(153 days)
Not Found
Yes
The device description explicitly states that "Deep learning algorithms are applied to images" and that these "Algorithms are trained with a large database". Deep learning is a subset of machine learning and artificial intelligence.
No
The device is a diagnostic aid that helps identify suspicious regions but does not directly treat or prevent a disease.
Yes
The device identifies regions suspicious for breast cancer and assesses their likelihood of malignancy, providing scores and categories indicating the likelihood that cancer is present, which clearly aligns with the definition of a diagnostic device.
Yes
The device description explicitly states "Transpara is a software only application". While it requires a "standalone processing appliance" and interacts with other systems (workstations, PACS, RIS), the device being submitted for 510(k) is the software itself, not the hardware it runs on or interacts with.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The description clearly states that Transpara processes images (mammograms) of the breast, not biological samples like blood, tissue, or urine.
- The intended use is as a concurrent reading aid for physicians interpreting medical images. This is a function related to medical imaging analysis, not laboratory testing of biological specimens.
- The device description focuses on image processing and AI algorithms applied to images. There is no mention of reagents, calibrators, controls, or other components typically associated with IVDs.
Therefore, Transpara falls under the category of a medical device, specifically a software medical device used in diagnostic imaging, rather than an In Vitro Diagnostic device.
No
The provided text does not explicitly state that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
Transpara software is intended for use as a concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams from compatible FFDM and DBT systems, to identify regions suspicious for breast cancer and assess their likelihood of malignancy. Output of the device includes locations of calcifications groups and soft-tissue regions, with scores indicating the likelihood that cancer is present, and an exam score indicating the likelihood that cancer is present in the exam. Patient management decisions should not be made solely on the basis of analysis by Transpara.
Product codes
ODO, QDQ
Device Description
Transpara is a software only application designed to be used by physicians to improve interpretation of full-field digital mammography (FFMD) and digital breast tomosynthesis (DBT). Deep learning algorithms are applied to images for recognition of suspicious calcifications and soft tissue lesions (including densities, masses, architectural distortions, and asymmetries). Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue.
Transpara offers the following functions which may be used at any time in the reading process, to improve detection and characterization of abnormalities and enhance workflow:
- · AI findings for display in the images to highlight locations where the device detects suspicious calcifications or soft tissue lesions, along with region scores per finding on a scale ranging from 1-100, with higher scores indicating a higher level of suspicion.
- Links between corresponding regions in different views of the breast, which may be utilized to enhance user interfaces and workflow.
- An exam-based score which categorizes exams with increasing likelihood of cancer on a scale of 1-10 or in three risk categories labeled as 'low', 'intermediate' or 'elevated'.
The concurrent use indication implies that it is up to the users to decide how to use Transpara in the reading process. Transpara functions can be used before, during or after visual interpretation of an exam by a user.
Results of Transpara are computed in a standalone processing appliance which accepts mammograms in DICOM format as input, processes them, and sends the processing output to a destination using the DICOM protocol in a standardized mammography CAD DICOM format. Common destinations are medical workstations, PACS and RIS. The system can be configured using a service interface. Implementation of a user interface for end users in a medical workstation is to be provided by third parties.
Mentions image processing
Yes
Mentions AI, DNN, or ML
AI, Deep learning algorithms
Input Imaging Modality
full-field digital mammography (FFDM), digital breast tomosynthesis (DBT)
Anatomical Site
Breast
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Physicians qualified to read screening mammography exams and digital breast tomosynthesis exams.
Healthcare facility or hospital.
Description of the training set, sample size, data source, and annotation protocol
Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue.
Description of the test set, sample size, data source, and annotation protocol
Standalone performance tests were conducted to demonstrate substantial equivalence with the predicate device. For these tests an independent dataset was used, which was acquired from multiple centers and had not been used for development of the algorithms. This testset contained FFDM and DBT mammograms acquired with devices from different manufacturers (FFDM: Hologic, GE, Philips, Siemens, and Fujifilm, DBT: Hologic, Siemens, GE and Fuiifilm), representative for breast imaging practices performing screening and diagnostic assessment, collected from multiple clinical centers in seven EU countries and the US. For the inclusion of the normal exams in the test set the majority of exams had a normal follow-up of at least one year.
The testset consisted of 10,207 exams, including 1,350 exams with biopsy-proven cancer.
| Number of Exams | Normal | Benign | Cancer | |
|---|---|---|---|---|
| FFDM | 5,730 | 4,830 | 150 | 750 |
| DBT | 4,477 | 3,757 | 120 | 600 |
| Total | 10,207 | 8,587 | 270 | 1,350 |
The testset for Temporal Analysis consists of 5,724 exams, including 643 exams with biopsy-proven cancer.
| Number of Exams | Normal | Benign | Cancer | |
|---|---|---|---|---|
| FFD | ||||
| M | 4,266 | 3,742 | 53 | 471 |
| DBT | 1,458 | 1,256 | 30 | 172 |
| Total | 5,724 | 4,998 | 83 | 643 |
Summary of Performance Studies
Standalone performance tests were conducted to demonstrate substantial equivalence with the predicate device. For these tests an independent dataset was used, which was acquired from multiple centers and had not been used for development of the algorithms. This testset contained FFDM and DBT mammograms acquired with devices from different manufacturers (FFDM: Hologic, GE, Philips, Siemens, and Fujifilm, DBT: Hologic, Siemens, GE and Fuiifilm), representative for breast imaging practices performing screening and diagnostic assessment, collected from multiple clinical centers in seven EU countries and the US. For the inclusion of the normal exams in the test set the majority of exams had a normal follow-up of at least one year.
The testset consisted of 10,207 exams, including 1,350 exams with biopsy-proven cancer.
Exam based sensitivity for cancer detection in the testset was computed by taking the fraction of cancers that were correctly localized in at least one view (MLO or CC). False positive rates were computed in exams without cancer, by dividing the number of regions detected per image by the number of images.
Table 2: Results overall stand-alone performance Transpara – Sensitivity and Area under the ROC Curve (AUC)
| | Sensitivity for
Sensitive Mode
(70% specificity) | Sensitivity for
Specific Mode
(80% specificity) | Sensitivity for
Elevated Risk
(97% specificity) | Exam-based AUC |
|------|--------------------------------------------------------|-------------------------------------------------------|-------------------------------------------------------|-----------------------|
| FFDM | 97.4% (96.3 - 98.5) | 95.2% (93.7 - 96.7) | 80.8% (78.0 - 83.6) | 0.960 (0.953 - 0.966) |
| DBT | 96.9% (95.5 - 98.3) | 95.1% (93.3 - 96.8) | 78.4% (75.1 - 81.7) | 0.955 (0.947 - 0.963) |
Additional performance testing for Transpara cancer detection consisted of sub-group analyses to ensure no deviations in specific sub-groups. The sub-groups include Ethnicity, Age Groups, Lesion Size, Radiological Lesion Subtypes, Histology Subtypes, Screening vs Diagnostic (DBT), and Breast Density. The results for the analysis do not show any deviations for specific sub-groups. The performance in the sub-groups meets the algorithm requirements.
Transpara 2.1.0 comes with the introduction of Temporal Analysis. The testset for this feature consists of 5,724 exams, including 643 exams with biopsy-proven cancer. Exam based sensitivity for cancer detection in the testset with temporal analysis was computed by taking the fraction of cancers that were correctly localized in at least one view (MLO or CC). False positive rates were computed in exams without cancer, by dividing the number of regions detected per image by the number of images.
Table 5: Results overall stand-alone performance Transpara with and without Temporal Analysis – Sensitivity and Area under the ROC Curve (AUC), where TA = Temporal Analysis
| | Sensitivity for
Sensitive Mode
(70% specificity) | Sensitivity for
Specific Mode
(80% specificity) | Sensitivity for
Elevated Risk
(97% specificity) | Exam-based AUC |
|-----------------|--------------------------------------------------------|-------------------------------------------------------|-------------------------------------------------------|-----------------------------------|
| FFDM without TA | 95.7% (93.7 - 97.6) | 94.5% (92.3 - 96.7) | 78.9% (75.0 - 82.8) | 0.954 (0.941 - 0.965) |
| FFDM with TA | 95.7% (93.7 - 97.6) | 95.4% (93.4 - 97.4) | 82.7% (79.1 - 86.4) | 0.958 (0.946 - 0.969) |
| DBT without TA | 94.6% (91.2 - 98.0) | 91.0% (86.7 - 95.4) | 67.1% (59.9 - 74.2) | 0.935 (0.915 - 0.953) |
| DBT with TA | 94.6% (91.2 - 98.0) | 91.0% (86.7 - 95.4) | 74.9% (68.3 - 81.4) | 0.941 (0.921 - 0.958) |
Additional performance testing for Transpara cancer detection with Temporal Analysis consisted of sub-group analyses: Prior Time Intervals, Age Groups, Single vs Multi-prior, Breast Density, Modality of Prior Type for DBT current, and Manufacturer Type. The results for the analysis do not show any deviations for specific sub-groups. The performance in the sub-groups meets the algorithm requirements.
Based on standalone testing without temporal comparison it was concluded that Transpara 2.1.0 breast cancer detection performance for FFDM and DBT mammograms of compatible devices is non-inferior and superior to the performance of the predicate device Transpara 1.7.2. With temporal comparison the performance of the device is superior to the performance without temporal comparison.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity, Specificity, Exam-based AUC
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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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November 25, 2024
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health and Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
ScreenPoint Medical B.V. Robin Barwegen VP of QA/RA Mercator II, 7th floor, Toernooiveld 300 Nijmegen, Gelderland 6525 EC NETHERLANDS
Re: K241831
Trade/Device Name: Transpara (2.1.0) Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: ODO Dated: October 25, 2024 Received: October 25, 2024
Dear Robin Barwegen:
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/cdrb/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.
1
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatory
2
assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
YANNA S. KANG-S
Yanna Kang, Ph.D. Assistant Director Mammography and Ultrasound Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
3
Indications for Use
Submission Number (if known)
K241831
Device Name
Transpara (2.1.0)
Indications for Use (Describe)
Transpara software is intended for use as a concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams from compatible FFDM and DBT systems, to identify regions suspicious for breast cancer and assess their likelihood of malignancy. Output of the device includes locations of calcifications groups and soft-tissue regions, with scores indicating the likelihood that cancer is present, and an exam score indicating the likelihood that cancer is present in the exam. Patient management decisions should not be made solely on the basis of analysis by Transpara.
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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K241831
510(k) Summary Transpara®
This 510(k) summary of safety and effectiveness information is prepared in accordance with the requirements of 21 CFR § 807.92.
Submitter 1.
Manufacturer:
ScreenPoint Medical B.V.
Mercator II, 7th floor
Toernooiveld 300
6525 EC Nijmegen
Netherlands
Contact person:
Robin Barwegen, VP of QA/RA
Office: +31 24 3030045 | +31 24 2020020
Mobile: +31 6 44077104
- Mercator II, 7th floor, Toernooiveld 300, 6525 EC Nijmegen, Netherlands
Date:
June 25, 2024
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2. Device
| Device trade name | Transpara 2.1.0 |
|---|---|
| Device | Radiological Computer Assisted Detection and Diagnosis |
| Software | |
| Classification regulation | 21 CFR 892.2090 |
| Panel | Radiology |
| Device class | II |
| Product code | QDQ |
| Submission type | Traditional 510(k) |
3. Legally marketed predicate device
| Device trade name | Transpara 1.7.2 |
|---|---|
| Legal Manufacturer | ScreenPoint Medical B.V. |
| Device | Radiological Computer Assisted Detection and Diagnosis |
| Software | |
| Classification regulation | 21 CFR 892.2090 |
| Panel | Radiology |
| Device class | II |
| Product code | QDQ |
| Clearance number | K221347 |
Device description 4.
Transpara is a software only application designed to be used by physicians to improve interpretation of full-field digital mammography (FFMD) and digital breast tomosynthesis (DBT). Deep learning algorithms are applied to images for recognition of suspicious calcifications and soft tissue lesions (including densities, masses, architectural distortions, and asymmetries). Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue.
Transpara offers the following functions which may be used at any time in the reading process, to improve detection and characterization of abnormalities and enhance workflow:
- · Al findings for display in the images to highlight locations where the device detects suspicious calcifications or soft tissue lesions, along with region scores per finding on a scale ranging from 1-100, with higher scores indicating a higher level of suspicion.
6
- Links between corresponding regions in different views of the breast, which may be utilized to enhance user interfaces and workflow.
- An exam-based score which categorizes exams with increasing likelihood of cancer on a scale of 1-10 or in three risk categories labeled as 'low', 'intermediate' or 'elevated'.
The concurrent use indication implies that it is up to the users to decide how to use Transpara in the reading process. Transpara functions can be used before, during or after visual interpretation of an exam by a user.
Results of Transpara are computed in a standalone processing appliance which accepts mammograms in DICOM format as input, processes them, and sends the processing output to a destination using the DICOM protocol in a standardized mammography CAD DICOM format. Common destinations are medical workstations, PACS and RIS. The system can be configured using a service interface. Implementation of a user interface for end users in a medical workstation is to be provided by third parties.
Indications for use 5.
Transpara is a software medical device for use in a healthcare facility or hospital with the following indications for use:
Transpara software is intended for use as a concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams from compatible FFDM and DBT systems, to identify regions suspicious for breast cancer and assess their likelihood of malignancy. Output of the device includes locations of calcifications groups and soft-tissue regions, with scores indicating the likelihood that cancer is present, and an exam score indicating the likelihood that cancer is present in the exam. Patient management decisions should not be made solely on the basis of analysis by Transpara.
Intended user population
Intended users of Transpara are physicians qualified to read screening mammography exams and digital breast tomosynthesis exams.
Intended patient population
The device is intended to be used in the population of women undergoing screening mammography or digital breast tomosynthesis.
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Warnings and precautions
Transpara is an adjunct tool and not intended to replace a physicians' own review of a mammogram. Decisions should not be made solely based on analysis by Transpara.
6. Predicate device comparison
The indication for use of Transpara 2.1.0 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 abnormalities. The devices are not intended as a replacement for the review of a physician or their clinical judgement.
The overall design of Transpara 2.1.0 is the same as that of the predicate device. Both versions detect and characterize findings in radiological breast images and provide information about the presence, location, and characteristics of the findings to the user in a similar way. There are differences in the algorithmic components, which have changed to improve detection accuracy for FFDM and DBT. Furthermore, temporal comparison has been included to allow a comparison of the current exam with prior exams. In case prior exams are assessed, the suspicious regions on the current exam will be adjusted based on the comparison with prior exams.
Changes do not raise different questions of safety and effectiveness of the device when used as labeled.
7. Summary of non-clinical performance data
In the design and development of Transpara 2.1.0, ScreenPoint applied the following voluntary FDA recognized standards and guidelines:
| Standard ID | Year / | Standard Title | FDA |
|---|---|---|---|
| Edition | Recognition # | ||
| IEC 62366-1 | Edition 1.1 | ||
| 2020-06 | Medical devices - Part 1: Application of usability | ||
| engineering to medical devices | 5-129 | ||
| ISO 20417 | First edition | ||
| 2021-04 | |||
| Corrected | |||
| version | |||
| 2021-12 | Medical devices - Information to be supplied by the | ||
| manufacturer | 5-135 | ||
| ISO 14971 | Third | ||
| Edition | |||
| 2019-12 | Medical Devices - Application Of Risk Management To | ||
| Medical Devices | 5-125 | ||
| IEC 62304 | Edition 1.1 | ||
| 2015-06 | Medical Device Software - Software Life Cycle | ||
| Processes | 13-79 | ||
| IEC 82304-1 | Edition 1.0 | ||
| 2016-10 | Health software - Part 1: General requirements for | ||
| product safety | 13-97 |
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| ISO 15223-1 | Fourth
edition
2021-07 | Medical devices - Symbols to be used with information
to be supplied by the manufacturer - Part 1: General
requirements | 5-134 |
| ------------- | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- | ------- |
|---|
The following guidance documents were used to support this submission:
| ID | Year | Title |
|---|---|---|
| FDA-1997-D- | ||
| 0029 | 2002 | General Principles of Software Validation |
| FDA-2011-D- | ||
| 0652 | 2014 | The 510(k) Program: Evaluating Substantial Equivalence in |
| Premarket Notifications [510(k)] | ||
| FDA-2015-D- | ||
| 5105 | 2016 | Postmarket Management of Cybersecurity in Medical Devices |
| FDA-2011-D- | ||
| 0469 | 2016 | Applying Human Factors and Usability Engineering to Medical |
| Devices | ||
| FDA-2015-D- | ||
| 4852 | 2017 | Design Considerations and Pre-market Submission |
| Recommendations for Interoperable Medical Devices | ||
| FDA-2014-D- | ||
| 0456 | 2018 | Appropriate Use of Voluntary Consensus Standards in Premarket |
| Submissions for Medical Devices | ||
| FDA-2018-D- | ||
| 1329 | 2019 | Recommended Content and Format of Non-Clinical Bench Performance |
| Testing Information in Premarket Submissions | ||
| FDA-2016-D- | ||
| 1853 | 2021 | Unique Device Identification System: Form and Content of the |
| Unique Device Identifier (UDI) | ||
| FDA-2009-D- | ||
| 0593 | 2022 | Computer-Assisted Detection Devices Applied to Radiology Images and |
| Radiology Device Data - Premarket Notification [510(k)] Submissions | ||
| FDA-2009-D- | ||
| 0503 | 2022 | Clinical Performance Assessment: Considerations for |
| Computer-Assisted Detection Devices Applied to Radiology Images and | ||
| Radiology Device Data in Premarket Notification (510(k)) Submissions | ||
| FDA-2019-D- | ||
| 1470 | 2022 | Technical Performance Assessment of Quantitative Imaging in |
| Radiological Device Premarket Submissions | ||
| FDA | ||
| 2021-D-1158 | 2023 | Cybersecurity in Medical Devices: Quality System Considerations and |
| Content of Premarket Submissions | ||
| FDA-2021-D- | ||
| 0775 | 2023 | Content of Premarket Submissions for Device Software Functions |
| FDA-2019-D- | ||
| 3598 | 2023 | Off-The-Shelf Software Use in Medical Devices |
| FDA-2023-D- | ||
| 1030 | 2023 | Cybersecurity in Medical Devices: Refuse to Accept Policy for Cyber |
| Devices and Related Systems Under Section 524B of the FD&C Act | ||
| FDA-2021-D- | ||
| 0872 | 2023 | Electronic Submission Template for Medical Device 510(k) Submissions |
Transpara 2.1.0 is a software-only device. The recommended documentation level is Basic Documentation Level.
Non-clinical performance tests
Verification testing was conducted, which consisted of software unit testing, software integration testing and software system testing. The verification tests showed that the software application satisfied the software requirements.
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Standalone performance tests were conducted to demonstrate substantial equivalence with the predicate device. For these tests an independent dataset was used, which was acquired from multiple centers and had not been used for development of the algorithms. This testset contained FFDM and DBT mammograms acquired with devices from different manufacturers (FFDM: Hologic, GE, Philips, Siemens, and Fujifilm, DBT: Hologic, Siemens, GE and Fuiifilm), representative for breast imaging practices performing screening and diagnostic assessment, collected from multiple clinical centers in seven EU countries and the US. For the inclusion of the normal exams in the test set the majority of exams had a normal follow-up of at least one year.
The testset consisted of 10,207 exams, including 1,350 exams with biopsy-proven cancer. An overview is presented in table 1. In the dataset for 2D images, 57% of the biopsy-proven cancer findings are characterized as soft tissue lesions, while 36% present with calcifications. The median diameter of the lesions is 16.34 mm. At the exam level, 44% of biopsy-proven cancer cases involve invasive ductal carcinoma, while 19% present only with ductal carcinoma in situ. For 3D images, 61% of the biopsy-proven cancer findings in the dataset are characterized as soft tissue lesions, while 34% present with calcifications. The median diameter of the lesions is 15.57 mm. At the exam level, 48% of biopsy-proven cancer cases involve invasive ductal carcinoma, while 17% present only with ductal carcinoma in situ.
| Number of Exams | Normal | Benign | Cancer | |
|---|---|---|---|---|
| FFDM | 5,730 | 4,830 | 150 | 750 |
| DBT | 4,477 | 3,757 | 120 | 600 |
| Total | 10,207 | 8,587 | 270 | 1,350 |
Exam based sensitivity for cancer detection in the testset was computed by taking the fraction of cancers that were correctly localized in at least one view (MLO or CC). False positive rates were computed in exams without cancer, by dividing the number of regions detected per image by the number of images. The results are demonstrated in the table below.
Table 2: Results overall stand-alone performance Transpara – Sensitivity and Area under the ROC Curve (AUC)
| | Sensitivity for
Sensitive Mode
(70% specificity) | Sensitivity for
Specific Mode
(80% specificity) | Sensitivity for
Elevated Risk
(97% specificity) | Exam-based AUC |
|------|--------------------------------------------------------|-------------------------------------------------------|-------------------------------------------------------|-----------------------|
| FFDM | 97.4% (96.3 - 98.5) | 95.2% (93.7 - 96.7) | 80.8% (78.0 - 83.6) | 0.960 (0.953 - 0.966) |
| DBT | 96.9% (95.5 - 98.3) | 95.1% (93.3 - 96.8) | 78.4% (75.1 - 81.7) | 0.955 (0.947 - 0.963) |
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Additional performance testing for Transpara cancer detection consisted of the sets described in table 3. For each sub-analysis the AUC as well as the sensitivities at the most important operating points (70% specificity, 80% specificity, 97% specificity) was compared. The goal of these sub-analysis was to ensure that Transpara does not show any deviations in specific sub-groups.
| Test Description | Subgroup | Total Number of Exams
(FFDM / DBT pooled) | Non-cancer | Cancer |
|----------------------------------|--------------------------------|----------------------------------------------|------------|--------|
| Ethnicity | White Non-Hispanic | 529 | 728 | 89 |
| | White Hispanic | 324 | 255 | 69 |
| | Black | 351 | 253 | 37 |
| | Turkish | 189 | 149 | 27 |
| | Asian | 601 | 544 | 57 |
| | Other | 360 | 203 | 108 |
| Age Groups | Above 50 years old | 13024 | 11523 | 1498 |
| | Below 50 years old | 2845 | 2637 | 208 |
| Lesion Size | Mass 50mm | 120 | 0 | 120 |
| Radiological Lesion
Subtypes | Mass | 917 | 0 | 917 |
| | Calcification Groups | 541 | 0 | 541 |
| | Architectural
Distortion | 663 | 0 | 663 |
| | Asymmetries | 52 | 0 | 52 |
| Histology Subtypes | ILC | 129 | 0 | 129 |
| | IDC | 432 | 0 | 432 |
| | DCIS | 183 | 0 | 183 |
| Screening vs
Diagnostic (DBT) | Screening | 824 | 679 | 145 |
| | Diagnostic | 3,931 | 3,454 | 477 |
| Breast Density | Low Density (BIRADS
A + B) | 5,154 | 4,582 | 572 |
| | High Density
(BIRADS C + D) | 4,982 | 4,263 | 719 |
Table 3: Data used for sub-group analysis of stand-alone performance for Transpara Cancer Detection
The results for the analysis do not show any deviations for specific sub-groups. The performance in the sub-groups meets the algorithm requirements.
Transpara 2.1.0 comes with the introduction of Temporal Analysis. The testset for this feature consists of 5.724 exams, including 643 exams with biopsy-proven cancer. An overview is presented in table 4.
| Number of Exams | Normal | Benign | Cancer | |
|---|---|---|---|---|
| FFD | ||||
| M | 4,266 | 3,742 | 53 | 471 |
| DBT | 1,458 | 1,256 | 30 | 172 |
| Total | 5,724 | 4,998 | 83 | 643 |
Table 4: Data used for evaluation of stand-alone performance of Temporal Analysis
Exam based sensitivity for cancer detection in the testset with temporal analysis was computed by taking the fraction of cancers that were correctly localized in at least one view (MLO or CC). False positive rates were computed in exams without cancer, by
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dividing the number of regions detected per image by the number of images. The results are demonstrated in the table below.
| | Sensitivity for
Sensitive Mode
(70% specificity) | Sensitivity for
Specific Mode
(80% specificity) | Sensitivity for
Elevated Risk
(97% specificity) | Exam-based AUC |
|-----------------|--------------------------------------------------------|-------------------------------------------------------|-------------------------------------------------------|-----------------------------------|
| FFDM without TA | 95.7% (93.7 - 97.6) | 94.5% (92.3 - 96.7) | 78.9% (75.0 - 82.8) | 0.954 (0.941 - 0.965) |
| FFDM with TA | 95.7% (93.7 - 97.6) | 95.4% (93.4 - 97.4) | 82.7% (79.1 - 86.4) | 0.958 (0.946 - 0.969) |
| DBT without TA | 94.6% (91.2 - 98.0) | 91.0% (86.7 - 95.4) | 67.1% (59.9 - 74.2) | 0.935 (0.915 - 0.953) |
| DBT with TA | 94.6% (91.2 - 98.0) | 91.0% (86.7 - 95.4) | 74.9% (68.3 - 81.4) | 0.941 (0.921 - 0.958) |
Table 5: Results overall stand-alone performance Transpara with and without Temporal Analysis – Sensitivity and Area under the ROC Curve (AUC), where TA = Temporal Analysis
Additional performance testing for Transpara cancer detection consisted of the sets described in table 3. For each sub-analysis the AUC is compared to Transpara without Temporal Analysis. The qoal of these sub-analysis is to ensure that Transpara does not show any deviations in specific sub-groups.
| Test Description | Subgroup | Total Number of Exams
(FFDM / DBT pooled) | Non-can
cer | Cancer |
|-------------------------------------------|--------------------------------|----------------------------------------------|----------------|--------|
| Prior Time Intervals | 0.9 - 1.5 years | 3,152 | 2,731 | 421 |
| | 1.5 - 6 years | 2,489 | 2,267 | 222 |
| Age Groups | Under 50 years old | 909 | 876 | 33 |
| | Between 50 - 65
years old | 2,796 | 2,532 | 264 |
| | Above 65 years old | 1,375 | 1,173 | 202 |
| Single vs Multi-prior | Single prior | 1,526 | 1,306 | 220 |
| | Multi priors | 1,526 | 1,306 | 220 |
| Breast Density | Low Density
(BIRADS A + B) | 2,915 | 2,573 | 342 |
| | High Density
(BIRADS C + D) | 2,728 | 2,425 | 303 |
| Modality of Prior Type for
DBT current | FFDM | 533 | 519 | 14 |
| | Synthetic | 927 | 761 | 166 |
| Manufacturer Type | Single Manufacturer | 3,213 | 2,852 | 361 |
| | Cross Manufacturer | 1,053 | 943 | 110 |
Table 6: Data used for sub-group analysis of stand-alone performance for Transpara with Temporal Analysis
The results for the analysis do not show any deviations for specific sub-groups. The performance in the sub-groups meets the algorithm requirements.
Based on standalone testing without temporal comparison it was concluded that Transpara 2.1.0 breast cancer detection performance for FFDM and DBT mammograms of compatible devices is non-inferior and superior to the performance of the predicate
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device Transpara 1.7.2. With temporal comparison the performance of the device is superior to the performance without temporal comparison.
The table below lists the manufacturers compatible with Transpara processing of exams without temporal comparison.
| Table 7 Compatible Manufacturers for standalone image processing | |||
|---|---|---|---|
| Standalone | |||
| FFDM | DBT | ||
| Compatible | |||
| manufacturers | Hologic, GE, Philips, | ||
| Siemens, and Fujifilm | Hologic, Siemens, GE | ||
| and Fujifilm |
The table below outlines the manufacturers compatible with Transpara for temporal comparison, including various combinations of mammography types,
Table 8 Compatible Manufacturer for temporal comparison
| Current | Prior |
|---|---|
| Siemens FFDM | Hologic FFDM, Siemens FFDM |
| GE FFDM | Hologic FFDM, GE FFDM |
| Hologic FFDM | Hologic FFDM, Siemens FFDM, GE FFDM |
| Hologic DBT + SM | Hologic DBT + SM, Hologic FFDM |
Conclusions 8.
The data presented in this 510(k) includes all required information to support the review by FDA. Standalone performance tests with FFDM and DBT demonstrate that Transpara 2.1.0 achieves non-inferior and superior detection performance compared to the predicate device.
ScreenPoint has applied a risk management process in accordance with FDA recognized standards to identify, evaluate, and mitigate all known hazards related to Transpara 2.1.0. These hazards may occur when accuracy of diagnosis is potentially affected, causing either false-positives or false-negatives. All identified risks are effectively mitigated and it can be concluded that the residual risk is outweighed by the benefits.
As described above, the subject device, Transpara 2.1.0, and the predicate device have the same intended use. While there are differences in technological characteristics, these differences were evaluated with performance testing as described and the technological differences do not raise different questions of safety and effectiveness. Therefore, the results of the testing demonstrate that the subject device, Transpara 2.1.0, is substantially equivalent to the predicate device.