided in a DICOM format | CT images provided in a DICOM format |
| **Device Output Format** | DICOM format output containing the region of interest marked with contour lines and labelled overlays indicating the texture, spiculation status, calcification status, volume, average diameter, long axis, and short axis, suitable for viewing in the PACS or radiology viewer. | Output that includes the lesion type, location, lesion-level/case-level score, and outline of the regions suspected of breast cancer | DICOM format output containing ROIs which are displayed as overlays with computed characteristics – avg density (HU), max diameter (mm), min diameter (mm) and volume (mm^3) | Output containing patient related information, dictation Table with Nodule result table and additional findings LungRADS and Risk Calculator |
| **Device Features** | Detection Characterization: • Texture (solid, part-solid, ground glass) • Spiculation Status • Calcification Status Quantification: • Nodule Volume (mm3) • Average Diameter (mm) • Long Axis Diameter (mm) | Detection • Characterization: • Lesion type • Location • Calcifications Lesion-level/case-level score | Detection • Volume • Maximum diameter • Minimum diameter • Density | Quantification: • Short axis (mm) • Long Axis (mm) • Average diameter (mm) • Max 3D diameter (mm) • Effective diameter (mm) • Volume (mm3) • Mean densities (HU) Characterization: |
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qure.ai
510(k) Summary
| Device Name | Subject Device qCT Lung (v1.0) | Predicate Device Lunit INSIGHT DBT v1.1 | Reference Device 1 ClearRead CT | Reference Device 2 Lung Nodule Assessment and Comparison Option (LNA) |
| --- | --- | --- | --- | --- |
| | • Short Axis Diameter (mm) | | | • Nodule type (solid, part-solid, ground-glass, calcified) • Lobe Location • Nodule shape (Circular/Oval/Triangular) • Nodule Spiculation Comparison and matching features: • Doubling time in days • Percent (%) and absolute change of all numerical parameters (growth in nodule long axis, short axis, average diameter, max 3D diameter, effective diameter, volume, mean HU). |
| Lesion Detection | Yes | Yes | Yes | No - user identified nodules |
| Characterization | Yes | Yes | No | Yes |
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quire.ai
510(k) Summary
## 10 TESTING
### 10.1. Software Testing
Software verification and validation testing were executed, and documentation was provided as recommended by FDA's Guidance for the Content of Premarket Submission for Device Software Functions (June 2023). During all verification and validation tests carried out for the qCT Lung software, which included evaluating both the algorithmic functionality and the overall performance of the software and its components, qCT Lung functioned as designed and successfully met the anticipated performance criteria.
Verification, validation, and testing activities were conducted to establish the performance, functionality, and reliability characteristics of the device. Unit Test, Integration Test, Regression Test, and User Acceptance test were carried out to account towards the device's performance non-clinically. Functional testing is done to assess the functional requirements of the product. The device passed all the tests based on determined acceptance criteria. Standards Regulatory references Used are ISO 13485: 2016 and IEC 62304:2006+A1:2015.
### 10.2. Standalone Study Performance Testing
Standalone study was conducted to estimate the performance of qCT Lung in detecting the pulmonary nodules, characterizing, and quantifying the region of intertest in low-dose CT (LDCT) scans. A total of 670 scans (462 with at least one nodule and 208 without nodules) were used to evaluate the performance of qCT Lung. The reference standard was established by three American Board of Radiology (ABR)-certified thoracic radiologists each with more than 10 years of experience.
The primary endpoints demonstrating the success of the study, along with the results of the standalone study, are summarized in Table 2.
**Table 2: Standalone Performance Testing of qCT Lung**
| Endpoints | Results (95% CI) |
| --- | --- |
| Scan-level AUROC | 0.912 (0.892-0.933) |
| AUROC (calcification) | 0.959 (0.925-0.994) |
| AUROC (spiculation) | 0.851 (0.816-0.885) |
| AUROC (texture) | Macro AUC: 0.934 (0.915-0.950) AUC Solid: 0.917 (0.896-0.939) AUC Part-solid: 0.935 (0.911-0.959) AUC GGN: 0.951 (0.926-0.975) |
| Mean bias: Bland-Altman analysis for average diameter | -0.36 mm (95% CI: -0.47 to -0.24) |
| Limits of Agreement (LOA): Bland-Altman analysis for average diameter | Lower LoA (95% CI): -3.48 mm (-3.83 to -3.15) Upper LoA (95% CI): 2.77 mm (2.44 to 3.15) |
| MANE for average diameter | 0.093 (0.085-0.097) |
| Mean bias - Bland-Altman analysis for volume | +2.4% (95% CI: +0.2 to +4.6) |
| Limits of Agreement (LOA): Bland-Altman analysis for volume | Lower LoA % (95% CI): -41.7 (-43.5 to -39.7) Upper LoA % (95% CI): +79.8 (+73.1 to +86.6) |
| MANE for volume | 0.18 (0.169-0.201) |
Overall, the results indicate that qCT Lung consistently met the predefined success criteria across detection, quantification, and characterization endpoints when evaluated against the ground truth (reference standard). The consistent and strong performance across diverse metrics and subtype-
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quire.ai
510(k) Summary
specific analyses supports its intended use as a decision-support tool to assist radiologists and pulmonologists in pulmonary nodule evaluation, potentially enhancing diagnostic consistency and efficiency. The study results also indicated that the outputs of the device are accurate and uniform across a wide range of potential sources of measurement error.
### 10.3. Clinical Performance Testing using MRMC Study
A multi-reader multi-case (MRMC) study was conducted to evaluate the performance of readers with (aided) and without the aid (unaided) of qCT Lung. A total of 300 cases, which consisted of 200 diseased scans and 100 non-diseased scans, were used to evaluate the performance of qCT Lung. The MRMC study was performed with 16 readers, including radiologists and pulmonologists spanning varying levels of clinical experience.
The results of the MRMC study are summarized in Table 3.
**Table 3: Overall Performance Metrics for the MRMC Study**
| Endpoints | Unaided | Aided | Difference |
| --- | --- | --- | --- |
| Detection: wAFROC (95% CI) | 0.655 (0.596, 0.714) | 0.763 (0.707, 0.819) | 0.108 (0.074, 0.142) |
| Calcification: Accuracy -ORH Method (95% CI) | 51.85% (44.37, 59.33) | 66.11% (57.70, 74.52) | 14.26% (10.92, 17.60) |
| Texture: Accuracy - ORH Method (95% CI) | 46.17% (39.39, 52.95) | 60.79% (52.78, 68.80) | 14.62% (11.28, 17.96) |
| Spiculation: Accuracy - ORH Method (95% CI) | 45.64% (38.43, 52.85) | 60.35% (52.05, 68.65) | 14.71% (11.24, 18.19) |
The MRMC study results demonstrate that qCT Lung assistance significantly improves the performance of the readers across all primary and secondary endpoints, with consistent benefits observed across reader specialties and experience levels. The device not only improves the detection of pulmonary nodules but also enhances the accuracy of their characterization, which is critical for decision-making and patient management in clinical workflows.
## 11 PREDETERMINED CHANGE CONTROL PLAN
The qCT Lung submission contains a Predetermined Change Control Plan (PCCP). Modifications to the algorithm of qCT Lung will be made in accordance with the guidelines outlined in the "Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions". The PCCP describes the modifications that can occur in a controlled manner, ensuring continued safety, effectiveness, and performance of the device.
The algorithm modification will not include any adaptive algorithms that can continuously learned from the environment. According to the modification outlined in the PCCP, any change will only occur after training, testing, and validating the algorithm after which the change will be locked. When the modification is locked this will be communicated to all users through updated labelling via the Instructions for use. Verification and Validation activities will be conducted in accordance with the PCCP to compare the performance of the modified qCT Lung algorithms to the original model to ensure the safety and effectiveness. The modification details are summarized as follows:
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quire.ai
510(k) Summary
Table 4 Details of Modification involved in PCCP
| Modification | Rationale | Testing Methods | Impact Assessment (including Benefits, Risks & Mitigations) |
| --- | --- | --- | --- |
| M1 – Data-Driven Retraining (CNN architecture unchanged) | Improve the algorithm performance | Retraining with newly acquired, high-quality adult CT datasets including scans from lung cancer screening population, followed by locked test-set evaluation and comparison to baseline model using predefined acceptance criteria | **Benefits:** Improved detection and characterization of lung nodules. **Risks:** Potential overfitting and unintended bias if new data are imbalanced; risk of minor performance variability. **Risk Mitigation:** The modified model will be tested against an independent validation dataset. |
## 12 CONCLUSION
The comparisons provided in Table 1 as well as the software and performance testing demonstrate that overall intended purpose, indications for use and methodological aspects of subject and predicate devices are the same. The subject and predicate devices have been classified under 21 CFR 892.2090 as Radiological Computer Assisted Detection and Diagnosis Software (Product Code: QDQ) and the classification aligns with their intended use and regulatory status. The comparison of technological characteristics demonstrates that qCT Lung is substantially equivalent to the predicate device and comparable to the reference devices, with similar modes of operation. The performance evaluation tests, including both standalone and MRMC studies, also assured that qCT Lung performs as indented and is as effective as the previously cleared predicate and reference devices.
Although the subject and predicate devices differ in anatomical focus (lung vs. breast), modality (CT vs. DBT), they share the same intended purpose to assist physicians in identifying and characterizing the suspicious lesions as a concurrent diagnostic support tool. When comparing with the reference devices, qCT Lung combines the functionalities of the reference devices ClearRead CT and LNA into a single integrated device. The technology, software architectures, algorithm and the operating procedure for the reference devices are similar to the subject device. All the four devices are intended as adjuncts to image interpretation, not as replacements for physician judgment. The minor technological differences between these devices are considered low risk, and they do not raise any questions or concerns about safety and effectiveness. The subject device does not introduce fundamentally new scientific technology, and the clinical tests demonstrate that the device is safe and effective.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the ? button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only Software as a Medical Device, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (Software as a Medical Device), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.
