(269 days)
Not Found
Yes
The device description mentions "Certainty of Finding" and "Case Scores" which are assigned by the algorithm to indicate confidence levels. This scoring mechanism, especially when compared to a predicate device named "ProFound AI V3.0", strongly suggests the use of AI/ML algorithms for image analysis and prediction. While the terms "AI" or "ML" are not explicitly used in the provided text, the context of a "computer-assisted detection and diagnosis (CAD) software device" that assigns confidence scores based on image analysis is highly indicative of AI/ML technology. The predicate device name "ProFound AI V3.0" further supports this inference.
No
The device is a computer-assisted detection and diagnosis (CAD) software that aids interpreting physicians in identifying potential abnormalities in breast tomosynthesis exams. It does not directly treat or prevent any medical condition.
Yes
The device is described as "computer-assisted detection and diagnosis (CAD) software." Its intended use involves assisting physicians in "identifying soft tissue densities and calcifications," and it provides "Certainty of Finding and Case Scores" to aid in this process, which are clearly diagnostic functions.
Yes
The device is explicitly described as a "computer-assisted detection and diagnosis (CAD) software device" and its function is to process digital images and provide scores and marks as overlays, which are software-based outputs. There is no mention of accompanying hardware components that are part of the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze biological samples: IVDs are designed to examine specimens taken from the human body, such as blood, urine, tissue, etc., to provide information about a person's health.
- This device analyzes medical images: ProFound Detection V4.0 analyzes digital breast tomosynthesis (DBT) images, which are medical images, not biological samples.
- The intended use is image analysis for detection and diagnosis assistance: The software assists physicians in interpreting images to identify potential findings, not by analyzing biological markers in a sample.
The device falls under the category of medical image analysis software, specifically a Computer-Assisted Detection and Diagnosis (CAD) device for breast imaging.
Yes
The letter explicitly states, "FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP)."
Intended Use / Indications for Use
ProFound Detection V4.0 is a computer-assisted detection and diagnosis (CAD) software device intended to be used concurrently by interpreting physicians while reading digital breast tomosynthesis (DBT) exams from compatible DBT system detects soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in the 3D DBT slices. The detections and Certainty of Finding and Case Scores assist interpreting physicians in identifying soft tissue densities and calcifications that may be confirmed or dismissed by the interpreting Physician.
Product codes (comma separated list FDA assigned to the subject device)
QDQ
Device Description
ProFound Detection V4.0 is a computer-assisted detection and diagnosis (CAD) software device that detects malignant soft-tissue densities and calcifications in digital breast tomosynthesis (DBT) images. The ProFound Detection V4.0 software allows an interpreting physician to quickly identify suspicious soft tissue densities and calcifications by marking the detected areas in the tomosynthesis images. When the ProFound Detection V4.0 marks are displayed by a user, the marks will appear as overlays on the tomosynthesis images. Each detected finding will also be assigned a "score" that corresponds to the ProFound Detection V4.0 algorithm's confidence that the detected finding is a cancer (Certainty of Finding). Certainty of Finding scores are a percentage in range of 0% to 100% to indicate CAD's confidence that the finding is malignant. ProFound Detection V4.0 also assigns a score to each case (Case Score) as a percentage in range of 0% to 100% to indicate CAD's confidence that the case has malignant findings. The higher the Certainty of Finding or Case Score, the higher the confidence that the detected finding is a cancer or that the case has malignant findings.
Mentions image processing
Yes
Mentions AI, DNN, or ML
This has been accomplished by changing the neural network architecture for each subsystem and retraining the model, and by the processing of prior images when they are available.
Input Imaging Modality
Digital breast tomosynthesis slices
Anatomical Site
Breast
Indicated Patient Age Range
Minimum: 35, Maximum: 90
Intended User / Care Setting
Interpreting physicians / Not Found
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
A standalone study was conducted, which evaluated the performance of ProFound Detection version 4.0 without an interpreting physician. There were 952 cases meeting the inclusion and exclusion criteria for this study that were retrospectively collected by iCAD from U.S. image acquisition sites. Hologic DBT system exam data was collected from multiple sites, in accordance with pre-specified protocols. These cases included 251 biopsy proven cancer cases with 256 malignant lesions and 701 non-cancer cases. The standalone performance of ProFound Detection V4.0 was evaluated by comparing its results to established reference standards. These reference standards were derived from clinical data including radiology report, follow-up biopsy and pathology data. Each cancer case was a biopsy proven positive, truthed by an expert breast imaging radiologist who outlined the location and extent of cancer lesions in the case. The dataset is representative of the mammography screening population for such subgroups as breast density, BI-RADS, cancer lesion size, appearance, and whether or not the cancer is invasive. The data was collected from sites that are independent of those included in the training and development. iCAD ensures the independence of this dataset by sequestering the data an keeping it separate from the test and development datasets.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Standalone Performance Assessment and Paired Analysis.
Sample size: 952 cases (251 cancer, 701 non-cancer).
AUC of ProFound Detection V4.0 using priors: 0.8753 (0.8475-0.9032).
AUC of ProFound Detection V4.0: 0.8714 (0.8423-0.9007).
AUC of Predicate (ProFound AI V3.0): 0.8230 (0.7878-0.8570).
Standalone Performance: A standalone study was conducted, which evaluated the performance of ProFound Detection version 4.0 without an interpreting physician.
Key results: ProFound Detection V4.0 was not inferior to the predicate device (V3.0) on the same independent dataset, considering performance metrics for Sensitivity, False Positives Per Image (FPPI) and Area Under the ROC Curve (AUC). While using prior exams in conjunction with current exams, ProFound Detection V4.0 achieved noninferiority over the predicate device (V3.0) for all primary endpoints, as well as achieving superiority in specificity.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity:
ProFound Detection V4.0 using priors: 0.9004 (0.8633-0.9374)
ProFound Detection V4.0: 0.9004 (0.8633-0.9374)
Predicate (ProFound AI V3.0): 0.8725 (0.8312-0.9138)
Specificity:
ProFound Detection V4.0 using priors: 0.6205 (0.5846-0.6565)
ProFound Detection V4.0: 0.5863 (0.5498-0.6228)
Predicate (ProFound AI V3.0): 0.5278 (0.4909-0.5648)
AUC:
ProFound Detection V4.0 using priors: 0.8753 (0.8475-0.9032)
ProFound Detection V4.0: 0.8714 (0.8423-0.9007)
Predicate (ProFound AI V3.0): 0.8230 (0.7878-0.8570)
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).
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act.
The PCCP in the subject device includes proposed modifications related to extending supported DBT image acquisition systems.
In accordance with the PCCP, the CAD algorithm will be trained, tuned, and locked prior to commercial release of the algorithm with the extended DBT image acquisition system(s) listed in the PCCP. Updates to the device Instructions for User Manual) will be made available, and include performance information per each DBT image acquisition system. DBT image acquisition systems in scope of the PCCP include Hologic 3Dimensions (Clarity HD), GE Senographe Essential with SenoClaire, GE Senographe Pristina, Siemens Mammomat Revelation both Standard and Empire Reconstruction, Siemens Mammomat Inspiration both Standard and Empire Reconstruction.
All DBT image acquisition systems will be subject to standalone performance protocols to measure the performance of said DBT image acquisition system. A secondary protocol will be utilized to confirm the system performance to be statistically non-inferior to a baseline system performance as defined in the PCCP.
§ 892.2090 Radiological computer-assisted detection and diagnosis software.
(a)
Identification. A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.(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 image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.
0
November 8, 2024
Image /page/0/Picture/1 description: The image shows the logo for 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, which 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.
iCAD, Inc. Spence Hartwell Sr. Regulatory Affairs Manager 98 Spit Brook Road. Suite 100 NASHUA NH 03062
Re: K240417
Trade/Device Name: ProFound Detection (V4.0) Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: QDQ Dated: October 2, 2024 Received: October 7, 2024
Dear Spence Hartwell:
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.
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an
1
established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device, or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (OS) 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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rele"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
2
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 medical devices and radiation-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
Enclosure
3
Indications for Use
Submission Number (if known)
Device Name
ProFound Detection (V4.0)
Indications for Use (Describe)
ProFound Detection V4.0 is a computer-assisted detection and diagnosis (CAD) software device intended to be used concurrently by interpreting physicians while reading digital breast tomosynthesis (DBT) exams from compatible DBT system detects soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in the 3D DBT slices. The detections and Certainty of Finding and Case Scores assist interpreting physicians in identifying soft tissue densities and calcifications that may be confirmed or dismissed by the interpreting Physician.
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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Image /page/4/Picture/0 description: The image shows the iCAD logo. The logo consists of a blue snowflake-like symbol on the left and the word "iCAD" in black on the right. The snowflake symbol has a dark blue center and light blue petals. The word "iCAD" is in a sans-serif font.
510(k) Summary K240417
Date Prepared: November 5, 2024
Submitter:
iCAD, Inc. 98 Spit Brook Road, Suite 100 Nashua, NH 03062 USA
Contact Person:
Spence Hartwell Sr. Regulatory Affairs Manager Email: shartwell(@icadmed.com Phone: (603) 882-5200
Device Information:
| Trade Name: | ProFound Detection V4.0 |
|---|---|
| Common Name: | Medical Imaging Software |
| Classification: | Radiological Computer Assisted Detection and Diagnosis Software |
| Product Code: | QDQ |
| Regulation Number: | 21 CFR 892.2090 |
| Review Panel: | Radiology |
Predicate Device:
| 510k Number: | K203822 |
|---|---|
| Manufacturer: | iCAD, Inc. |
| Device Name: | ProFound AI V3.0 |
Device Description
ProFound Detection V4.0 is a computer-assisted detection and diagnosis (CAD) software device that detects malignant soft-tissue densities and calcifications in digital breast tomosynthesis (DBT) images. The ProFound Detection V4.0 software allows an interpreting physician to quickly identify suspicious soft tissue densities and calcifications by marking the detected areas in the tomosynthesis images. When the ProFound Detection V4.0 marks are displayed by a user, the marks will appear as overlays on the tomosynthesis images. Each detected finding will also be assigned a "score" that corresponds to the ProFound Detection V4.0 algorithm's confidence that the detected finding is a cancer (Certainty of Finding). Certainty of Finding scores are a percentage in range of 0% to 100% to indicate CAD's confidence that the finding is malignant. ProFound Detection V4.0 also assigns a score to each case (Case Score) as a percentage in range of 0% to 100% to indicate CAD's confidence that the case has malignant findings. The higher the Certainty of Finding or Case Score, the higher the confidence that the detected finding is a cancer or that the case has malignant findings.
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Image /page/5/Picture/0 description: The image contains the logo for iCAD. The logo consists of a blue geometric design on the left and the text "iCAD" in black on the right. The geometric design is a stylized snowflake or starburst shape, with a central blue circle surrounded by smaller blue circles and curved shapes. The text "iCAD" is in a sans-serif font, with the "i" in lowercase and the "CAD" in uppercase.
Indications for Use/Intended Use
ProFound Detection V4.0 is a computer-assisted detection and diagnosis (CAD) software device intended to be used concurrently by interpreting physicians while reading digital breast tomosynthesis (DBT) exams from compatible DBT image acquisition systems. The system detects soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in the 3D DBT slices. The detections and Certainty of Finding and Case Scores assist interpreting physicians in identifying soft tissue densities and calcifications that may be confirmed or dismissed by the interpreting Physician.
| | Predicate Device
ProFound AI V3.0
K203822 | Subject Device
ProFound Detection V4.0
K240417 |
|---------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturer | iCAD, Inc. | iCAD, Inc. |
| Classification
Name | Radiological Computer Assisted
Detection and Diagnosis Software | Radiological Computer Assisted
Detection and Diagnosis Software |
| Regulation
Number | 21 CFR 892.2090 | 21 CFR 892.2090 |
| Product Code | QDQ | QDQ |
| Intended Use /
Indication for Use | ProFound AI® V3.0 is a computer-
assisted detection and diagnosis
(CAD) software device intended to be
used concurrently by interpreting
physicians while reading digital
breast tomosynthesis (DBT) exams
from compatible DBT systems. The
system detects soft tissue densities
(masses, architectural distortions and
asymmetries) and calcifications in the
3D DBT slices. The detections and
Certainty of Finding and Case Scores
assist interpreting physicians in
identifying soft tissue densities and
calcifications that may be confirmed
or dismissed by the interpreting
Physician. | ProFound Detection V4.0 is a
computer-assisted detection and
diagnosis (CAD) software device
intended to be used concurrently by
interpreting physicians while reading
digital breast tomosynthesis (DBT)
exams from compatible DBT systems.
The system detects soft tissue densities
(masses, architectural distortions and
asymmetries) and calcifications in the
3D DBT slices. The detections and
Certainty of Finding and Case Scores
assist interpreting physicians in
identifying soft tissue densities and
calcifications that may be confirmed or
dismissed by the interpreting
Physician. |
| End User | Radiologists | Radiologists |
| | Predicate Device
ProFound AI V3.0
K203822 | Subject Device
ProFound Detection V4.0
K240417 |
| Patient
Population | Symptomatic and asymptomatic
women undergoing mammography. | Symptomatic and asymptomatic
women undergoing mammography. |
| Mode of Action | Image processing device intended to
aid in the detection, localization, and
characterization of soft tissue
densities (masses, architectural
distortions and asymmetries) and
calcifications in the 3D DBT slices. | Image processing device intended to
aid in the detection, localization, and
characterization of soft tissue densities
(masses, architectural distortions and
asymmetries) and calcifications in the
3D DBT slices. |
| Image Source
Modalities | Digital breast tomosynthesis slices | Digital breast tomosynthesis slices |
| Output Device | Softcopy Workstation | Softcopy Workstation |
| Supported Digital
Breast
Tomosynthesis
Systems | • Hologic Selenia
Dimensions/3Dimensions
• Hologic 3Dimentions (Clarity HD)
• GE Senographe Essential with
SenoClaire
• GE Senographe Pristina
• Siemens Mammomat Inspiration
both Standard and Empire
Reconstruction
• Siemens Mammomat Revelation
both Standard and Empire
Reconstruction | • Hologic Selenia
Dimensions/3Dimensions |
| Certainty of
Finding and Case
Scores | • Represented on a 0% to 100%
scale
• Certainty of Finding relative score
assigned to each detected region
• Case Score assigned to each case
(regardless of the number of
detected regions) | • Represented on a 0% to 100% scale
• Certainty of Finding relative score
assigned to each detected region
• Case Score assigned to each case
(regardless of the number of
detected regions) |
| | Predicate Device
ProFound AI V3.0
K203822 | Subject Device
ProFound Detection V4.0
K240417 |
| View Processing
Component
Architecture | Implements view processing in three
steps. | Implements view processing in two
steps. |
| Processing of
Prior Exams | Not included | Included |
| CAD Output
Display | Marks displayed on the current exam. | Marks displayed on the current exam. |
| Maximum Marks
Per View | Unlimited | Three |
| Inclusion of PCCP | N/A | Included
The PCCP in the subject device
includes proposed modifications
related to extending supported DBT
image acquisition systems. |
Comparison with Predicate Device
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Image /page/6/Picture/0 description: The image contains the logo for iCAD. The logo consists of a blue circular design with eight curved lines extending outward, resembling a compass or star. To the right of the design is the text "iCAD" in a bold, sans-serif font, with the "i" in lowercase and the "CAD" in uppercase.
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Image /page/7/Picture/0 description: The image shows the logo for iCAD. The logo consists of a blue geometric design on the left and the text "iCAD" in black on the right. The geometric design is a stylized flower or star shape with a central blue circle and radiating blue petals or points. The text "iCAD" is in a bold, sans-serif font.
From the comparison above, the subject device and predicate device have the same Indications for Use and the same mode of action for image processing device intended to aid in the detection, localization, and characterization of soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in the 3D DBT slices. The minor differences in the devices do not raise different questions of safety or effectiveness.
The subject device includes a Predetermined Change Control Plan (PCCP) that extends supported DBT image acquisition systems. In accordance with the PCCP, the CAD algorithm will be trained, tuned, and locked prior to commercial release of the algorithm with the extended DBT image acquisition system(s) listed in the PCCP. Updates to the device Instructions for User Manual) will be made available, and include performance information per each DBT image acquisition system. DBT image acquisition systems in scope of the PCCP include Hologic 3Dimensions (Clarity HD), GE Senographe Essential with SenoClaire, GE Senographe Pristina, Siemens Mammomat Revelation both Standard and Empire Reconstruction, Siemens Mammomat Inspiration both Standard and Empire Reconstruction. The PCCP in the subject device with the proposed modifications related to extending supported DBT image acquisition systems do not raise different questions of safety and effectiveness from the predicate device.
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Image /page/8/Picture/0 description: The image contains the logo for iCAD. On the left side of the logo is a blue symbol that resembles a compass or a snowflake. To the right of the symbol is the text "iCAD" in black font. The "i" is lowercase, while "CAD" is uppercase.
All DBT image acquisition systems will be subject to standalone performance protocols to measure the performance of said DBT image acquisition system. A secondary protocol will be utilized to confirm the system performance to be statistically non-inferior to a baseline system performance as defined in the PCCP.
Assessment of Non-Clinical Performance Data
ProFound Detection V4.0 has been verified and validated. A Standalone Performance Assessment and Paired Analysis have demonstrated that ProFound Detection V4.0 has improved performance over the predicate device (V3.0). This has been accomplished by changing the neural network architecture for each subsystem and retraining the model, and by the processing of prior images when they are available. The primary endpoint of the assessment demonstrated that ProFound Detection V4.0 was not inferior to the predicate device (V3.0) on the same independent dataset, considering performance metrics for Sensitivity, False Positives Per Image (FPPI) and Area Under the ROC Curve (AUC). The secondary endpoint demonstrated that while using prior exams in conjunction with current exams, ProFound Detection V4.0 achieved noninferiority over the predicate device (V3.0) for all primary endpoints, as well as achieving superiority in specificity. CAD performance on the different versions is provided in the table below on the same independent data set:
| Sensitivity | Specificity | AUC | |
|---|---|---|---|
| ProFound Detection | |||
| V4.0 using priors | 0.9004 (0.8633-0.9374) | 0.6205 (0.5846-0.6565) | 0.8753 (0.8475-0.9032) |
| ProFound Detection | |||
| V4.0 | 0.9004 (0.8633-0.9374) | 0.5863 (0.5498-0.6228) | 0.8714 (0.8423-0.9007) |
| Predicate | |||
| (ProFound AI V3.0) | 0.8725 (0.8312-0.9138) | 0.5278 (0.4909-0.5648) | 0.8230 (0.7878-0.8570) |
A standalone study was conducted, which evaluated the performance of ProFound Detection version 4.0 without an interpreting physician. There were 952 cases meeting the inclusion and exclusion criteria for this study that were retrospectively collected by iCAD from U.S. image acquisition sites. Hologic DBT system exam data was collected from multiple sites, in accordance with pre-specified protocols. These cases included 251 biopsy proven cancer cases with 256 malignant lesions and 701 non-cancer cases. The standalone performance of ProFound Detection V4.0 was evaluated by comparing its results to established reference standards. These reference standards were derived from clinical data including radiology report, follow-up biopsy and pathology data. Each cancer case was a biopsy proven positive, truthed by an expert breast imaging radiologist who outlined the location and extent of cancer lesions in the case. The dataset is representative of the mammography screening population for such subgroups as breast density, BI-RADS, cancer lesion size, appearance, and whether or not the cancer is invasive. The data was collected from sites that are independent of those included in the training and
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development. iCAD ensures the independence of this dataset by sequestering the data an keeping it separate from the test and development datasets. Demographic information about the test population is provided in the table below:
| Cancer status | Malignant: 26% |
|---|---|
| Normal/benign: 74% | |
| Breast Density | Fatty: 65% |
| Dense: 35% | |
| Age (years old) | Minimum: 35 |
| Maximum: 90 | |
| Mean: 63 | |
| 25th percentile: 55 | |
| 75th percentile: 71 | |
| Race/Ethnicity | White, non-Hispanic: 59% |
| Black, non-Hispanic: 10% | |
| Asian or Pacific Islander, non-Hispanic:10% | |
| American Indian or Alaska Native, non-Hispanic: 0.3% | |
| Other or Multiracial, non-Hispanic: 8% | |
| Hispanic: 12% | |
| Imaging Modality | DBT: 100% |
| Modality Manufacturer | Hologic: 100% |
| Exam Dates (range) | 2018 - 2022 |
Unit and integration/system level (feature) testing was performed, and expected results were achieved.
Conclusion
The performance testing demonstrated that the subject device, ProFound Detection V4.0, can perform as well or better than the predicate device, ProFound AI V3.0. Therefore, the subject device is substantially equivalent to the predicate device.