K233108 · Vinbigdata Joint Stock Company · QFM · May 23, 2024 · Radiology
Device Facts
Record ID
K233108
Device Name
VinDr-Mammo
Applicant
Vinbigdata Joint Stock Company
Product Code
QFM · Radiology
Decision Date
May 23, 2024
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2080
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K233108 · May 23, 2024
VinDr-Mammo
Vinbigdata Joint Stock Company
RSNA Screening Mammography Breast Cancer Detection AI Challenge dataset; Retrospective clinical cohort from Hanoi Medical University Hospital
Retrospective clinical datasets were used to evaluate the performance (sensitivity, specificity, AUC) and generalizability of the AI algorithm across diverse demographics, breast densities, and scanner models.
Retrospective cohort; Performance validation; Generalizability; AI algorithm
RSNA Screening Mammography Breast Cancer Detection AI Challenge dataset: 1000 2D FFDM exams (252 positive, 748 negative). Hanoi Medical University Hospital dataset: 1864 2D FFDM exams (466 positive, 1398 negative).
—
Indications for Use
The VinDr-Mammo is a passive notification for prioritization-only, a parallel-workflow software tool used by MQSA qualified interpreting physicians to prioritize patients with suspicious findings in the medical care environment. VinDr-Mammo utilizes an artificial intelligence algorithm to analyze 2D FFDM screening mammograms and flags those that are suggestive of the presence of at least one suspicious finding at the exam-level. VinDr-Mammo produces an exam-level output to a PACS/Workstation for flagging the suspicious case and allows worklist prioritization. MQSA qualified interpreting physicians are responsible for reviewing each exam on a display approved for use in mammography, according to the current standard of care. VinDr-Mammo device is limited to the categorization of exams, does not provide any diagnostic information beyond triage and prioritization, does not remove images from the interpreting physician's worklist, and should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis. The VinDr-Mammo device is intended for use with complete 2D FFDM mammography exams acquired using validated FFDM systems only.
Device Story
VinDr-Mammo is a SaMD tool for radiological triage of 2D FFDM screening mammograms. It uses a machine learning algorithm to analyze images for suspicious findings; outputs an exam-level flag to PACS/workstations to prioritize worklists. Used by MQSA-qualified radiologists in clinical settings. The device operates in parallel with standard-of-care workflows; it does not remove images from worklists or provide diagnostic confirmation. It aids radiologists by highlighting potentially suspicious cases, potentially improving workflow efficiency. Input data includes 2D FFDM images; output is a prioritization flag (code 1 vs 0). The system includes modules for data validation, algorithm analysis, API cognitive services, and error reporting.
Clinical Evidence
No human clinical testing performed. Performance validated via two retrospective studies: 1) 1,000 exams from RSNA AI Challenge (Sensitivity 0.889, Specificity 0.906, AUC 0.958); 2) 1,864 exams from a Vietnamese hospital (Sensitivity 0.906, Specificity 0.911, AUC 0.965). Aggregate performance: Sensitivity 0.900, Specificity 0.910, AUC 0.962. Subgroup analysis confirmed consistent AUC across breast densities, age groups, and scanner models (Hologic, Siemens, Fujifilm, GE). Timing performance averaged 2.8 minutes.
Technological Characteristics
SaMD; machine learning algorithm for image analysis. Compatible with 2D FFDM systems (GE, Siemens, Fujifilm, Hologic). Connectivity via DICOM/API to PACS/RIS/EPR. Hosted on compatible server. Software modules: data validation, algorithm analysis, API service, error reporting.
Indications for Use
Indicated for MQSA qualified interpreting physicians to prioritize 2D FFDM screening mammograms suggestive of suspicious findings. Intended for use with complete 2D FFDM exams from validated systems. Not for diagnostic use; does not replace full patient evaluation.
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.
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May 23, 2024
VinBigData Joint Stock Company % Nguyet (Jun) Phan Regulatory Affairs Specialist Symphony Office Building, Chu Huy Man Street, Vinhomes Riverside Ecological Urban Area, Phuc Loi Ward, Long Bien District, Ha Noi VIETNAM
# Re: K233108
Trade/Device Name: VinDr-Mammo Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological Computer Aided Triage And Notification Software Regulatory Class: Class II Product Code: QFM Dated: April 10, 2024 Received: April 23, 2024
# Dear Nguyet (Jun) Phan:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
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Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
# Yanna S. Kang -S
Yanna Kang, Ph.D. Assistant Director Mammography and Ultrasound Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
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# Indications for Use
Submission Number (if known)
K233108
Device Name
VinDr-Mammo
Indications for Use (Describe)
The VinDr-Mammo is a passive notification for prioritization-only, a parallel-workflow software tool used by MQSA qualified interpreting physicians to prioritize patients with suspicious findings in the medical care environment. VinDr-Mammo utilizes an artificial intelligence algorithm to analyze 2D FFDM screening mammograms and flags those that are suggestive of the presence of at least one suspicious finding at the exam-level. VinDr-Mammo produces an exam-level output to a PACS/ Workstation for flagging the suspicious case and allows worklist prioritization.
MQSA qualified interpreting physicians are responsible for reviewing each exam on a display approved for use in mammography, according to the current standard of care. VinDr-Mammo device is limited to the categorization of exams, does not provide any diagnostic information beyond triage and prioritization, does not remove images from the interpreting physician's worklist, and should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis.
The VinDr-Mammo device is intended for use with complete 2D FFDM mammography exams acquired using validated FFDM systems only.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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#### I. Submission Sponsor
# VinBigData Joint Stock Company
Symphony Office Building, Chu Huy Man Street, Vinhomes Riverside Ecological Urban Area, Phuc Loi Ward, Long Bien District, Ha Noi, Vietnam
Telephone number: (+84) 968 496 314
Contact: Phan (Jun) Minh Nguyệt
Title: Regulatory Affairs Specialist
Phone number: (+84) 326 066 2088
Date Prepared: 19 April 2024
# Device Identification:
| Trade/Proprietary Name: | VinDr-Mammo |
|-------------------------|----------------------------------------------------------------------------|
| Common/Usual Name: | Radiological computer aided triage and notification software |
| Classification Name: | Radiological computer aided triage and notification software |
| Regulation Number: | 21 CFR 892.2080 |
| Product Code: | QFM, Radiological Computer-Assisted Prioritization Software<br>For Lesions |
| Device Class: | Class II |
| Classification Panel: | Radiology |
#### II. Predicate Device
The VinDr-Mammo device is substantially equivalent to the following device:
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| Proprietary Name | CogNet QmTRIAGE |
|------------------------|--------------------------------------------------------|
| Premarket Notification | K220080 |
| Classification Name | Radiological Computer-Assisted Prioritization Software |
| Regulation Number | 21 CFR 892.2080 |
| Product Code | QFM |
| Regulatory Class | II |
#### Device Description III.
The VinDr-Mammo is an innovative medical device designed to assist in the analysis and triage of 2D full-field digital mammogram (FFDM) screening mammograms. Operating as non-invasive computer-assisted software, known as SaMD, it employs a machine learning algorithm to identify potential suspicious findings within the images. Once identified, the system promptly notifies a PACS/workstation for further examination. This passive-notification feature enables radiologists to prioritize their workload efficiently and view studies in order of importance using standard PACS or workstation viewing software. It is important to note that the VinDr-Mammo software is intended solely to aid in the prioritization and triage of radiological medical images. It serves as a valuable tool for MQSA interpreting physicians who specialize in mammogram readings, complementing the standard of care. It should be emphasized that the device does not replace the need for a comprehensive evaluation as per established medical practices. During the algorithm's training, independent datasets from various global sites were utilized, ensuring a robust and diverse training experience.
The VinDr-Mammo code can be viewed by radiologists on a Picture Archiving and Communication System (PACS), Electronic Patient Record (EPR), and/or Radiology
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Information System (RIS) worklist and can be used to reorder the worklist: the mammographic studies with code 1 should be prioritized over those with code 0 and, thus, should be moved to the top of the worklist. As a software-only device, VinDr-Mammo can be hosted on a compatible host server connected to the necessary clinical IT systems such that DICOM studies can be received and the resulting outputs returned where they can be incorporated into the radiology worklist.
The following modules compose the VinDr-Mammo software:
- Data input and validation: Following retrieval of a study, the validation feature . assessed the input data (i.e. age, modality, view) to ensure compatibility for processing by the algorithm.
- VinDr-Mammo algorithm: Once a study has been validated, the algorithm analyzes . the 2D FFDM screening mammogram for detection of suspected findings.
- API Cognitive service: The study analysis and the results of a successful study . analysis are provided through an API service, whose outputs will then be sent to the appropriate clinical IT system for viewing on a radiology worklist.
- Error codes feature: In the case of a study failure during data validation or the . analysis by the algorithm, an error is provided to the system.
#### IV. Intended Use/Indication for Use
The VinDr-Mammo is a passive notification for prioritization-only, a parallelworkflow software tool used by MQSA qualified interpreting physicians to prioritize patients with suspicious findings in the medical care environment. VinDr-Mammo utilizes an artificial intelligence algorithm to analyze 2D FFDM screening mammograms and flags those that are suggestive of the presence of at least one suspicious finding at the examlevel. VinDr-Mammo produces an exam-level output to a PACS/Workstation for flagging the suspicious case and allows worklist prioritization.
MOSA qualified interpreting physicians are responsible for reviewing each exam on a display approved for use in mammography, according to the current standard of care. VinDr-Mammo device is limited to the categorization of exams, does not provide any
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diagnostic information beyond triage and prioritization, does not remove images from the interpreting physician's worklist, and should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis.
The VinDr-Mammo device is intended for use with complete 2D FFDM mammography exams acquired using validated FFDM systems only.
#### v. Technological Characteristics Compared to Predicate Device
The technological characteristics, e.g., overall design, mechanism of action, mode of operation, performance characteristics, etc., and the intended use of the VinDr-Mammo device are substantially equivalent to the predicate device cited above.
| Technological<br>Characteristics | Proposed Device<br>VinDr-Mammo | Predicate Device<br>CogNet QmTRIAGE<br>(K220080) | Summary | | |
|--------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|
| Indication for<br>Use/Intended<br>Use | The VinDr-Mammo is<br>a passive notification<br>for prioritization-only,<br>parallel-workflow<br>software tool used by<br>MQSA qualified<br>interpreting physicians<br>to prioritize patients<br>with suspicious<br>findings in the medical<br>care environment.<br>VinDr-Mammo utilizes<br>an artificial intelligence | The MedCognetics<br>(CogNet)<br>QmTRIAGETM<br>software is a passive<br>notification for<br>prioritization-only,<br>parallel-workflow<br>software tool used by<br>MQSA qualified<br>interpreting physicians<br>to prioritize patients<br>with suspicious<br>findings in the medical | Same | | |
| | | | | | |
| algorithm to analyze | care environment. | | | | |
| 2D FFDM screening | QmTRIAGETM | | | | |
| mammograms and | utilizes an artificial | | | | |
| flags those that are | intelligence algorithm | | | | |
| suggestive of the | to analyze 2D FFDM | | | | |
| presence of at least one | screening | | | | |
| suspicious finding at | mammograms and | | | | |
| the exam-level. VinDr- | flags those that are | | | | |
| Mammo produces an | suggestive of the | | | | |
| exam-level output to a | presence of at least one | | | | |
| PACS/Workstation for | suspicious finding at | | | | |
| flagging the suspicious | the exam level. | | | | |
| case and allows | QmTRIAGETM | | | | |
| worklist prioritization. | produces an exam level | | | | |
| MQSA qualified | output to a | | | | |
| interpreting physicians | PACS/Workstation for | | | | |
| are responsible for | flagging the suspicious | | | | |
| reviewing each exam | study and allows for | | | | |
| on a display approved | worklist prioritization. | | | | |
| for use in | MQSA qualified | | | | |
| mammography, | interpreting physicians | | | | |
| according to the current | are responsible for | | | | |
| standard of care. | reviewing each exam | | | | |
| VinDr-Mammo device | on a display approved | | | | |
| is limited to the | for use in | | | | |
| categorization of | mammography, | | | | |
| exams, does not | according to the current | | | | |
| provide any diagnostic | standard of care. The | | | | |
| | | | | | |
| | | | | | |
| | information beyond<br>triage and<br>prioritization, does not<br>remove images from<br>the interpreting<br>physician's worklist,<br>and should not be used<br>in lieu of full patient<br>evaluation, or relied<br>upon to make or<br>confirm diagnosis.<br>The VinDr-Mammo<br>device is intended for<br>use with complete 2D<br>FFDM mammography<br>exams acquired using<br>validated FFDM<br>systems only. | QmTRIAGE device is<br>limited to the<br>categorization of<br>exams, does not<br>provide any diagnostic<br>information beyond<br>triage and<br>prioritization, does not<br>remove images from<br>the interpreting<br>physician's worklist,<br>and should not be used<br>in lieu of full patient<br>evaluation, or relied<br>upon to make or<br>confirm diagnosis.<br>The QmTRIAGE<br>device is intended for<br>use with complete 2D<br>FFDM mammography<br>exams acquired using<br>validated FFDM<br>systems only. | | | |
| Notification-<br>only, parallel<br>workflow tool | Yes | Yes | Same | | |
| User | Interpreting physician | Interpreting physician | Same | | |
| Alert to finding | Yes; passive<br>notification flagged for<br>review | Yes; passive<br>notification flagged for<br>review | Same | | |
| Independent of<br>SoC workflow | Yes; No cases are<br>removed from worklist | Yes; No cases are<br>removed from worklist | Same | | |
| Modality | FFDM screening<br>mammograms | FFDM screening<br>mammograms | Same | | |
| FFDM<br>manufacturers<br>have been<br>validated | GE, Siemens, Fujifilm | Hologic | Same | | |
| Body part | Breast | Breast | Same | | |
| AI algorithm | Yes | Yes | Same | | |
| Limited to<br>analysis of<br>imaging data | Yes | Yes | Same | | |
| Inclusion<br>Criteria | - Standard 2D FFDM<br>screening<br>mammograms<br>- Biopsy proven cancer<br>studies (soft tissues and<br>microcalcifications)<br>- Biopsy-proven benign<br>studies | - Standard 2D FFDM<br>screening<br>mammograms<br>- Biopsy proven cancer<br>studies (soft tissues and<br>microcalcifications)<br>- BIRADS 1 and 2<br>normal/benign cases | Equivalent | | |
| | - BIRADS 1 and 2<br>normal cases with 2-<br>year follow-up of a<br>negative diagnosis<br>- Bila…
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 Tutorial 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-only devices, 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-only devices), 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.
