ProFound Detection (V4.0)

K240417 · Icad, Inc. · QDQ · Nov 8, 2024 · Radiology

Device Facts

• Record ID: K240417
• Device Name: ProFound Detection (V4.0)
• Applicant: Icad, Inc.
• Product Code: QDQ · Radiology
• Decision Date: Nov 8, 2024
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 892.2090
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device, PCCP

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.

Device Story

ProFound Detection V4.0 is a CAD software device for radiologists reading digital breast tomosynthesis (DBT) exams. It processes 3D DBT slices to detect malignant soft-tissue densities and calcifications. The device uses a neural network architecture to analyze current exams and, optionally, prior exams. It outputs visual overlays on the workstation display marking detected findings. Each finding is assigned a 'Certainty of Finding' score (0-100%) and each case is assigned a 'Case Score' (0-100%) representing the algorithm's confidence in malignancy. These scores assist the physician in identifying suspicious areas for confirmation or dismissal. The device is used in clinical settings to improve diagnostic accuracy and efficiency. It includes a Predetermined Change Control Plan (PCCP) to allow for future expansion of supported DBT image acquisition systems.

Clinical Evidence

Bench testing only. A retrospective standalone study of 952 cases (251 cancer, 701 non-cancer) compared V4.0 to the predicate. V4.0 demonstrated improved performance: Sensitivity 0.9004 (vs 0.8725), Specificity 0.5863 (vs 0.5278), and AUC 0.8714 (vs 0.8230). With prior exams, V4.0 achieved 0.9004 sensitivity, 0.6205 specificity, and 0.8753 AUC. Data was independent of training/development sets.

Technological Characteristics

Software-based CAD for DBT. Uses neural network architecture (retrained/tuned for V4.0). Processes 3D DBT slices. Connectivity: Softcopy workstation integration. Output: Visual overlays with malignancy confidence scores (0-100%). Includes PCCP for expanding compatibility with specific Hologic, GE, and Siemens DBT systems.

Indications for Use

Indicated for symptomatic and asymptomatic women undergoing mammography to assist interpreting physicians in the detection, localization, and characterization of soft tissue densities (masses, architectural distortions, asymmetries) and calcifications in 3D DBT slices.

Regulatory Classification

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.

Special Controls

A radiological computer assisted detection and diagnosis software must comply with the following special controls: Design verification and validation must include: 1. i. A detailed description of the image analysis algorithm, including but not limited to 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 contain 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 but not limited to 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 1(iii) above. 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 as programs that addresses 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.

*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.

Predicate Devices

• ProFound AI V3.0 (K203822)

Submission Summary (Full Text)

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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

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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.

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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 ([e-mail]) or phone [phone] 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

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# Indications for Use

Submission Number (if known)

K240417

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: [phone]

#### 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<br>ProFound AI V3.0<br>K203822 | Subject Device<br>ProFound Detection V4.0<br>K240417 |
|---------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturer | iCAD, Inc. | iCAD, Inc. |
| Classification<br>Name | Radiological Computer Assisted<br>Detection and Diagnosis Software | Radiological Computer Assisted<br>Detection and Diagnosis Software |
| Regulation<br>Number | 21 CFR 892.2090 | 21 CFR 892.2090 |
| Product Code | QDQ | QDQ |
| Intended Use /<br>Indication for Use | ProFound AI® V3.0 is a computer-<br>assisted detection and diagnosis<br>(CAD) software device intended to be<br>used concurrently by interpreting<br>physicians while reading digital<br>breast tomosynthesis (DBT) exams<br>from compatible DBT systems. The<br>system detects soft tissue densities<br>(masses, architectural distortions and<br>asymmetries) and calcifications in the<br>3D DBT slices. The detections and<br>Certainty of Finding and Case Scores<br>assist interpreting physicians in<br>identifying soft tissue densities and<br>calcifications that may be confirmed<br>or dismissed by the interpreting<br>Physician. | ProFound Detection V4.0 is a<br>computer-assisted detection and<br>diagnosis (CAD) software device<br>intended to be used concurrently by<br>interpreting physicians while reading<br>digital breast tomosynthesis (DBT)<br>exams from compatible DBT systems.<br>The system detects soft tissue densities<br>(masses, architectural distortions and<br>asymmetries) and calcifications in the<br>3D DBT slices. The detections and<br>Certainty of Finding and Case Scores<br>assist interpreting physicians in<br>identifying soft tissue densities and<br>calcifications that may be confirmed or<br>dismissed by the interpreting<br>Physician. |
| End User | Radiologists | Radiologists |
| | Predicate Device<br>ProFound AI V3.0<br>K203822 | Subject Device<br>ProFound Detection V4.0<br>K240417 |
| Patient<br>Population | Symptomatic and asymptomatic<br>women undergoing mammography. | Symptomatic and asymptomatic<br>women undergoing mammography. |
| Mode of Action | Image processing device intended to<br>aid in the detection, localization, and<br>characterization of soft tissue<br>densities (masses, architectural<br>distortions and asymmetries) and<br>calcifications in the 3D DBT slices. | Image processing device intended to<br>aid in the detection, localization, and<br>characterization of soft tissue densities<br>(masses, architectural distortions and<br>asymmetries) and calcifications in the<br>3D DBT slices. |
| Image Source<br>Modalities | Digital breast tomosynthesis slices | Digital breast tomosynthesis slices |
| Output Device | Softcopy Workstation | Softcopy Workstation |
| Supported Digital<br>Breast<br>Tomosynthesis<br>Systems | • Hologic Selenia<br>Dimensions/3Dimensions<br>• Hologic 3Dimentions (Clarity HD)<br>• GE Senographe Essential with<br>SenoClaire<br>• GE Senographe Pristina<br>• Siemens Mammomat Inspiration<br>both Standard and Empire<br>Reconstruction<br>• Siemens Mammomat Revelation<br>both Standard and Empire<br>Reconstruction | • Hologic Selenia<br>Dimensions/3Dimensions |
| Certainty of<br>Finding and Case<br>Scores | • Represented on a 0% to 100%<br>scale<br>• Certainty of Finding relative score<br>assigned to each detected region<br>• Case Score assigned to each case<br>(regardless of the number of<br>detected regions) | • Represented on a 0% to 100% scale<br>• Certainty of Finding relative score<br>assigned to each detected region<br>• Case Score assigned to each case<br>(regardless of the number of<br>detected regions) |
| | Predicate Device<br>ProFound AI V3.0<br>K203822 | Subject Device<br>ProFound Detection V4.0<br>K240417 |
| View Processing<br>Component<br>Architecture | Implements view processing in three<br>steps. | Implements view processing in two<br>steps. |
| Processing of<br>Prior Exams | Not included | Included |
| CAD Output<br>Display | Marks displayed on the current exam. | Marks displayed on the current exam. |
| Maximum Marks<br>Per View | Unlimited | Three |
| Inclusion of PCCP | N/A | Included<br>The PCCP in the subject device<br>includes proposed modifications<br>related to extending supported DBT<br>image acquisition systems. |

## Comparison with Predicate Device

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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<br>V4.0 using priors | 0.9004 (0.8633-0.9374) | 0.6205 (0.5846-0.6565) | 0.8753 (0.8475-0.9032) |
| ProFound Detection<br>V4.0 | 0.9004 (0.8633-0.9374) | 0.5863 (0.5498-0.6228) | 0.8714 (0.8423-0.9007) |
| Predicate<br>(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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Image /page/9/Picture/0 description: The image contains the logo for iCAD. On the left side of the image is a blue circular design with a dot in the center and 8 lines extending from the center dot. The lines are connected to smaller dots. To the right of the design is the text "iCAD" in a sans-serif font. The "i" is lowercase and the "CAD" is uppercase.

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.