Transpara (2.1.0)

{0}  Screenpoint Medical B.V. Robin Barwegen VP of QA/RA Mercator II, 7th floor, Toernooiveld 300 Nijmegen, Gelderland 6525 EC NETHERLANDS August 14, 2026 Re: K260898 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: QDQ Dated: July 20, 2026 Received: July 20, 2026 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/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. U.S. Food & Drug Administration 10903 New Hampshire Avenue Silver Spring, MD 20993 www.fda.gov {1} K260898 - Robin Barwegen Page 2 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 Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13485 clause 8.3 (Nonconforming product), ISO 13485 clause 8.5.2 (Corrective action), and ISO 13485 clause 8.5.3 (Preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and ISO 13485 clause 7.5) and document changes and approvals in the Medical Device File (ISO 13485 clause 4.2.3). 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 (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-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (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-devices/device-advice-comprehensive-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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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-devices/device-advice-comprehensive-regulatory- {2} K260898 - Robin Barwegen Page 3 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, Digitally signed by Michael D. O'hara -S Date: 2026.08.14 15:44:02 -04'00' For 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 | | | | --- | --- | --- | | Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. | K260898 | ? | | Please provide the device trade name(s). | | ? | | Transpara (2.1.0) | | | | Please provide your Indications for Use below. | | ? | | 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. | | | | Please select the types of uses (select one or both, as applicable). | ☑ Prescription Use (21 CFR 801 Subpart D) ☐ Over-The-Counter Use (21 CFR 801 Subpart C) | ? | | Please select the age group(s) for which the device(s) is to be used. | ☐ Neonates/Newborns (Birth to < 29 days old) ☐ Infants (29 days old to < 2 years old) ☐ Children (2 years old to < 12 years old) ☐ Adolescents (12 years old to < 22 years old) ☑ Adults (22 years old and greater) | ? | {4} K260898 # 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. ## 1. Submitter **Manufacturer:** ScreenPoint Medical B.V. Mercator II, 7th floor Toernooiveld 300 6525 EC Nijmegen Netherlands www.screenpoint-medical.com **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:** July 20, 2026 Transpara® 510(k) Submission Red ID: BLW-REG-001-012 Revision: F {5} K260898 ## 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 | Special 510(k) | ## 3. Legally marketed predicate device | Device trade name | Transpara 2.1.0 | | --- | --- | | 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 | K241831 | ## 4. 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. Transpara® 510(k) Submission Red ID: BLW-REG-001-012 Revision: F {6} K260898 - 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. ## 5. Indications for use 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. Transpara® 510(k) Submission Red ID: BLW-REG-001-012 Revision: F {7} K260898 ### 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 modification consists of adding compatibility for the temporal analysis feature for DBT examinations acquired on GE systems with prior GE examinations, where the prior may include DBT, DBT + SM, or FFDM. 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 / Edition | Standard Title | FDA 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 | | 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 | Transpara® 510(k) Submission Red ID: BLW-REG-001-012 Revision: F {8} K260898 | 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. 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 Fujifilm), 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. Transpara® 510(k) Submission Red ID: BLW-REG-001-012 Revision: F {9} K260898 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. Table 1: Data used for evaluation of stand-alone performance | | 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) | 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 | Transpara® 510(k) Submission Red ID: BLW-REG-001-012 Revision: F {10} K260898 | Age Groups | Above 50 years old | 13024 | 11523 | 1498 | | --- | --- | --- | --- | --- | | | Below 50 years old | 2845 | 2637 | 208 | | Lesion Size | Mass <20 mm | 705 | 0 | 705 | | | Mass 20 - 50mm | 573 | 0 | 573 | | | 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 4,607 exams, including 742 exams with biopsy-proven cancer. An overview is presented in table 4. | | Number of Exams | Normal | Benign | Cancer | | --- | --- | --- | --- | --- | | FFDM | 3,476 | 2,993 | 52 | 431 | | DBT | 1,131 | 748 | 72 | 311 | | Total | 4,607 | 3,741 | 124 | 742 | 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 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 | 93.6% (90.7 - 96.0) | 90.0% (86.7 - 93.2) | 62.7% (57.8 - 68.0) | 0.918 (0.897 - 0.936) | | DBT with TA | 93.6% (90.7 - 96.0) | 90.7% (87.1 - 93.7) | 65.3% (60.2 - 70.6) | 0.924 (0.903 - 0.941) | 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 Transpara® 510(k) Submission Red ID: BLW-REG-001-012 Revision: F {11} K260898 Additional performance testing for Transpara cancer detection consisted of the sets described in table 6. For each sub-analysis the AUC is compared to Transpara without Temporal Analysis. The goal 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-cancer | Cancer | | --- | --- | --- | --- | --- | | Prior Time Intervals | 0.9 - 1.5 years | 2,981 | 2,614 | 367 | | | 1.5 - 6 years | 2,320 | 2,152 | 168 | | Age Groups | Under 50 years old | 857 | 808 | 49 | | | Between 50 - 65 years old | 2,539 | 2,219 | 320 | | | Above 65 years old | 1,276 | 1,001 | 275 | | Single vs Multi-prior | Single prior | 1,692 | 1,398 | 294 | | | Multi priors | 1,692 | 1,398 | 294 | | Breast Density | Low Density (BIRADS A + B) | 2,744 | 2,456 | 288 | | | High Density (BIRADS C + D) | 2,557 | 2,308 | 249 | | Modality of Prior Type for DBT current | FFDM Synthetic | 318 | 208 | 110 | | 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 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. Transpara® 510(k) Submission Red ID: BLW-REG-001-012 Revision: F {12} K260898 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 | | GE DBT + SM | GE DBT + SM, GE FFDM | ## 8. Conclusions 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. Transpara® 510(k) Submission Red ID: BLW-REG-001-012 Revision: F