qER-CTA is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of the standard of care workflow. qER-CTA uses a deep learning algorithm to analyze images for findings suggestive of a pre-specified clinical condition and to notify an appropriate medical specialist in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyses CT angiogram images of the brain acquired in the acute setting and sends notifications to a neurovascular specialist that a suspected large vessel occlusion has been identified, recommending review of those images. Images can be previewed through a mobile application. qER-CTA is intended to analyze the internal carotid artery (ICA) and M1 segment of the middle cerebral artery (MCA) for LVOs on CTA scans of adults (≥ 22 years of age). Images previewed through the mobile application are compressed and for informational purposes only, not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer, conducting appropriate patient evaluation, and engaging in relevant discussions with the treating physician before making care-related decisions or requests. qER-CTA is limited to the analysis of imaging data and should not be used as a substitute for full patient evaluation or relied upon to make or confirm a diagnosis.
Device Story
qER-CTA is a radiological CADt software for triage of head CTA scans. It processes DICOM images to detect suspected large vessel occlusions (LVO) in the ICA and MCA-M1 segments. The device operates in parallel to standard-of-care workflows; it does not alter images or change worklist order. It provides a passive notification flag to clinicians and allows image preview via a mobile application. The system integrates with hospital PACS or an on-premises gateway. Clinicians receive notifications and are responsible for reviewing non-compressed images on a diagnostic viewer to inform clinical decision-making. The device aims to reduce time to notification for neurovascular specialists, potentially accelerating patient evaluation and treatment in acute settings.
Clinical Evidence
Standalone performance study using 584 head CTA scans (289 LVO, 295 non-LVO). Ground truth established by three board-certified neuroradiologists. Results: AUC 0.959 (95% CI: 0.943–0.975), sensitivity 91.35% (95% CI: 87.54%–94.07%), specificity 91.86% (95% CI: 88.18%–94.47%). Mean time to notification was 6.36 minutes.
Technological Characteristics
Software-only radiological CADt; deep learning algorithm; DICOM input; PACS/workstation integration; mobile application for compressed image preview. Standards: ISO 13485:2016, IEC 62304:2006+A1:2015.
Indications for Use
Indicated for adults (≥ 22 years) undergoing head CT angiography in acute settings to identify suspected large vessel occlusions (LVO) in the internal carotid artery (ICA) and M1 segment of the middle cerebral artery (MCA).
Regulatory Classification
Identification
Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.
Special Controls
Radiological computer aided triage and notification software must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm 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 effective triage (e.g., improved time to review of prioritized images for pre-specified clinicians). iii. Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment. iv. Standalone performance testing protocols and results of the device. v. 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). 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 user and user training that addresses appropriate use protocols for the device. iii. 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 for certain subpopulations), as applicable. iv. A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images. v. Device operating instructions. vi. A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, 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 notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm 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 effective triage (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) 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).(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 user and user training that addresses appropriate use protocols for the device;
(iii) 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 for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (*e.g.,* confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
{0}
FDA U.S. FOOD & DRUG ADMINISTRATION
September 8, 2025
September 8, 2025
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
# Quere.ai Technologies
Rajesh Sukhija
Regulatory Affairs Associate
6th Floor, Wing E, Times Square, Andheri- Kurla Road, Marol, Andheri
Mumbai, Maharashtra 400059
India
# Re: K251610
Trade/Device Name: qER-CTA (v1.0)
Regulation Number: 21 CFR 892.2080
Regulation Name: Radiological Computer Aided Triage And Notification Software
Regulatory Class: Class II
Product Code: QAS
Dated: July 31, 2025
Received: August 1, 2025
Dear Rajesh Sukhija:
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.
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K251610 - Rajesh Sukhija
Page 2
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 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 (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 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.
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 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
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K251610 - Rajesh Sukhija
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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,
Jessica Lamb, Ph D
Assistant Director
Imaging Software Team
DHT8B: Division of Radiological 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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qER-CTA
Page 8 of 33
| Indications for Use | | |
| --- | --- | --- |
| Please type in the marketing application/submission number, if it is known. This
textbox will be left blank for original applications/submissions. | K251610 | ? |
| Please provide the device trade name(s). | | ? |
| qER-CTA (v1.0) | | |
| Please provide your Indications for Use below. | | ? |
| qER-CTA is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to
identify and communicate images of specific patients to a specialist, independent of the standard of care
workflow. qER-CTA uses a deep learning algorithm to analyze images for findings suggestive of a pre-
specified clinical condition and to notify an appropriate medical specialist in parallel to standard of care
image interpretation. Identification of suspected findings is not for diagnostic use beyond notification.
Specifically, the device analyses CT angiogram images of the brain acquired in the acute setting and sends
notifications to a neurovascular specialist that a suspected large vessel occlusion has been identified,
recommending review of those images. Images can be previewed through a mobile application. qER-CTA is
intended to analyze the internal carotid artery (ICA) and M1 segment of the middle cerebral artery (MCA) for
LVOs on CTA scans of adults (≥ 22 years of age). Images previewed through the mobile application are
compressed and for informational purposes only, not intended for diagnostic use beyond notification.
Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer, conducting
appropriate patient evaluation, and engaging in relevant discussions with the treating physician before
making care-related decisions or requests. qER-CTA is limited to the analysis of imaging data and should
not be used as a substitute for full patient evaluation or relied upon to make or confirm a diagnosis. | | |
| Please select the types of uses (select one or both, as
applicable). | ☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C) | ? |
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qure.ai
510(k) Summary
K251610
# 510(k) SUMMARY
qER-CTA
# 1 SUBMITTER
Qure.ai Technologies Pvt. Ltd.
6th Floor, Wing E, Times Square,
Andheri-Kurla Road, Marol, Andheri (East),
Mumbai - 400059, Maharashtra
Phone: +91-7015256339
Primary Contact Person: Rajesh, Regulatory Affairs Associate
Secondary Contact Person: Bunty Kundnani, Chief Regulatory Affairs Officer
Date Prepared:
# 2 SUBJECT DEVICE
| Name of Device: | qER-CTA (v1.0) |
| --- | --- |
| Classification Name: | Radiological computer-assisted triage and notification software |
| Regulatory Class: | Class II |
| Regulation Number: | 21 CFR 892.2080 |
| Product Code: | QAS |
# 3 PREDICATE DEVICES
| Name of Device: | Viz LVO |
| --- | --- |
| Manufacturer: | Viz.ai, Inc. |
| 510(k) Number: | K223042 |
| Regulatory Class: | Class II |
| Regulation Number: | 21 CFR 892.2080 |
| Product Code | QAS |
# 4 INTENDED USE / INDICATIONS FOR USE:
qER-CTA is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of the standard of care workflow. qER-CTA uses a deep learning algorithm to analyze images for findings suggestive of a pre-specified clinical condition and to notify an appropriate medical specialist in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyses CT angiogram images of the brain acquired in the acute setting and sends notifications to a neurovascular specialist that a suspected large vessel occlusion has been identified, recommending review of those images. Images can be previewed through a mobile application. qER-CTA is intended to analyze the internal carotid artery (ICA) and M1 segment of the middle cerebral artery (MCA) for LVOs on CTA scans of adults (≥ 22 years of age). Images previewed through the mobile application are compressed and for informational purposes only, not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer, conducting appropriate patient evaluation, and engaging in relevant
Page 1 of 7
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qure.ai
510(k) Summary
discussions with the treating physician before making care-related decisions or requests. qER-CTA is limited to the analysis of imaging data and should not be used as a substitute for full patient evaluation or relied upon to make or confirm a diagnosis.
# 5 DEVICE DESCRIPTION
qER-CTA is a radiological computer-aided triage and notification (CADt) software designed to assist trained clinicians and radiologists in analyzing and triaging head CTA scans for suspected LVO (Large Vessel Occlusion) in the anterior circulation.
The software uses a deep learning algorithm to analyze CTA images and provide a case-level output available in the PACS or workstation for worklist prioritization or triage. It does not alter the original image, change the worklist order, or send proactive alerts directly to the end user. Instead, the end user can sort the worklist based on the passive notification flag. Images can be previewed through a mobile application also. There are two alternatives' users can choose from engaging with qER-CTA.
1. For de-identified CTA scans, they are sent to qER-CTA via transmission functions built within the user's PACS or workstation. Results are pushed back to the user's PACS or other user-specified radiology software database once the processing is complete.
2. For the client system that does not have de-identification and re-identification capabilities, qER-CTA interacts with on-premises gateway rather than directly with the PACS.
qER-CTA is not intended to direct attention to specific portions of the image, rule out target conditions, or be used as a standalone tool for clinical decision-making. It operates as a parallel workflow tool, independent of the standard of care, to assist in identifying and communicating suspected LVO cases to appropriate medical specialists for further review. Images previewed through the mobile application are compressed and are for informational purposes only.
Page 2 of 7
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qure.ai
510(k) Summary
# 6 COMPARISON WITH PREDICATE DEVICE
Table 1 Comparison between qER-CTA and the Predicate Device
| | Primary Predicate Device | Subject Device |
| --- | --- | --- |
| Device Name | Viz LVO | qER-CTA |
| 510(k) Number | K223042 | K251610 |
| Device Class | Class II | Class II |
| Device Classification Name | Radiological computer aided triage and notification software | Radiological computer aided triage and notification software |
| Regulation Number | 21 CFR 892.2080 | 21 CFR 892.2080 |
| Product Code | QAS | QAS |
| Manufacturer | Viz.ai, Inc. | Qure.ai Technologies Pvt. Ltd. |
| Intended use / Indications for Use | Viz LVO is a notification-only, parallel workflow tool designed for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of the standard of care workflow. Viz LVO uses an artificial intelligence algorithm to analyze images for findings suggestive of a pre-specified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard-of-care image interpretation. The identification of suspected findings is intended solely for notification purposes and not for diagnostic use beyond this notification. Specifically, the device analyzes CT angiogram images of the brain acquired in acute settings and sends notifications to a neurovascular specialist when a suspected large vessel occlusion (LVO) has been identified. It also recommends a review of those images. Images can be previewed through a mobile application. Viz LVO is intended to analyze the terminal ICA and MCA-M1 vessels for LVOs. Images previewed through the mobile application are | qER-CTA is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of the standard of care workflow. qER-CTA uses a deep learning algorithm to analyse images for findings suggestive of a pre-specified clinical condition and to notify an appropriate medical specialist in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyses CT angiogram images of the brain acquired in the acute setting and sends notifications to a neurovascular specialist that a suspected large vessel occlusion has been identified, recommending review of those images. Images can be previewed through a mobile application. qER-CTA is intended to analyse the internal carotid artery (ICA) and M1 segment of the middle cerebral artery (MCA) for LVOs on CTA scans of adults (≥ 22 years of age). Images previewed through the mobile application are compressed and for informational |
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qure.ai
510(k) Summary
| | Primary Predicate Device | Subject Device |
| --- | --- | --- |
| Device Name | Viz LVO | qER-CTA |
| | compressed and are for informational purposes only. They are not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussions with the treating physician before making care-related decisions or requests. Viz LVO is limited to the analysis of imaging data and should not be used in lieu of a full patient evaluation or relied upon to make or confirm a diagnosis. | purposes only, not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer, conducting appropriate patient evaluation, and engaging in relevant discussions with the treating physician before making care-related decisions or requests.qER-CTA is limited to the analysis of imaging data and should not be used as a substitute for full patient evaluation or relied upon to make or confirm a diagnosis. |
| Intended User | Neurovascular specialists, such as vascular neurologists, neuro-interventional specialists, or users with similar training who have been pre-authorized by their Healthcare Organization or Facility. | Neurologists, neuroradiologists, radiologists, neuro-interventional specialist, and/or other emergency department physicians. |
| Modality | Head Computed Tomography Angiography (CTA) | Head Computed Tomography Angiography (CTA) |
| Target clinical conditions | Large Vessel Occlusion | Large Vessel Occlusion |
| Algorithm for pre-specified critical findings detection | Image processing algorithms for large vessel occlusion | Image processing algorithms for large vessel occlusion |
| Input format | DICOM | DICOM |
| Performance level – accuracy of classification | Sensitivity: 87.8% (95% CI: 81.2% - 92.5%)
Specificity: 89.6% (95% CI: 83.7% - 93.9%)
AUC: 0.91 | Sensitivity: 91.35% (95% CI: 87.54%-94.07%)
Specificity: 91.86% (95% CI: 88.18% -94.47%)
AUC: 0.959 |
Page 4 of 7
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qure.ai
510(k) Summary
| | Primary Predicate Device | Subject Device |
| --- | --- | --- |
| Device Name | Viz LVO | qER-CTA |
| Mean Time to Notification | 7.32 [5.51, 9.13] | 6.36 [ 6.06, 6.66 ] |
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qure.ai
510(k) Summary
# 7 TESTING
## Software:
Software verification and validation testing was 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 qER-CTA software, which included evaluating both the algorithmic functionality and the overall performance of the software and its components, qER-CTA 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 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.
## Clinical Performance Testing:
Performance of the qER-CTA device in classification of large vessel occlusion was assessed using the standalone study. The target sample size for the study, including confounders was 584 head CTA scans (LVO = 289; non-LVO = 295), Three U.S. board certified neuroradiologists with at least 10 years of experience did the ground truthing. The device showed good performance and met the predefined success criteria when evaluated against the ground truth (reference standard). 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.
Table 2 Standalone Performance Testing Results for qER-CTA
| Abnormality | AUC (95% CI) | Sensitivity (95% CI), TP/P | Specificity (95% CI), TN/N |
| --- | --- | --- | --- |
| Large Vessel Occlusion | 0.959 (0.943 – 0.975) | 91.35% (87.54%-94.07%) | 91.86% (88.18% - 94.47%) |
## Time to Notification
| qER-CTA
(N = 584) | Time to Notification of
Specialist for LVO cases (mins) |
| --- | --- |
| Mean | 6.36 (6.06-6.66) |
| SD | 3.68 |
| Median | 6.89 |
Page 6 of 7
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qure.ai
510(k) Summary
# 8 CONCLUSION
The comparison in Table 1 as well as the software & performance testing presented above demonstrate that the qER-CTA device is substantially equivalent to the predicate device. Both the subject and predicate device are medical image analyzers intended to read head CTA scans to classify user identified large vessel occlusion. The algorithms function similarly and with the same purpose of classification of large vessel occlusion. The new device does not introduce fundamentally new scientific technology, and the clinical tests demonstrate that the device is safe and effective.
The qER-CTA is a software only device with similar indications, technological characteristics, and principles of operation as the predicate devices. The comparison of intended purpose, technological characteristics and performance demonstrates that the qER-CTA device performs as intended and can be considered as substantially equivalent to the predicate device, Viz LVO (K223042).
Page 7 of 7
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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.
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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.
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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.
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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.
