K203822 · Icad, Inc. · QDQ · Mar 12, 2021 · Radiology
Device Facts
Record ID
K203822
Device Name
ProFound AI Software V3.0
Applicant
Icad, Inc.
Product Code
QDQ · Radiology
Decision Date
Mar 12, 2021
Decision
SESE
Submission Type
Special
Regulation
21 CFR 892.2090
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Soft tissue density and calcification detection
Deep learning technology with pattern recognition
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—
—
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ProFound AI V3.0 Hologic Supplemental Standalone Study
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Soft tissue density and calcification detection
Deep learning technology with pattern recognition
—
—
—
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ProFound AI V3.0 GE Supplemental Standalone Study
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Indications 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 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 AI V3.0 is a CAD software device for digital breast tomosynthesis (DBT) exams. It processes 3D DBT slices to detect soft tissue densities (masses, architectural distortions, asymmetries) and calcifications. Using deep learning and pattern recognition, the device marks suspicious regions as overlays on images and assigns a 'Certainty of Finding' score (0-100%) and a 'Case Score' (0-100%) representing the algorithm's confidence of malignancy. Used by radiologists at a softcopy workstation, the output serves as a concurrent read to assist in identifying findings for confirmation or dismissal. The device does not replace clinical judgment. It is deployed on a standalone computer platform integrated into clinical networks via DICOM. By highlighting suspicious areas and providing confidence scores, it aims to improve diagnostic specificity and assist in clinical decision-making for breast cancer screening.
Clinical Evidence
Bench testing only. Supplemental standalone studies compared ProFound AI V3.0 performance against V2.0 baseline using Hologic and GE DBT images. Metrics included case sensitivity, false positive (FP) rate per 3D volume, and Area Under the ROC Curve (AUC). Results demonstrated non-inferiority in sensitivity and a statistically significant increase in specificity for both Hologic and GE modalities compared to V2.0. Clinical claims are supported by the original pivotal reader study (K182373).
Technological Characteristics
Radiological CAD software; deep learning-based pattern recognition for lesion detection. Operates on standalone computer platform; DICOM-compliant. Compatible with Hologic (Selenia Dimensions/3Dimensions), GE (Senographe Essential/SenoClaire, Pristina), and Siemens (Inspiration, Revelation) DBT systems. Outputs include visual overlays and malignancy confidence scores (0-100%).
Indications for Use
Indicated for symptomatic and asymptomatic women undergoing mammography to assist radiologists in identifying soft tissue densities (masses, architectural distortions, asymmetries) and calcifications in 3D digital breast tomosynthesis (DBT) exams.
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.
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March 12, 2021
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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
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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
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# Indications for Use
510(k) Number (if known) K203822
Device Name ProFound AI® Software V3.0
#### Indications for Use (Describe)
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
| Type of Use (Select one or both, as applicable) | |
|-------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|
| <label><input checked="" type="checkbox"/> Prescription Use (Part 21 CFR 801 Subpart D)</label> | <label><input type="checkbox"/> Over-The-Counter Use (21 CFR 801 Subpart C)</label> |
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#### 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
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.
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## 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 ●
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- 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 Device<br>ProFound™ AI V2.1 | MODIFIED Device<br>ProFound™ AI V3.0 |
|--------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturer | iCAD, Inc. | iCAD, Inc. |
| Classification Name | Radiological Computer Assisted<br>Detection and Diagnosis Software | Radiological Computer Assisted<br>Detection and Diagnosis Software |
| Regulation Number | 21 CFR 892.2090 | 21 CFR 892.2090 |
| Product Code | QDQ | QDQ |
| 510(k) # | K191994 | Pending |
| Intended Use / Indication for<br>Use | ProFound™ AI V2.1 is a<br>computer-assisted detection and<br>diagnosis (CAD) software device<br>intended to be used concurrently<br>by interpreting physicians while<br>reading digital breast<br>tomosynthesis (DBT) exams from<br>compatible DBT systems. The<br>system detects soft tissue<br>densities (masses, architectural<br>distortions and asymmetries) and<br>calcifications in the 3D DBT<br>slices. The detections and<br>Certainty of Finding and Case<br>Scores assist interpreting<br>physicians in identifying soft<br>tissue densities and calcifications<br>that may be confirmed or<br>dismissed by the interpreting<br>Physician. | ProFound AI® V3.0 is a<br>computer-assisted detection and<br>diagnosis (CAD) software device<br>intended to be used concurrently<br>by interpreting physicians while<br>reading digital breast<br>tomosynthesis (DBT) exams from<br>compatible DBT systems. The<br>system detects soft tissue<br>densities (masses, architectural<br>distortions and asymmetries) and<br>calcifications in the 3D DBT<br>slices. The detections and<br>Certainty of Finding and Case<br>Scores assist interpreting<br>physicians in identifying soft<br>tissue densities and calcifications<br>that may be confirmed or<br>dismissed by the interpreting<br>Physician. |
| End User | Radiologists | Radiologists |
| Patient Population | Symptomatic and asymptomatic<br>women<br>undergoing mammography. | Symptomatic and asymptomatic<br>women<br>undergoing mammography. |
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| | UNMODIFIED Device<br>ProFound™ AI V2.1 | MODIFIED Device<br>ProFound™ AI V3.0 |
|---------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Mode of Action | Image processing device intended<br>to aid in the detection,<br>localization, and characterization<br>of soft tissue densities (masses,<br>architectural distortions<br>and asymmetries) and<br>calcifications in the 3D DBT<br>slices. | Image processing device intended<br>to aid in the detection,<br>localization, and characterization<br>of soft tissue densities (masses,<br>architectural distortions<br>and asymmetries) and<br>calcifications in the 3D DBT<br>slices. |
| Image Source Modalities | Digital breast tomosynthesis<br>slices | Digital breast tomosynthesis<br>slices |
| Output Device | Softcopy Workstation | Softcopy Workstation |
| Deployment | Standalone computer | Standalone computer |
| Supported Digital Breast<br>Tomosynthesis Systems | ProFound AI V2 Software:<br>• Hologic Selenia<br>Dimensions/3Dimensions<br>• GE Senographe Esssential<br>with SenoClaire<br>• GE Senographe Pristina<br>ProFound AI V2.1 Software:<br>• Siemens Inspiration both<br>Standard and Empire<br>Reconstruction<br>• Siemens Revelation both<br>Standard and Empire<br>Reconstruction | Profound AI V3.0 Software:<br>• Hologic Selenia<br>Dimensions/3Dimensions<br>• GE Senographe Esssential<br>with SenoClaire<br>• GE Senographe Pristina<br>ProFound AI V2.1 Software:<br>• Siemens Inspiration both<br>Standard and Empire<br>Reconstruction<br>• Siemens Revelation both<br>Standard and Empire<br>Reconstruction |
## 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.
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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.
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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.
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Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.
