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 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 Description

The ProFound Al® V3.0 device detects malignant soft-tissue densities and calcifications in digital breast tomosynthesis (DBT) images. The ProFound AI V3.0 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 AI V3.0 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 AI V3.0 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 indicate CAD's confidence that the finding is malignant. ProFound AI V3.0 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.

AI/ML Overview

The provided text describes specific acceptance criteria and the study conducted to demonstrate that ProFound AI® Software V3.0 meets these criteria.

1. Table of Acceptance Criteria and Reported Device Performance

The document states that the "Indications for Use" remain unchanged from the Predicate UNMODIFIED Device ProFound AI V2.1, and that the "technological characteristics of Modified Device, ProFound AI V3.0 remain unchanged from Unmodified Device ProFound AI V2.1 as the predicate." The key improvement for V3.0 is "software improvements leading to improved specificity for GE and Hologic modalities."

While specific numerical acceptance criteria (e.g., minimum sensitivity, minimum specificity) are not explicitly stated in a table format with target thresholds, the performance is assessed relative to the predicate device (ProFound AI V2.0/V2.1). The primary performance improvements demonstrated are in specificity.

Acceptance Criterion (Implicitly based on Predicate Equivalence)	Reported Device Performance (ProFound AI V3.0)
Non-inferiority in case sensitivity vs. ProFound AI V2.0/V2.1	Hologic DBT: The conclusion of non-inferiority of the standalone performance of ProFound AI V3.0 with a Hologic DBT screening population compared to the baseline performance of ProFound AI V2 with a Hologic DBT screening population in terms of case sensitivity, FP rate per 3D volume, and AUC. Claims established in the original Reader Study (K182373) apply to ProFound AI V3.0 with Hologic DBT. GE DBT: The conclusion of non-inferiority of the standalone performance of ProFound AI V3.0 with a GE DBT screening population compared to the baseline performance of ProFound AI V2 with a Hologic DBT screening population in terms of case sensitivity, FP rate per 3D volume, and AUC. Claims established in the original Reader Study (K182373) apply to ProFound AI V3.0 with GE DBT.
Improved specificity for GE and Hologic modalities	Hologic DBT: A paired comparison demonstrated a significant increase in specificity from V2.0 to V3.0. GE DBT: A paired comparison demonstrated a significant increase in specificity from V2.0 to V3.0.
Retention of original Indications for Use	Unchanged from ProFound AI V2.1.
Non-raising of different questions of safety and effectiveness	"These changes do not raise different questions of safety and effectiveness."

2. Sample Size Used for the Test Set and Data Provenance

The document refers to a "ProFound AI V2 Pivotal Reader Study Clinical Study Report (CSR) (K182373)". This reader study, performed for the predicate device, is the basis for the claims applicable to V3.0 regarding non-inferiority in sensitivity.

For the Hologic DBT Non-clinical Validation Testing and GE DBT Non-clinical Validation Testing for V3.0 itself, the document states, "A paired comparison assessed the performance of ProFound AI V3.0 on [Hologic/GE] DBT images to the performance of ProFound AI V2.0 on the same set of [Hologic/GE] DBT images," implying that the test set for these specificity comparisons consisted of images from both Hologic and GE systems, from which both V2.0 and V3.0 analyses were derived.

Sample Size: The exact number of cases or images in the test set specifically for the V3.0 validation studies (Hologic and GE paired comparisons) is not explicitly stated in the provided text. The non-inferiority claims rely on the original K182373 study, but its sample size is also not detailed here.
Data Provenance: The document does not specify the country of origin for the data. Since the device is U.S. FDA cleared, it is plausible the data is from the US, but this is not confirmed. The studies are described as "Non-clinical Validation Testing" and "Supplemental Standalone Study," indicating they are retrospective studies.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The provided text does not explicitly state the number of experts used to establish the ground truth or their specific qualifications for the test sets. It references "the original Reader Study described in 0074-6003. PowerLook® Tomo Detection V2 Pivotal Reader Study Clinical Study Report (CSR) (K182373)", which would have involved radiologists, but details are not provided here.

4. Adjudication Method for the Test Set

The adjudication method for establishing ground truth for the test sets is not explicitly stated in the provided text.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Was an MRMC study done?
- The document states that the claims established in the original Reader Study (K182373) for the predicate device (V2) apply to ProFound AI V3.0. This original study was likely an MRMC study to support the human-in-the-loop performance of V2.
- For V3.0 itself, the validation focuses on standalone performance comparisons between V2.0 and V3.0 to demonstrate non-inferiority in sensitivity and improvement in specificity. A new human reader study was not conducted specifically for V3.0 to re-evaluate human reader improvement.
Effect size of human readers improving with AI vs. without AI assistance: This information is not provided for V3.0, as the primary validation focused on the standalone performance of the algorithm and its non-inferiority/specificity improvement over its predecessor. The predicate device's MRMC study (K182373) would contain this information for V2.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study

Yes, standalone studies were done. The document explicitly refers to "ProFound AI V3.0 Hologic supplemental Standalone Study" and "ProFound AI V3.0 GE Supplemental Standalone Study." These studies compared the performance of V3.0 with V2.0/V2.1 on the same image sets.
The performance metrics assessed in these standalone studies included:
- Case sensitivity
- FP rate per 3D volume (False Positives)
- Area Under the localized Receiver Operating Characteristic (ROC) Curve (AUC)
- Specificity (which was shown to have a significant increase)

7. Type of Ground Truth Used

The type of ground truth used is not explicitly detailed in the provided text. However, for breast cancer detection, ground truth for such studies typically involves:

Expert Consensus: Multiple radiologists reviewing cases and reaching agreement.
Pathology: Biopsy-proven presence or absence of malignancy.
Follow-up Outcomes Data: Clinical follow-up over time to confirm benign or malignant status.

Given that the device detects "malignant soft-tissue densities and calcifications," it is highly likely that pathology (biopsy results) and/or expert radiologist consensus with follow-up were used to establish definitive ground truth regarding the presence and nature of cancers.

8. Sample Size for the Training Set

The document does not specify the sample size used for the training set of ProFound AI V3.0. It mentions that V3.0 uses "deep learning technology to process feature computations and uses pattern recognition to identify suspicious breast lesions," which implies a training phase, but details about the training data are absent.

9. How the Ground Truth for the Training Set Was Established

The document does not specify how the ground truth for the training set was established. Similar to the test set, it would likely involve expert annotations, pathology, and/or follow-up data.

Summary

{0}------------------------------------------------

March 12, 2021

Image /page/0/Picture/1 description: The image shows the logo for the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health and Human Services (HHS) logo on the left and the FDA logo on the right. The HHS logo is a stylized representation of a human figure, while the FDA logo is a blue rectangle with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.

iCAD Inc. % Heather Reed Vice President Quality Assurance and Regulatory Affairs 98 Spit Brook Road, Suite 100 NASHUA NH 03062

Re: K203822

Trade/Device Name: ProFound AI® Software V3.0 Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological computer assisted detection and diagnosis software Regulatory Class: Class II Product Code: QDQ Dated: February 16, 2021 Received: February 22, 2021

Dear Heather Reed:

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/cfpmp/bmn.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 801); 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

{1}------------------------------------------------

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,

Michael D. O'Hara
For

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) K203822

Device Name ProFound AI® Software V3.0

Indications for Use (Describe)

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)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

{3}------------------------------------------------

Image /page/3/Picture/0 description: The image is a logo for iCAD. The logo is in dark blue and features the letters "iCAD" in a stylized font. To the right of the letter "D" is a figure of a person with their arms raised above their head. A curved line extends from the bottom of the "D" and wraps around the figure.

510k Summary K203822

Date Prepared: February 16, 2021

Submitter:

iCAD, Inc. 98 Spit Brook Road Suite 100 Nashua, NH 03062

Contact Person:

Heather Reed Vice President, Quality Assurance and Regulatory Affairs Email: hreed@icadmed.com Phone: (603) 309-1945 Fax: (603) 880-3043

Device Name:

Trade Name:	ProFound AI® Software V3.0
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:	K191994
Manufacturer:	iCAD, Inc.
Device Name:	ProFound AI® V2.1

Device Description

{4}------------------------------------------------

Technical Characteristics:

Lesion Detection

ProFound AI V3.0 software detects soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in the 3D digital breast tomosynthesis images. The ProFound AI algorithm uses deep learning technology to process feature computations and uses pattern recogmition to identify suspicious breast lesions appearing as soft tissue densities or clusters of calcifications. Each detected region in the tomosynthesis data is identified or represented by marking the contour of the lesion in the tomosynthesis slice where it was detected.

Certainty of Finding Scores

Certainty of Finding scores are relative scores assigned to each detected region and a Case Score is assigned to each case regardless of the number of detected regions. Certainty of Finding and Case Scores are computed by the ProFound AI algorithm and represent the algorithm's confidence that a specific finding or case is malignant. The scores are represented on a 0% to 100% scale. Higher scores represent a higher algorithm confidence that a finding or case is malignant. Lower scores represent a lower algorithm confidence that a finding or case is malignant. The scores are based on a population with 50% prevalence of cancer and should be interpreted as the probability of the finding or case correctly being identified as malignant in a population of 50% cancers and 50% non-cancers. The scores serve as a guide to interpreting physicians to aid in determining if a suspicious finding or case needs further work-up. These scores are not intended to be the clinically used "probability of malignancy". Certainty of Finding and Case Scores are not calibrated to the prevalence in the intended use population or to the prevalence in the pivotal reader study outlined in the Assessment of Non-Clinical Performance Data section, and consequently, the Certainty of Finding and Case Scores are in general higher than the actual probability of malignancy in an intended use population with less than 50% prevalence. These scores represent a relative level of concern or level of suspicion because they do not represent an absolute clinical probability of malignancy.

Case Score

Each tomosynthesis study is assigned a Case Scores are the CAD algorithm's confidence that a study contains a malignant finding. Case Scores are represented on a 0%-100% scale. Higher Case Scores represent a higher confidence that the case contains a malignant finding. Lower Case Scores represent a lower confidence that the case contains a malignant finding.

Supported Digital Breast Tomosynthesis Systems

The following Digital Breast Tomosynthesis systems have been tested and are compatible with ProFound AI V3.0 software:

Hologic Selenia Dimensions/ 3Dimensions (Standard Resolution)
GE Senographe Essential with SenoClaire ●
GE Senographe Pristina ●
Siemens Inspiration both Standard and Empire Reconstruction ●

{5}------------------------------------------------

Siemens Revelation both Standard and Empire Reconstruction ●

Intended Use / "Indications for Use"

ProFound Al® 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.

Comparison with Predicate Device:
-----------------------------------	--	--	--

	UNMODIFIED DeviceProFound™ AI V2.1	MODIFIED DeviceProFound™ AI V3.0
Manufacturer	iCAD, Inc.	iCAD, Inc.
Classification Name	Radiological Computer AssistedDetection and Diagnosis Software	Radiological Computer AssistedDetection and Diagnosis Software
Regulation Number	21 CFR 892.2090	21 CFR 892.2090
Product Code	QDQ	QDQ
510(k) #	K191994	Pending
Intended Use / Indication forUse	ProFound™ AI V2.1 is acomputer-assisted detection anddiagnosis (CAD) software deviceintended to be used concurrentlyby interpreting physicians whilereading digital breasttomosynthesis (DBT) exams fromcompatible DBT systems. Thesystem detects soft tissuedensities (masses, architecturaldistortions and asymmetries) andcalcifications in the 3D DBTslices. The detections andCertainty of Finding and CaseScores assist interpretingphysicians in identifying softtissue densities and calcificationsthat may be confirmed ordismissed by the interpretingPhysician.	ProFound AI® V3.0 is acomputer-assisted detection anddiagnosis (CAD) software deviceintended to be used concurrentlyby interpreting physicians whilereading digital breasttomosynthesis (DBT) exams fromcompatible DBT systems. Thesystem detects soft tissuedensities (masses, architecturaldistortions and asymmetries) andcalcifications in the 3D DBTslices. The detections andCertainty of Finding and CaseScores assist interpretingphysicians in identifying softtissue densities and calcificationsthat may be confirmed ordismissed by the interpretingPhysician.
End User	Radiologists	Radiologists
Patient Population	Symptomatic and asymptomaticwomenundergoing mammography.	Symptomatic and asymptomaticwomenundergoing mammography.

{6}------------------------------------------------

	UNMODIFIED DeviceProFound™ AI V2.1	MODIFIED DeviceProFound™ AI V3.0
Mode of Action	Image processing device intendedto aid in the detection,localization, and characterizationof soft tissue densities (masses,architectural distortionsand asymmetries) andcalcifications in the 3D DBTslices.	Image processing device intendedto aid in the detection,localization, and characterizationof soft tissue densities (masses,architectural distortionsand asymmetries) andcalcifications in the 3D DBTslices.
Image Source Modalities	Digital breast tomosynthesisslices	Digital breast tomosynthesisslices
Output Device	Softcopy Workstation	Softcopy Workstation
Deployment	Standalone computer	Standalone computer
Supported Digital BreastTomosynthesis Systems	ProFound AI V2 Software:• Hologic SeleniaDimensions/3Dimensions• GE Senographe Esssentialwith SenoClaire• GE Senographe PristinaProFound AI V2.1 Software:• Siemens Inspiration bothStandard and EmpireReconstruction• Siemens Revelation bothStandard and EmpireReconstruction	Profound AI V3.0 Software:• Hologic SeleniaDimensions/3Dimensions• GE Senographe Esssentialwith SenoClaire• GE Senographe PristinaProFound AI V2.1 Software:• Siemens Inspiration bothStandard and EmpireReconstruction• Siemens Revelation bothStandard and EmpireReconstruction

Summary of Indications for Use:

The "Indications for Use" remain unchanged from the Predicate UNMODIFIED Device ProFound AI V2.1.

Summary of Technological Characteristic

The technological characteristics of Modified Device, ProFound AI V3.0 remain unchanged from Unmodified Device ProFound AI V2.1 as the predicate. Per 21 CFR 892.2090, both devices are radiological computer assisted detection and diagnostic software intended to aid in the detection, localization, and characterization of disease specific findings on acquired medical images. The outputs of both devices serve as a secondary or concurrent read and not a primary read. The output is used to inform the interpreting physician (who themselves make the primary diagnostic and patient management decisions) and will not replace the clinical expertise and judgment of the clinical user.

{7}------------------------------------------------

The difference in technological characteristics of ProFound AI V3.0 and the predicate device are software improvements leading to improved specificity for GE and Hologic modalities. These changes do not raise different questions of safety and effectiveness.

General Safety and Effectiveness Concerns

The device labeling contains instructions for use and any necessary cautions and warnings to

provide for safe and effective use of this device. Risk management is ensured via a risk analysis which is used to identify and mitigate potential hazards. Any potential hazards are controlled via software development, verification and validation testing. In addition, general controls of the FD&C Act, and special controls established for Radiological Computer Assisted Detection and Diagnosis Software are in place to further mitigate any safety and or effectiveness risks.

Assessment of Non-Clinical Performance Data

ProFound AI V3.0 has been verified and validated according to iCAD's design control processes. All supporting documentation has been included in this 510(k) Premarket Notification. Verification activity included unit, integration, and regression testing was performed. Lastly, ProFound AI V3.0 is deployed on a DICOM platform that has been successfully tested for clinical network integration.

Hologic DBT Non-clinical Validation Testing:

ProFound AI V3.0 Hologic Supplemental Standalone Study compared performance of ProFound AI V3.0 with Hologic DBT images to the baseline performance of ProFound AI V2 with Hologic DBT images in terms of case sensitivity. FP rate per 3D volume and Area Under the localized Receiver Operating Characteristic (ROC) Curve (AUC).

The conclusion of non-inferiority of the standalone performance of ProFound AI V3.0 with a Hologic DBT screening population compared to the baseline performance of ProFound AI V2 with a Hologic DBT screening population is that the claims established in the original Reader Study described in 0074-6003. PowerLook® Tomo Detection V2 Pivotal Reader Study Clinical Study Report (CSR) (K182373) also apply to ProFound AI V3.0 with Hologic DBT.

A paired comparison assessed the performance of ProFound AI V3.0 on Hologic DBT images to the performance of ProFound AI V2.0 on the same set of Hologic DBT images and demonstrated a significant increase in specificity from V2.0 to V3.0.

GE DBT Non-clinical Validation Testing:

ProFound AI V3.0 GE Supplemental Standalone Study compared performance of ProFound AI V3.0 with GE DBT images to the baseline performance of ProFound AI V2 with Hologic DBT images in terms of case sensitivity, FP rate per 3D volume and AUC.

The conclusion of non-inferiority of the standalone performance of ProFound AI V3.0 with a GE DBT screening population compared to the baseline performance of ProFound AI V2 with a Hologic DBT screening population is that the claims established in the original Reader Study described in 0074-6003, PowerLook® Tomo Detection V2 Pivotal Reader Study Report (CSR) (K182373) also apply to ProFound AI V3.0 with GE DBT.

{8}------------------------------------------------

A paired comparison assessed the performance of ProFound AI V3.0 on GE DBT images to the performance of ProFound AI V2.0 on the same set of GE DBT images and demonstrated a significant increase in specificity from V2.0 to V3.0.

Conclusion:

Based upon the information presented in this submission, it is concluded that ProFound AI V3.0 is substantially equivalent to the named predicate device.

Regulation Number and Section

§ 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.