(182 days)
Not Found
Yes
The device description explicitly states that MammoScreen is a "medical software device using artificial intelligence" and that "a choice of medical image processing and machine learning techniques are implemented."
No
The device aids in the interpretation of mammograms for diagnostic purposes, but it does not directly treat or provide therapy to a patient.
Yes
Explanation: The device is intended to assist physicians in interpreting screening mammograms by providing graphical marks of findings, suspicion scores, and location of findings. This information aids in identifying potential abnormalities and making a medical assessment, which are functions of a diagnostic device. The statement "Patient management decisions should not be made solely based on the analysis by MammoScreen 3" indicates it provides diagnostic information, but not the final diagnosis.
Yes
The device description explicitly states "MammoScreen is a concurrent reading medical software device". The summary focuses on software processing of images and integration with existing hardware (mammography systems, DICOM viewers, reporting systems) rather than including or requiring specific hardware components for its core functionality.
Based on the provided information, MammoScreen® 3 is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostics are devices intended for use in the examination of specimens derived from the human body in order to provide information for diagnostic, monitoring, or compatibility purposes. This typically involves analyzing biological samples like blood, urine, tissue, etc.
- MammoScreen's Function: MammoScreen® 3 processes medical images (mammograms), not biological specimens. It analyzes these images to identify potential findings and provide information to assist physicians in interpreting the images.
- Intended Use: The intended use clearly states it's a "concurrent reading and reporting aid for physicians interpreting screening mammograms." It's designed to help radiologists analyze images, not to perform tests on biological samples.
Therefore, MammoScreen® 3 falls under the category of a medical device that processes and analyzes medical images, rather than an In Vitro Diagnostic device.
No
The letter does not state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
MammoScreen® 3 is a concurrent reading and reporting aid for physicians interpreting screening mammograms. It is intended for use with compatible full-field digital mammography and digital breast tomosynthesis systems. The device can also use compatible prior examinations in the analysis.
Output of the device includes graphical marks of findings as soft-tissue lesions or calcifications on mammograms along with their level of suspicion scores. The lesion type is characterized as mass/ asymmetry, distortion, or calcifications for each detected finding. The level of suspicion score is expressed at the finding level, for each breast, and overall for the mammogram.
The location of findings including quadrant, depth, and distance from the nipple, is also provided. This adjunctive information is intended to assist interpreting physicians during reporting.
Patient management decisions should not be made solely based on the analysis by MammoScreen 3.
Product codes
QDQ
Device Description
MammoScreen is a concurrent reading medical software device using artificial intelligence to assist radiologists in the interpretation of mammograms.
MammoScreen processes the mammogram(s) and detects findings suspicious for breast cancer. Each detected finding gets a score called the MammoScreen Score™. The score was designed such that findings with a low score have a very low level of suspicion. As the score increases, so does the level of suspicion. For each mammogram, MammoScreen outputs detected findings with their associated score, a score per breast, driven by the highest finding score for each breast, and a score per case, driven by the highest finding score overall. The MammoScreen Score goes from one to ten.
MammoScreen is available for 2D (FFDM images) and 3D processing (FFDM & DBT or 2DSM & DBT). Optionally, MammoScreen can use prior examinations in the analysis. The following table describes the supported combinations:
| Current | ||||||
|---|---|---|---|---|---|---|
| FFDM | DBT | 2DSM | DBT+FFDM | DBT+2DSM | ||
| Prior | / | Supported | Unsupported | Unsupported | Supported | Supported |
| FFDM | Unsupported | Unsupported | Unsupported | Supported | Unsupported | |
| DBT | Unsupported | Unsupported | Unsupported | Unsupported | Unsupported | |
| 2DSM | Unsupported | Unsupported | Unsupported | Unsupported | Unsupported | |
| DBT + FFDM | Unsupported | Unsupported | Unsupported | Unsupported | Unsupported | |
| DBT + 2DSM | Unsupported | Unsupported | Unsupported | Unsupported | Unsupported |
MammoScreen can also aid in the reporting process by populating an initial report with chosen findings, including lesion type and position (quadrant, depth and distance to nipple).
The results indicating potential breast cancer, identified by MammoScreen, are accessible via a dedicated user interface and can seamlessly integrate into DICOM viewers (using DICOM-SC and DICOM-SR). Reporting aid outputs can be incorporated into the practice's reporting system to generate a preliminary report. Additionally, certain outputs like the case score can be reported into the patient management worklist.
Note that the MammoScreen outputs should be used as complementary information by radiologists while interpreting mammograms. For all cases, the medical professional interpreting the mammogram remains the sole decision-maker.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
full-field digital mammography, digital breast tomosynthesis, FFDM, DBT, 2DSM, 2D, 3D
Anatomical Site
Breast
Indicated Patient Age Range
Not Found
Intended User / Care Setting
physicians interpreting screening mammograms
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
A multi-reader multi-case study (MRMC) has been conducted to establish the clinical performance of MammoScreen 3. The study applied a fully crossed design, so that each case was red by each reader both with and without the aid of MammoScreen 3.
The study aimed to determine:
- Whether the performance of radiologists when using MammoScreen 3 is superior to unaided radiologist for interpretation of screening mammograms (primary objective).
- Whether the performance of MammoScreen 3 standalone is superior to unaided radiologist performance.
- Whether the performance of MammoScreen 3 standalone is non-inferior to aided radiologist performance.
The dataset included 240 combined DBT/2D mammograms (DBT+FFDM or DBT+2DSM) with a prior. Readers were all MQSA qualified and ABR certified radiologists (18 breast specialists and 5 general radiologists). All performances were evaluated at mammogram, breast and finding level and assessed in terms of Area Under the Receiver Operating Characteristic Curve (AUROC).
Results:
- Radiologists improved their diagnostic performance in breast cancer detection when using MammoScreen.
- The average value of AUC went from 0.797 [0.752 - 0.843] in unaided reading conditions to 0.871 [0.829 - 0.912] in reading conditions assisted by MammoScreen, leading to a statistically significant difference of +0.074 [0.047 - 0.101] (p-value 65 | 382 | 911 | 0.921 (0.902, 0.938) | 0.914 (0.885, 0.942) | 0.705 (0.669, 0.740) |
| By lesion type | Mass | 180 | 6,420 | 0.945 (0.927, 0.961) | 0.939 (0.902, 0.973) | 0.727 (0.715, 0.740) |
| | Calcifications | 271 | 6,420 | 0.926 (0.909, 0.942) | 0.912 (0.878, 0.941) | 0.727 (0.715, 0.740) |
| | Asymmetries | 117 | 6,420 | 0.884 (0.854, 0.911) | 0.835 (0.768, 0.897) | 0.727 (0.715, 0.740) |
| | Focal asymmetries | 262 | 6,420 | 0.921 (0.901, 0.938) | 0.902 (0.861, 0.936) | 0.727 (0.715, 0.740) |
| | Distortions | 223 | 6,420 | 0.945 (0.929, 0.960) | 0.938 (0.906, 0.969) | 0.727 (0.715, 0.740) |
| By lesion size | 30 mm | 318 | 6,420 | 0.959 (0.947, 0.971) | 0.959 (0.934, 0.983) | 0.727 (0.715, 0.740) |
| By lesion severity | BI-RADS 4 | 525 | 6,420 | 0.924 (0.910, 0.936) | 0.910 (0.884, 0.935) | 0.727 (0.715, 0.740) |
| | BI-RADS 5 | 178 | 6,420 | 0.972 (0.960, 0.982) | 0.983 (0.961, 1.000) | 0.727 (0.715, 0.740) |
| By current image combination | FFDM | 886 | 4,303 | 0.908 (0.896, 0.919) | 0.915 (0.895, 0.933) | 0.632 (0.615, 0.647) |
| | DBT + FFDM | 823 | 3,744 | 0.929 (0.919, 0.939) | 0.925 (0.905, 0.942) | 0.710 (0.694, 0.725) |
| | DBT + 2DSM | 161 | 2,114 | 0.904 (0.878, 0.927) | 0.858 (0.806, 0.903) | 0.762 (0.739, 0.785) |
| By prior image combination | FFDM | 782 | 4,216 | 0.926 (0.916, 0.936) | 0.901 (0.880, 0.921) | 0.761 (0.746, 0.776) |
| | DBT + FFDM | 534 | 1833 | 0.930 (0.918, 0.942) | 0.907 (0.882, 0.930) | 0.775 (0.755, 0.795) |
| | DBT + 2DSM | 80 | 921 | 0.903 (0.861, 0.937) | 0.838 (0.750, 0.912) | 0.767 (0.735, 0.797) |
| By current & prior image combination | No prior | 1,037 | 6,211 | 0.925 (0.915, 0.933) | 0.933 (0.918, 0.948) | 0.667 (0.653, 0.681) |
| | Current FFDM – Prior FFDM | 679 | 2,135 | 0.905 (0.891, 0.920) | 0.908 (0.887, 0.929) | 0.664 (0.643, 0.683) |
| | Current DBT+FFDM Prior FFDM | 630 | 1,717 | 0.932 (0.920, 0.943) | 0.914 (0.894, 0.935) | 0.767 (0.746, 0.788) |
| | Subgroup | Number of positive cases | Number of negative cases | AUROC (95% CI) | Sensitivity (95% CI) | Specificity (95% CI) |
| | Current DBT+FFDM | 482 | 1,586 | 0.928 (0.913, 0.941) | 0.906 (0.880, 0.932) | 0.771 (0.750, 0.791) |
| | Prior DBT+FFDM | | | | | |
| | Current DBT+FFDM Prior DBT+2DSM | 13 | 3 | 0.945 (0.769, 1.000) | 1.000 (1.000, 1.000) | 0.663 (0.000, 1.000) |
| | Current DBT+2DSM Prior FFDM | 120 | 1,196 | 0.908 (0.875, 0.936) | 0.875 (0.817, 0.933) | 0.764 (0.737, 0.789) |
| | Current DBT+2DSM Prior DBT+FFDM | 75 | 164 | 0.939 (0.905, 0.967) | 0.935 (0.867, 0.987) | 0.755 (0.680, 0.823) |
| | Current DBT+2DSM Prior DBT+2DSM | 65 | 878 | 0.890 (0.844, 0.931) | 0.817 (0.723, 0.908) | 0.770 (0.738, 0.799) |
| | Current FFDM - No prior | 878 | 4,167 | 0.899 (0.887, 0.911) | 0.911 (0.892, 0.930) | 0.611 (0.594, 0.628) |
| | Current DBT+FFDM No prior | 807 | 3,606 | 0.927 (0.917, 0.937) | 0.941 (0.924, 0.957) | 0.659 (0.642, 0.676) |
| | Current DBT+2DSM No prior | 158 | 2,040 | 0.902 (0.874, 0.928) | 0.893 (0.842, 0.943) | 0.689 (0.665, 0.712) |
| By prior time difference | 6 months ≤ prior
- The average value of AUC went from 0.797 [0.752 - 0.843] in unaided reading conditions to 0.871 [0.829 - 0.912] in reading conditions assisted by MammoScreen, leading to a statistically significant difference of +0.074 [0.047 - 0.101] (p-value 65 | 382 | 911 | 0.921 (0.902, 0.938) | 0.914 (0.885, 0.942) | 0.705 (0.669, 0.740) |
§ 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.
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August 1, 2024
Image /page/0/Picture/1 description: The image shows the logos of the Department of Health & Human Services and the U.S. Food & Drug Administration (FDA). The Department of Health & Human Services logo is on the left, featuring a stylized human figure. The FDA logo is on the right, with the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text. The logos are placed side by side.
Therapixel Quentin De Snoeck RA/QA Director/CISO 455 Promenade des Anglais NICE. 06200 FRANCE
Re: K240301
Trade/Device Name: MammoScreen® (3) Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: QDQ Dated: July 2, 2024 Received: July 2, 2024
Dear Quentin De Snoeck:
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.
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).
1
Your device is also subject to, among other requirements, the Quality System (QS) 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.
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 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,
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
2
DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
Indications for Use
Submission Number (if known)
Device Name
MammoScreen® (3)
Indications for Use (Describe)
MammoScreen® 3 is a concurrent reading and reporting aid for physicians interpreting screening mammograms. It is intended for use with compatible full-field digital mammography and digital breast tomosynthesis systems. The device can also use compatible prior examinations in the analysis.
Output of the device includes graphical marks of findings as soft-tissue lesions or calcifications on
mammograms along with their level of suspicion scores. The lesion type is characterized as mass/ asymmetry, distortion, or calcifications for each detected finding. The level of suspicion score is expressed at the finding level, for each breast, and overall for the mammogram.
The location of findings including quadrant, depth, and distance from the nipple, is also provided. This adjunctive information is intended to assist interpreting physicians during reporting.
Patient management decisions should not be made solely based on the analysis by MammoScreen 3.
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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510(k) Summary
This 510(k) summary is prepared in accordance with the requirements of 21 CFR § 807.92.
Applicant Information: Therapixel 455 Promenade des Anglais, 06200 Nice France Phone: +33 9 72 55 20 39 Submission Correspondent: Quentin de Snoeck RAQA Director/CISO Email: qdesnoeck@therapixel.com Phone: +33 9 72 55 20 39 Date Summary Prepared: August 01, 2024 Device Information: Trade Name: MammoScreen® Model: 3 Common Name: Computer-Assisted Detection Device Device Classification Name: Radiological Computer Assisted Detection/Diagnosis Software For Lesions Suspicious For Cancer Regulation Number: 892.2090 Class II Regulation Class:
Predicate Device:
Product Code:
Submission type 510(k) number:
The predicate device is MammoScreen, cleared under K211541 (Product code QDQ).
Traditional 510(k)
QDQ
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Device Description
MammoScreen is a concurrent reading medical software device using artificial intelligence to assist radiologists in the interpretation of mammograms.
MammoScreen processes the mammogram(s) and detects findings suspicious for breast cancer. Each detected finding gets a score called the MammoScreen Score™. The score was designed such that findings with a low score have a very low level of suspicion. As the score increases, so does the level of suspicion. For each mammogram, MammoScreen outputs detected findings with their associated score, a score per breast, driven by the highest finding score for each breast, and a score per case, driven by the highest finding score overall. The MammoScreen Score goes from one to ten.
MammoScreen is available for 2D (FFDM images) and 3D processing (FFDM & DBT or 2DSM & DBT). Optionally, MammoScreen can use prior examinations in the analysis. The following table describes the supported combinations:
| Current | ||||||
|---|---|---|---|---|---|---|
| FFDM | DBT | 2DSM | DBT+FFDM | DBT+2DSM | ||
| Prior | / | Supported | Unsupported | Unsupported | Supported | Supported |
| FFDM | Unsupported | Unsupported | Unsupported | Supported | Unsupported | |
| DBT | Unsupported | Unsupported | Unsupported | Unsupported | Unsupported | |
| 2DSM | Unsupported | Unsupported | Unsupported | Unsupported | Unsupported | |
| DBT + FFDM | Unsupported | Unsupported | Unsupported | Unsupported | Unsupported | |
| DBT + 2DSM | Unsupported | Unsupported | Unsupported | Unsupported | Supported |
MammoScreen can also aid in the reporting process by populating an initial report with chosen findings, including lesion type and position (quadrant, depth and distance to nipple).
The results indicating potential breast cancer, identified by MammoScreen, are accessible via a dedicated user interface and can seamlessly integrate into DICOM viewers (using DICOM-SC and DICOM-SR). Reporting aid outputs can be incorporated into the practice's reporting system to generate a preliminary report. Additionally, certain outputs like the case score can be reported into the patient management worklist.
Note that the MammoScreen outputs should be used as complementary information by radiologists while interpreting mammograms. For all cases, the medical professional interpreting the mammogram remains the sole decision-maker.
Indication for Use
MammoScreen 3 is a concurrent reading and reporting aid for physicians interpreting screening mammograms. It is intended for use with compatible full-field digital mammography and digital breast tomosynthesis systems. The device can also use compatible prior examinations in the analysis.
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Output of the device includes graphical marks of findings as soft-tissue lesions on mammograms along with their level of suspicion scores. The lesion type is characterized as mass/asymmetry, distortion, or calcifications for each detected finding. The level of suspicion score is expressed at the finding level, for each breast, and overall for the mammogram.
The location of findings, including quadrant, depth, and distance from the nipple, is also provided. This adjunctive information is intended to assist interpreting physicians during reporting.
Patient management decisions should not be made solely based on the analysis by MammoScreen 3.
| Predicate device (MammoScreen 2) | Subject device (MammoScreen 3) | ||
|---|---|---|---|
| Manufacturer | Therapixel | Therapixel | |
| Regulation number | 892.2090 | 892.2090 | |
| Product Code | QDQ | QDQ | |
| Intended Use | MammoScreen 2 is a concurrent reading and reporting aid for physicians interpreting screening mammograms. It is intended for use with compatible full-field digital mammography and digital breast tomosynthesis systems. Output of the device includes marks of findings on mammograms along with their type and level of suspicion scores. The level of suspicion score is expressed at the finding level, for each breast, and overall for the mammogram. | MammoScreen 3 is a concurrent reading and reporting aid for physicians interpreting screening mammograms. It is intended for use with compatible full-field digital mammography and digital breast tomosynthesis systems. The device can also use compatible prior examinations in the analysis. Output of the device includes graphical marks of findings as soft-tissue lesions or calcifications on mammograms along with their level of suspicion scores. The lesion type is characterized as mass/asymmetry, distortion, or calcifications for each detected finding. The level of suspicion score is expressed at the finding level, for each breast, and overall for the mammogram. The location of findings, including quadrant, depth, and distance from the nipple, is also provided. This adjunctive information is intended to assist interpreting physicians during reporting. Patient management decisions should not be made solely based on the analysis by MammoScreen 3. | |
| Predicate device (MammoScreen 2) | Subject device (MammoScreen 3) | ||
| Intended | user population | Physicians qualified to read mammograms. | Physicians qualified to read mammograms. |
| Intended | patient population | Women undergoing mammography. | Women undergoing mammography. |
| Anatomical Location | Breast | Breast | |
| Design | Software-only device | Software-only device |
Summary of Substantial Equivalence
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Predicate Device Comparison
The indication for use of MammoScreen 3 is similar to that of the predicate device. Both devices are intended for concurrent use by physicians interpreting breast images to help them with localizing and characterizing findings. The devices are not intended as a replacement for the review of a physician or their clinical judgment.
The predicate device and the subject device are two software versions of MammoScreen. They both rely on the same fundamental scientific technology. For both devices, a choice of medical image processing and machine learning techniques are implemented. The systems includes 'deep learning' modules for the detection of suspicious findings. These modules are trained with large databases of biopsy-proven examples of breast cancer and normal tissue.
Compared to the predicate device, MammoScreen 3 provides more detailed categories of finding characterization and a more detailed location of findings as a reporting aid component.
In addition, the algorithmic components have been updated to improve detection accuracy for the analysis. Moreover some additional inputs have been added to the MammoScreen 3 device compare to the predicate device. MammoScreen 3 takes into account current mammogram that can be FFDM or FFDM & DBT or 2DSM & DBT. MammoScreen 3 can also incorporate in its analysis one prior mammogram, acquired between 6 and 60 months before the current mammogram, whether it is FFDM or 2DSM & DBT.
The design changes of this new version of MammoScreen have been assessed and do not raise different questions of safety and effectiveness than the previous version.
| Technological
characteristic | Predicate
(MammoScreen 2)
device | Subject
(MammoScreen 3)
device |
|------------------------------------|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------|
| Type of artificial
intelligence | MammoScreen 2 is powered by artificial
intelligence/machine learning-based software algorithm | Same. |
| Level of suspicion | MammoScreen 2 outputs a level of
suspicion at the finding, breast and
case level. | Same. |
| Lesion type | For each detected finding
MammoScreen 2 classifies them as
soft tissue lesion or calcifications. | For each detected finding
MammoScreen 3 classifies them as
mass/asymmetry, distortion or
calcifications. |
7
| Technological
characteristic | Predicate
(MammoScreen 2) | Subject
(MammoScreen 3) |
|---------------------------------|--------------------------------------------------|---------------------------------------------------------------------------------------------------------------|
| Localization | MammoScreen 2 doesn't provide a
localization. | For each finding MammoScreen 3
provides a quadrant, a depth and a
distance to the nipple. |
| Inputs | FFMD or DBT | FFDM or 2DSM & DBT or FFDM
& DBT, with an optional prior
(FFDM or 2DSM & DBT) expect
for the former. |
Non-Clinical Testing
MammoScreen is a software-only device.
Tests have been performed in compliance with the following recognized consensus standards:
- IEC 62304:2006/A1:2016- Medical device software Software life-cycle processes ●
- IEC 62366-1:2015+AMD1:2020- Medical devices Application of usability engineering to medical ● devices.
MammoScreen 3 has successfully completed integration testing and beta validation. In addition, potential hazards have been evaluated and mitigated, and have acceptable levels.
Clinical Performance Data
A multi-reader multi-case study (MRMC) has been conducted to establish the clinical performance of MammoScreen 3. The study applied a fully crossed design, so that each case was red by each reader both with and without the aid of MammoScreen 3.
The study aimed to determine:
- . Whether the performance of radiologists when using MammoScreen 3 is superior to unaided radiologist for interpretation of screening mammograms (primary objective).
- . Whether the performance of MammoScreen 3 standalone is superior to unaided radiologist performance.
- . Whether the performance of MammoScreen 3 standalone is non-inferior to aided radiologist performance.
The dataset included 240 combined DBT/2D mammograms (DBT+FFDM or DBT+2DSM) with a prior. Readers were all MQSA qualified and ABR certified radiologists (18 breast specialists and 5 general
8
radiologists) All performances were evaluated at mammogram, breast and finding level and assessed in terms of Area Under the Receiver Operating Characteristic Curve (AUROC).
Results:
- . Radiologists improved their diagnostic performance in breast cancer detection when using MammoScreen.
- o The average value of AUC went from 0.797 [0.752 - 0.843] in unaided reading conditions to 0.871 [0.829 - 0.912] in reading conditions assisted by MammoScreen, leading to a statistically significant difference of +0.074 [0.047 - 0.101] (p-value 65 | 382 | 911 | 0.921 (0.902, 0.938) | 0.914 (0.885, 0.942) | 0.705 (0.669, 0.740) |
| By lesion type | Mass | 180 | 6,420 | 0.945 (0.927, 0.961) | 0.939 (0.902, 0.973) | 0.727 (0.715, 0.740) |
| | Calcifications | 271 | 6,420 | 0.926 (0.909, 0.942) | 0.912 (0.878, 0.941) | 0.727 (0.715, 0.740) |
| | Asymmetries | 117 | 6,420 | 0.884 (0.854, 0.911) | 0.835 (0.768, 0.897) | 0.727 (0.715, 0.740) |
| | Focal asymmetries | 262 | 6,420 | 0.921 (0.901, 0.938) | 0.902 (0.861, 0.936) | 0.727 (0.715, 0.740) |
| | Distortions | 223 | 6,420 | 0.945 (0.929, 0.960) | 0.938 (0.906, 0.969) | 0.727 (0.715, 0.740) |
| By lesion size | 30 mm | 318 | 6,420 | 0.959 (0.947, 0.971) | 0.959 (0.934, 0.983) | 0.727 (0.715, 0.740) |
| By lesion
severity | BI-RADS 4 | 525 | 6,420 | 0.924 (0.910, 0.936) | 0.910 (0.884, 0.935) | 0.727 (0.715, 0.740) |
| | BI-RADS 5 | 178 | 6,420 | 0.972 (0.960, 0.982) | 0.983 (0.961, 1.000) | 0.727 (0.715, 0.740) |
| By current
image
combination | FFDM | 886 | 4,303 | 0.908 (0.896, 0.919) | 0.915 (0.895, 0.933) | 0.632 (0.615, 0.647) |
| | DBT + FFDM | 823 | 3,744 | 0.929 (0.919, 0.939) | 0.925 (0.905, 0.942) | 0.710 (0.694, 0.725) |
| | DBT + 2DSM | 161 | 2,114 | 0.904 (0.878, 0.927) | 0.858 (0.806, 0.903) | 0.762 (0.739, 0.785) |
| By prior image
combination | FFDM | 782 | 4,216 | 0.926 (0.916, 0.936) | 0.901 (0.880, 0.921) | 0.761 (0.746, 0.776) |
| | DBT + FFDM | 534 | 1833 | 0.930 (0.918, 0.942) | 0.907 (0.882, 0.930) | 0.775 (0.755, 0.795) |
| | DBT + 2DSM | 80 | 921 | 0.903 (0.861, 0.937) | 0.838 (0.750, 0.912) | 0.767 (0.735, 0.797) |
| By current &
prior image
combination | No prior | 1,037 | 6,211 | 0.925 (0.915, 0.933) | 0.933 (0.918, 0.948) | 0.667 (0.653, 0.681) |
| | Current FFDM – Prior FFDM | 679 | 2,135 | 0.905 (0.891, 0.920) | 0.908 (0.887, 0.929) | 0.664 (0.643, 0.683) |
| | Current DBT+FFDM
Prior FFDM | 630 | 1,717 | 0.932 (0.920, 0.943) | 0.914 (0.894, 0.935) | 0.767 (0.746, 0.788) |
| | Subgroup | Number of
positive cases | Number of
negative cases | AUROC
(95% CI) | Sensitivity
(95% CI) | Specificity
(95% CI) |
| | Current DBT+FFDM | 482 | 1,586 | 0.928 (0.913, 0.941) | 0.906 (0.880, 0.932) | 0.771 (0.750, 0.791) |
| | Prior DBT+FFDM | | | | | |
| | Current DBT+FFDM
Prior DBT+2DSM | 13 | 3 | 0.945 (0.769, 1.000) | 1.000 (1.000, 1.000) | 0.663 (0.000, 1.000) |
| | Current DBT+2DSM
Prior FFDM | 120 | 1,196 | 0.908 (0.875, 0.936) | 0.875 (0.817, 0.933) | 0.764 (0.737, 0.789) |
| | Current DBT+2DSM
Prior DBT+FFDM | 75 | 164 | 0.939 (0.905, 0.967) | 0.935 (0.867, 0.987) | 0.755 (0.680, 0.823) |
| | Current DBT+2DSM
Prior DBT+2DSM | 65 | 878 | 0.890 (0.844, 0.931) | 0.817 (0.723, 0.908) | 0.770 (0.738, 0.799) |
| | Current FFDM - No prior | 878 | 4,167 | 0.899 (0.887, 0.911) | 0.911 (0.892, 0.930) | 0.611 (0.594, 0.628) |
| | Current DBT+FFDM
No prior | 807 | 3,606 | 0.927 (0.917, 0.937) | 0.941 (0.924, 0.957) | 0.659 (0.642, 0.676) |
| | Current DBT+2DSM
No prior | 158 | 2,040 | 0.902 (0.874, 0.928) | 0.893 (0.842, 0.943) | 0.689 (0.665, 0.712) |
| By prior time
difference | 6 months ≤ prior
- o The average value of AUC went from 0.797 [0.752 - 0.843] in unaided reading conditions to 0.871 [0.829 - 0.912] in reading conditions assisted by MammoScreen, leading to a statistically significant difference of +0.074 [0.047 - 0.101] (p-value 65 | 382 | 911 | 0.921 (0.902, 0.938) | 0.914 (0.885, 0.942) | 0.705 (0.669, 0.740) |