(101 days)
Not Found
Yes
The device description explicitly states that it uses a "deep learning algorithm" to analyze images and assess breast tissue composition. Deep learning is a subset of machine learning.
No.
The device provides adjunctive information for assessing breast tissue composition and is explicitly stated as "not a diagnostic aid," indicating it does not directly treat or diagnose to be considered therapeutic.
No.
The "Intended Use / Indications for Use" section explicitly states "It is not a diagnostic aid."
Yes
The device is explicitly described as a "standalone software application" that analyzes existing image data and provides outputs. There is no mention of accompanying hardware or hardware components being part of the device itself.
Based on the provided information, this device is NOT an In Vitro Diagnostic (IVD).
Here's why:
- Intended Use: The intended use explicitly states that WRDensity "provides an ACR BI-RADS 5th Edition breast density category to aid interpreting physicians in the assessment of breast tissue composition." It also clearly states, "It is not a diagnostic aid." IVDs are used to examine specimens derived from the human body to provide information for diagnosis, monitoring, or screening. WRDensity analyzes medical images, not biological specimens.
- Device Description: The description details how the software analyzes digital breast x-ray images and outputs breast density categories and probabilities. This is image analysis, not the analysis of biological samples.
- Input: The input is "for presentation" data from digital breast x-ray systems (images), not biological specimens like blood, urine, or tissue.
- Anatomical Site: The anatomical site is the Breast, which is consistent with imaging, not IVD testing.
While the device uses a deep learning algorithm and provides information to aid physicians, its function is to process and analyze medical images to assess breast tissue composition, which falls under the category of medical image analysis software, not in vitro diagnostics.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
WRDensity is a software application intended for use with compatible full field digital mammography and digital breast tomosynthesis systems. WRDensity provides an ACR BI-RADS 5th Edition breast density category to aid interpreting physicians in the assessment of breast tissue composition. WRDensity produces adjunctive information. It is not a diagnostic aid.
Product codes
QIH
Device Description
WRDensity is a standalone software application that automatically analyzes "for presentation" data from digital breast x-ray systems with a deep learning algorithm to assess breast tissue composition. WRDensity primarily generates two outputs for an exam, the Breast Density Level (BDL) and the Breast Density Level Probabilities (BDLP).
The Breast Density Level is a categorical breast density assessment in accordance with the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS®) Atlas 5th Edition breast density categories "A" through "D". The BDL is the primary output of WRDensity.
The Breast Density Level Probabilities are the probabilities calculated by WRDensity for each of the four density categories. The BDLP is a secondary output that provides more information about the breast density of an exam and the device's confidence level.
WRDensity takes in images via a Digital Imaging and Communications in Medicine (DICOM) transfer from the facility's mammography imaging system, Picture Archive and Communication Server (PACS), or DICOM router. After analysis, WRDensity sends outputs to be stored in the PACS and Radiology Information System (RIS). These outputs can then be reviewed by the radiologist on the mammography workstation as a DICOM Secondary Capture Image, a DICOM Structured Report, and in the RIS. These outputs can be configured to match user preferences.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
FFDM
Hologic Selenia Dimensions
Hologic Lorad Selenia
Synthetic 2D
Hologic C-View
Anatomical Site
Breast
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Interpreting Physicians
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
The output of WRDensity was compared against a consensus of five expert radiologists who independently assessed breast density on a test dataset that represented all compatible modalities and patient populations. The test dataset comprised 871 exams from unique patients.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Software verification and validation testing evaluated the performance of WRDensity, specifically:
- Performance was assessed by comparing the Breast Density Level output to the radiologist consensus using accuracy, quadratically-weighted Cohen's kappa, and confusion matrices. Performance on the four-class task and binary task, i.e. dense (BI-RADS C+D) vs. non-dense (BI-RADS A+B) were both assessed.
- Consistency was assessed by evaluating the agreement, in terms of percentage of cases, between the BDL for the mediolateral oblique (MLO) and craniocaudal (CC) views of the same breast.
- Reproducibility was assessed using the maximum root mean square error across all images between the predicted probabilities produced from an initial processing run and those produced in a second processing run on the same testing data.
The test dataset comprised 871 exams from unique patients.
On the four-class task, WRDensity achieved a quadratically-weighted Cohen's kappa of 0.90, 95% confidence interval [0.88, 0.92]. A confusion matrix demonstrating the level of agreement between the BDL and the radiologist consensus for each BI-RADS breast density category can be found in Figure 1.
On the binary task, WRDensity achieved a Cohen's kappa of 0.88, 95% confidence interval [0.85, 0.91]. The confusion matrix is presented in Figure 2.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
On the four-class task, WRDensity achieved a quadratically-weighted Cohen's kappa of 0.90, 95% confidence interval [0.88, 0.92].
On the binary task, WRDensity achieved a Cohen's kappa of 0.88, 95% confidence interval [0.85, 0.91].
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
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 & Human Services logo. To the right of that is the FDA logo, with the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, and then the word "ADMINISTRATION" in a smaller font below.
October 30, 2020
Whiterabbit.ai Inc. % Mr. Jason Su CTO and Co-founder 3930 Freedom Cir., Ste 101 SANTA CLARA CA 95054
Re: K202013
Trade/Device Name: WRDensity by Whiterabbit.ai Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: QIH Dated: September 29, 2020 Received: September 30, 2020
Dear Mr. Su:
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 (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 located 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.
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
1
801 and Part 809); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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,
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known)
Device Name
WRDensity by Whiterabbit.ai
Indications for Use (Describe)
WRDensity is a software application intended for use with compatible full field digital mammography and digital breast tomosynthesis systems. WRDensity provides an ACR BI-RADS 5th Edition breast density category to aid interpreting physicians in the assessment of breast tissue composition. WRDensity produces adjunctive information. It is not a diagnostic aid.
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
Section 5. 510(k) Summary
5.1 General Information
| 510(k) Sponsor | Whiterabbit AI Inc. |
|---|---|
| Address | 3930 Freedom Cir., Ste 101 |
| Santa Clara, CA 95054 | |
| Correspondence Person | Jason Su |
| Contact Information | 914-275-1097 |
| jason@whiterabbit.ai | |
| Date Prepared | October 29, 2020 |
5.2 Subject Device
| Proprietary Name | WRDensity by Whiterabbit.ai |
|---|---|
| Common Name | WRDensity |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
5.3 Predicate Device
| Proprietary Name | Densitas densityai |
|---|---|
| Premarket Notification | K192973 |
| Classification Name | System, Image Processing, Radiological |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | LLZ |
| Regulatory Class | II |
5.4 Device Description
WRDensity is a standalone software application that automatically analyzes "for presentation" data from digital breast x-ray systems with a deep learning algorithm to assess breast tissue composition. WRDensity primarily generates two outputs for an exam, the Breast Density Level (BDL) and the Breast Density Level Probabilities (BDLP).
4
The Breast Density Level is a categorical breast density assessment in accordance with the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS®) Atlas 5th Edition breast density categories "A" through "D". The BDL is the primary output of WRDensity.
The Breast Density Level Probabilities are the probabilities calculated by WRDensity for each of the four density categories. The BDLP is a secondary output that provides more information about the breast density of an exam and the device's confidence level.
WRDensity takes in images via a Digital Imaging and Communications in Medicine (DICOM) transfer from the facility's mammography imaging system, Picture Archive and Communication Server (PACS), or DICOM router. After analysis, WRDensity sends outputs to be stored in the PACS and Radiology Information System (RIS). These outputs can then be reviewed by the radiologist on the mammography workstation as a DICOM Secondary Capture Image, a DICOM Structured Report, and in the RIS. These outputs can be configured to match user preferences.
5.5 Indications for Use
WRDensity is a software application intended for use with compatible full-field digital mammography and digital breast tomosynthesis systems. WRDensity provides an ACR BI-RADS 5th Edition breast density category to aid interpreting physicians in the assessment of breast tissue composition. WRDensity produces adjunctive information. It is not a diagnostic aid.
5.6 Comparison of Technological Characteristics with the Predicate Device
| | Subject Device
WRDensity | Predicate Device
densityai (K192973) |
|-----------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Classification
Name | Automated Radiological Image
Processing Software | System, Image Processing,
Radiological |
| Product Code | QIH | LLZ |
| Regulation
Number | 892.2050 | 892.2050 |
| Regulation
Description | Picture archiving and
communication system | Picture archiving and
communication system |
| | Subject Device
WRDensity | Predicate Device
densityai (K192973) |
| Indications for
Use | WRDensity is a software
application intended for use with
compatible full field digital
mammography and digital breast
tomosynthesis systems.
WRDensity provides an ACR
BI-RADS Atlas 5th Edition breast
density category to aid
interpreting physicians in the
assessment of breast tissue
composition. WRDensity
produces adjunctive information.
It is not a diagnostic aid. | Densitas densityai™ is a software
application intended for use with
compatible full field digital
mammography and digital breast
tomosynthesis systems. Densitas
densityai™ provides an ACR
BI-RADS Atlas 5th Edition breast
density category to aid interpreting
physicians in the assessment of
breast tissue composition. Densitas
densityai™ produces adjunctive
information. It is not a diagnostic
aid. |
| Patient
Population | Symptomatic and
asymptomatic women
undergoing
mammography | Symptomatic and
asymptomatic women
undergoing
mammography |
| End Users | Interpreting Physicians | Interpreting Physicians |
| Image Source
Modalities | FFDM
Hologic Selenia Dimensions
Hologic Lorad Selenia
Synthetic 2D
Hologic C-View | FFDM
Hologic Selenia Dimensions
Hologic Lorad Selenia
GE Senographe Essential
GE Senographe Pristina
Siemens MAMMOMAT
Inspiration
Siemens MAMMOMAT Novation
DR
Siemens MAMMOMAT Fusion
Siemens MAMMOMAT
Inspiration Prime
Siemens MAMMOMAT
Revelation |
| | | Synthetic 2D
Hologic C-View |
| Input: Image
Data Format | DICOM digital mammography
images - For Presentation; RCC,
LCC, RMLO, LMLO | DICOM digital mammography
images - For Presentation; RCC,
LCC, RMLO, LMLO |
| | | |
| Output Data | BIRADS 5th Ed.
For each patient:
Whiterabbit.ai WRDensity Breast
Density Level, and Breast Density
Level Probability | BIRADS 5th Ed.
For each patient:
Densitas densityai™ breast density
grade |
| | | |
| Measurement
Scale | 4-category breast density scale
from 5th Ed. ACR BI-RADS
Atlas 2013 | 4-category breast density scale
from 5th Ed. ACR BI-RADS Atlas
2013 |
| Output Device | Mammography Workstation,
PACS, RIS | Mammography Workstation,
PACS, RIS |
| Output
Format | DICOM Structured
Report and Secondary
Capture
Text labels presented in a
radiologist's PACS
and RIS patient worklist. | DICOM Structured
Report and Secondary
Capture |
| | | |
| | | |
| Deployment | Virtual Machine Software | Standalone computer |
| Assessment
Scope | Results per exam | Results per exam |
| Assessment
Type | Image feature-based with deep
learning | Image feature-based |
| Anatomical
Location | Breast | Breast |
Table 5.1 Predicate Device Table
5
Table 5.2 Indications and Technological Characteristics Comparison
6
5.7 Performance Data
Safety and performance of WRDensity have been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, software validation activities were performed in
7
accordance with IEC 62304:2006/AC:2015 - Medical device software - Software life cycle processes, in addition to the FDA guidance document, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."
The validation testing evaluated the performance of WRDensity along a number of dimensions, including:
- · Performance was assessed by comparing the Breast Density Level output to the radiologist consensus using accuracy, quadratically-weighted Cohen's kappa, and confusion matrices. Performance on the four-class task and binary task, i.e. dense (BI-RADS C+D) vs. non-dense (BI-RADS A+B) were both assessed.
- Consistency was assessed by evaluating the agreement, in terms of percentage of . cases, between the BDL for the mediolateral oblique (MLO) and craniocaudal (CC) views of the same breast.
- Reproducibility was assessed using the maximum root mean square error across all . images between the predicted probabilities produced from an initial processing run and those produced in a second processing run on the same testing data.
The output of WRDensity was compared against a consensus of five expert radiologists who independently assessed breast density on a test dataset that represented all compatible modalities and patient populations. The test dataset comprised 871 exams from unique patients. On the four-class task, WRDensity achieved a quadratically-weighted Cohen's kappa of 0.90, 95% confidence interval [0.88, 0.92]. A confusion matrix demonstrating the level of agreement between the BDL and the radiologist consensus for each BI-RADS breast density category can be found in Figure 1.
8
Image /page/8/Figure/0 description: The image is a confusion matrix showing the relationship between WRDensity and Consensus. The matrix is a 4x4 grid, with each cell showing the percentage and number of observations. The diagonal elements show the percentage of agreement between WRDensity and Consensus, with values of 82%, 90%, 85%, and 85%. The off-diagonal elements show the percentage of disagreement between WRDensity and Consensus.
Figure 1: Confusion matrix comparing the performance of WRDensity against the radiologist consensus assessment of breast density for the four-class BI-RADS breast density task. The number of exams within each bin is shown in parentheses.
On the binary task, WRDensity achieved a Cohen's kappa of 0.88, 95% confidence interval [0.85, 0.91]. The confusion matrix is presented in Figure 2.
9
Image /page/9/Figure/0 description: The image is a confusion matrix comparing WRDensity and Consensus. The matrix shows the percentage and number of cases for each combination of non-dense and dense classifications. For non-dense classifications, 93% (406) are correctly classified, while 7% (29) are misclassified as dense. For dense classifications, 95% (414) are correctly classified, while 5% (22) are misclassified as non-dense.
Figure 2: Confusion matrix comparing the performance of WRDensity against the radiologist consensus assessment of breast density for the binary breast density task, dense (BI-RADS C+D) vs. non-dense (BI-RADS A+B). The number of exams within each bin is shown in parentheses.
5.8 Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics, and performance testing, WRDensity raises no new questions of safety or effectiveness and is substantially equivalent to the predicate device in terms of safety, efficacy, and performance.