(239 days)
Not Found
Yes
The device description explicitly states that it utilizes an "artificial intelligence algorithm" and "employs a machine learning algorithm".
No.
The device is described as a prioritization and triage tool, not one that directly treats or diagnoses a medical condition. It aids physicians in workflow efficiency by flagging suspicious cases for review, but it does not provide diagnostic information, nor does it replace the need for full patient evaluation or confirm a diagnosis.
No
Explanation: The "Intended Use / Indications for Use" section explicitly states: "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." This clearly indicates it is a prioritization and triage tool, not a diagnostic one.
Yes
The device description explicitly states, "As a software-only device, VinDr-Mammo can be hosted on a compatible host server connected to the necessary clinical IT systems". It also details the software modules and their functions without mentioning any accompanying hardware components that are part of the medical device itself.
Based on the provided information, the VinDr-Mammo device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The VinDr-Mammo analyzes medical images (mammograms), which are not biological specimens.
- IVDs are used to provide information about a physiological state, health, or disease. While the VinDr-Mammo analyzes images for suspicious findings, its stated intended use is for prioritization-only and triage. It explicitly states it "does not provide any diagnostic information beyond triage and prioritization" and "should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis."
The VinDr-Mammo is a software tool that assists in the workflow of interpreting physicians by prioritizing cases based on the analysis of medical images. This falls under the category of medical devices, specifically Software as a Medical Device (SaMD), but not an In Vitro Diagnostic device.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / 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.
Product codes (comma separated list FDA assigned to the subject device)
QFM
Device Description
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 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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Mentions AI: "VinDr-Mammo utilizes an artificial intelligence algorithm".
Mentions ML: "it employs a machine learning algorithm".
Input Imaging Modality
2D FFDM screening mammograms
Anatomical Site
Breast
Indicated Patient Age Range
Not Found. (The test set included patients of various ages, but an "Indicated Patient Age Range" for clinical use is not specified).
Intended User / Care Setting
MQSA qualified interpreting physicians / medical care environment
Description of the training set, sample size, data source, and annotation protocol
During the algorithm's training, independent datasets from various global sites were utilized, ensuring a robust and diverse training experience.
Description of the test set, sample size, data source, and annotation protocol
The VinDr-Mammo device has been validated in two separate pivotal studies.
The first study employed 2D digital mammograms (FFDM) provided by the Radiological Society of North America (RSNA) via their RSNA Screening Mammography Breast Cancer Detection AI Challenge. The RSNA dataset consisted of 1000 2D FFDM mammogram exams including 252 cases positive for cancer with histologically proven and 748 cases negative for breast cancer (BI-RADS 1, BI-RADS 2 and biopsy-proven benign) with a two-year follow-up of a negative diagnosis.
The second was a performance study for triage of 2D FFDM screening mammogram cases from two Vietnamese hospitals. Due to lack of scanner information from the RSNA dataset, a secondary dataset of 2D FFDM from a frontline Vietnamese hospital (Hanoi Medical University Hospital) was used to demonstrate the generalizability to different screening modalities. The data included a retrospective cohort of 1864 anonymized 2D FFDM mammograms, including 466 cases positive with biopsy-confirmed cancers and 1398 cases negative for breast cancer (BIRADS1, BIRADS2 and biopsy-proven benign) with a two-year follow-up of a negative diagnosis. To ensure the data addressed confounding factors present in the population of women undergoing breast cancer screening, the test set was carefully constructed. Several factors, such as lesion type, breast density, age, and histology type, were considered. The accuracy of stand-alone detection and triage was measured on this cohort against the ground truth.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Two pivotal studies were conducted.
-
RSNA Dataset Study:
- Study type: Performance study.
- Sample size: 1000 2D FFDM mammogram exams (252 cancer positive, 748 cancer negative).
- Data source: Radiological Society of North America (RSNA) via their RSNA Screening Mammography Breast Cancer Detection AI Challenge.
- Key Results:
- Sensitivity: 0.889 (Lower 95% CI bound: 0.849, Upper 95% CI bound: 0.926)
- Specificity: 0.906 (Lower 95% CI bound: 0.885, Upper 95% CI bound: 0.927)
- AUC: 0.958 (Lower 95% CI bound: 0.945, Upper 95% CI bound: 0.970)
-
Vietnamese Hospital Dataset Study:
- Study type: Performance study for triage of 2D FFDM screening mammogram cases.
- Sample size: 1864 anonymized 2D FFDM mammograms (466 cancer positive, 1398 cancer negative).
- Data source: Two Vietnamese hospitals, including a frontline Vietnamese hospital (Hanoi Medical University Hospital).
- Key Results:
- Sensitivity: 0.906 (Lower 95% CI bound: 0.879, Upper 95% CI bound: 0.931)
- Specificity: 0.911 (Lower 95% CI bound: 0.896, Upper 95% CI bound: 0.926)
- AUC: 0.965 (Lower 95% CI bound: 0.957, Upper 95% CI bound: 971)
Aggregate Results for both RSNA and Vietnamese datasets:
- Sensitivity: 0.900 (Lower 95% CI bound: 0.877, Upper 95% CI bound: 0.921)
- Specificity: 0.910 (Lower 95% CI bound: 0.897, Upper 95% CI bound: 0.922)
- AUC: 0.962 (Lower 95% CI bound: 0.957, Upper 95% CI bound: 0.971)
Secondary Endpoint:
- Time Performance: Average of 2.8 minutes at a network speed of 10Mbits/s upload and 36Mbits/s download.
No standalone performance was explicitly mentioned as "standalone" from the AI. No MRMC was specified.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity, Specificity, AUC.
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.2080 Radiological computer aided triage and notification software.
(a)
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.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the 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
Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health and Human Services logo. To the right of that is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
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).
1
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
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
2
Indications for Use
Submission Number (if known)
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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3
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 |
| For Lesions | |
| Device Class: | Class II |
| Classification Panel: | Radiology |
II. Predicate Device
The VinDr-Mammo device is substantially equivalent to the following device:
4
| 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
5
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
6
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
Characteristics | Proposed Device
VinDr-Mammo | Predicate Device
CogNet QmTRIAGE
(K220080) | Summary | | |
|--------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|
| Indication for
Use/Intended
Use | The VinDr-Mammo is
a passive notification
for prioritization-only,
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 | The MedCognetics
(CogNet)
QmTRIAGETM
software is a passive
notification for
prioritization-only,
parallel-workflow
software tool used by
MQSA qualified
interpreting physicians
to prioritize patients
with suspicious
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
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. | QmTRIAGE 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 QmTRIAGE
device is intended for
use with complete 2D
FFDM mammography
exams acquired using
validated FFDM
systems only. | | | |
| Notification-
only, parallel
workflow tool | Yes | Yes | Same | | |
| User | Interpreting physician | Interpreting physician | Same | | |
| Alert to finding | Yes; passive
notification flagged for
review | Yes; passive
notification flagged for
review | Same | | |
| Independent of
SoC workflow | Yes; No cases are
removed from worklist | Yes; No cases are
removed from worklist | Same | | |
| Modality | FFDM screening
mammograms | FFDM screening
mammograms | Same | | |
| FFDM
manufacturers
have been
validated | GE, Siemens, Fujifilm | Hologic | Same | | |
| Body part | Breast | Breast | Same | | |
| AI algorithm | Yes | Yes | Same | | |
| Limited to
analysis of
imaging data | Yes | Yes | Same | | |
| Inclusion
Criteria | - Standard 2D FFDM
screening
mammograms
- Biopsy proven cancer
studies (soft tissues and
microcalcifications) - Biopsy-proven benign
studies | - Standard 2D FFDM
screening
mammograms - Biopsy proven cancer
studies (soft tissues and
microcalcifications) - BIRADS 1 and 2
normal/benign cases | Equivalent | | |
| | - BIRADS 1 and 2
normal cases with 2-
year follow-up of a
negative diagnosis - Bilateral Studies with
4 standard views (LCC,
LMLO, RCC, RMLO) | with 2-year follow-up
of a negative diagnosis - Female patients 22
and older - Bilateral Studies with
4 standard views (LCC,
LMLO, RCC, RMLO) | | | |
| Exclusion
Criteria | -Studies that do not
include all 4 views
-Digital Breast
tomosynthesis studies - 3D studies
Studies that do not
comply with the
inclusion criteria | - Digital breast
tomosynthesis images - 2D synthetic views
from tomosynthesis | Similar, different
criteria and
scope are
intended to limit
to specific
mammography
methods. This
does not raise
any questions
regarding safety
or efficacy and
use of
mammography
images | | |
| | Aids prompt
identification of
cases with
indicated
findings | Yes | Yes | Same | |
| | | Multiple
operating
points | No Applicable | Not Applicable | Same |
| | | Preview Images | Presentation of
notification and
preview of the study
for initial assessment
not meant for
diagnostic purposes.
The device operates in
parallel with the
standard of care, which
remains the default
option for all cases. | The device operates in
parallel with the
standard of care, which
remains the default
option for all cases.
Encapsulated PDF
stored
with original DICOM
study and may be
downloaded and
viewed as a PDF. | Equivalent |
| | Where results
are received | PACS / Workstation | PACS / Workstation | same | |
A comparison of the technological characteristics with the predicate is summarized below.
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8
9
10
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Performance Data VI.
Safety and performance of VinDr-Mammo has been evaluated and verified in accordance with software specifications and applicable performance standards through Software Development and Validation & Verification Process to ensure performance according to specifications, User Requirements and Federal Regulations and Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices".
The performance of the VinDr-Mammo device has been validated in two separate pivotal studies. The first study employed 2D digital mammograms (FFDM) provided by the Radiological Society of North America (RSNA) via their RSNA Screening
12
Mammography Breast Cancer Detection AI Challenge. The second was a performance study for triage of 2D FFDM screening mammogram cases from two Vietnamese hospitals.
The RSNA dataset was used to demonstrate the generalizability of the device to the demographics of the US population. The data set consisted of 1000 2D FFDM mammogram exams including 252 cases positive for cancer with histologically proven and 748 cases negative for breast cancer (BI-RADS 1, BI-RADS 2 and biopsy-proven benign) with a two-year follow-up of a negative diagnosis. A table of results is provided below:
| Metrics | Mean | Lower 95% CI bound | Upper 95% CI bound |
|---|---|---|---|
| Sensitivity | 0.889 | 0.849 | 0.926 |
| Specificity | 0.906 | 0.885 | 0.927 |
| AUC | 0.958 | 0.945 | 0.970 |
A summary of the DDSM dataset characteristics are provided in the table below:
| Characteristics | Quantity/Type |
|---|---|
| Characteristics | Quantity/Type |
| Number of Studies | 1000 |
| Number of Images | 5126 |
| Number of Patients | 1000 |
| Density A | 100 |
| Density B | 400 |
| Density C | 400 |
| Density D | 100 |
| Age ( |