(104 days)
Yes
The device description explicitly states that 'Deep learning' algorithms are applied and trained with a large database, which is a form of machine learning.
No.
This device is a diagnostic aid, providing information to physicians for interpreting medical images and assessing the likelihood of malignancy. It does not directly provide therapy or treatment.
Yes
Explanation: The "Intended Use/Indications for Use" section states that the software is used "to identify regions suspicious for breast cancer and assess their likelihood of malignancy," and the "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." This directly describes diagnostic capabilities as it's involved in identifying and assessing the likelihood of a disease.
Yes
The device description explicitly states "Transpara™ is a software only application". While it processes images from hardware devices (mammography systems) and outputs to other hardware (workstations, PACS, RIS), the device itself, as described, is solely the software application.
Based on the provided information, Transpara™ software is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens taken from the human body, such as blood, urine, or tissue, to provide information about a person's health.
- Transpara™'s Function: Transpara™ analyzes medical images (mammograms and digital breast tomosynthesis exams) that are acquired directly from the patient's body. It does not process biological specimens.
- Intended Use: The intended use clearly states it's a "concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams." This involves image analysis, not laboratory testing of biological samples.
Therefore, Transpara™ falls under the category of medical imaging software or a computer-aided detection (CAD) device, not an IVD.
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
QDQ
Device Description
Transpara™ is a software only application designed to be used by physicians to improve interpretation of digital mammography and digital breast tomosynthesis. The system is intended to be used as a concurrent reading aid to help readers with detection and characterization of potential abnormalities suspicious for breast cancer and to improve workflow. 'Deep learning' algorithms are applied to FFDM images and DBT slices 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 during reading (concurrent use):
- a) Computer aided detection (CAD) marks to highlight locations where the device detected suspicious calcifications or soft tissue lesions.
- b) Decision support is provided by region scores on a scale ranging from 0-100, with higher scores indicating a higher level of suspicion.
- c) Links between corresponding regions in different views of the breast, which may be utilized to enhance user interfaces and workflow.
- d) An exam score which categorizes exams on a scale of 1-10 with increasing likelihood of cancer. The score is calibrated in such a way that approximately 10 percent of mammograms in a population of mammograms without cancer falls in each category.
Results of Transpara™ are computed in processing server which accepts mammograms or DBT exams 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. Use of the device is supported for images from the following modality manufacturers: FFDM (Hologic, Siemens, General Electric, Philips, Fujifilm) and DBT (Hologic, Siemens). Common destinations are medical workstations, PACS and RIS. Transpara™ is offered as a virtual machine and runs on pre-selected standard PC hardware as well as a dedicated virtual machine cluster. 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
Yes
Input Imaging Modality
Full-field digital mammography exams, digital breast tomosynthesis exams
Anatomical Site
Breast
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Intended users of Transpara™ are physicians qualified to read screening mammography exams and digital breast tomosynthesis exams.
Transpara™ is a software medical device for use in a 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
Validation testing consisted of determining stand-alone performance of the algorithms in Transpara 1.6.0 using an independent multi-vendor test-set of mammography and DBT exams acquired from multiple centers. This test dataset included mammography and DBT exams of women undergoing mammography acquired with devices from five manufacturers: Hologic, GE, Philips, Siemens, and Fujifilm.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
A pivotal reader study was conducted with Transpara 1.6.0.
The objective of the reader study was to determine whether the performance of radiologists detecting breast cancer in DBT exams increases when they concurrently use Transpara compared to when they read DBT exams unaided. In the study, both conditions are tested with a fully-crossed, multi-reader multi-case retrospective study performed by eighteen MQSA qualified radiologists. The study was conducted with an enriched sample of 240 Siemens Mammomat DBT exams, including 65 exams with breast cancer, 65 exams with benign abnormalities, and 110 normal exams.
The primary hypothesis for the study was superior breast-level area under the receiver operating characteristic curve (AUC, ROC) between conditions.
Statistical analysis of the reader study results showed that the primary objective and all pre-specified endpoints of the study were met. Radiologists significantly improved their detection performance when using Transpara™ to read DBT exams, with the average AUC increasing from 0.833 to 0.863 (P = 0.0025).
Standalone performance: Based on results of verification and validation tests it is concluded that Transpara™ is effective in the detection of soft lesions and calcifications at an appropriate safety level in mammograms acquired with mammography devices for which the software has been validated. Validation testing confirmed that algorithm performance is non-inferior or better in comparison to Transpara 1.3.0 for the four manufacturers for which the device was already cleared and that for Fuijfilm performance was non-inferior to the performance achieved on the pooled test data of devices cleared for use with the predicate device. Standalone performance tests with FFDM demonstrate that Transpara™ 1.6.0 achieves non-inferior or better detection performance compared to the predicate device.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Reader study: while reading time was significantly reduced and superior sensitivity was obtained with Transpara™. Secondary objectives of the reader study were to determine if the use of Transpara™ as an aid leads to: 1) reading time reduction, 2) non-inferior or higher sensitivity, 3) noninferior or higher specificity, and 4) reading time reduction on normal exams.
Predicate Device(s)
Transpara™ 1.3.0 (K181704)
Reference Device(s)
K192287 (Transpara™ 1.5.0)
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.
0
March 5, 2020
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & 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. % Umar Waqas, Ph.D. Head of Regulatory and Quality Affairs Mercator II, 7th floor, Toernooiveld 300 6525EC Nijmegen, Gelderland THE NETHERLANDS
Re: K193229
Trade/Device Name: Transpara™ 1.6.0 Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological computer assisted detection and diagnosis software Regulatory Class: Class II Product Code: QDQ Dated: January 28, 2020 Received: February 3, 2020
Dear Dr. Waqas:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/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.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
1
devices or postmarketing safety reporting (21 CFR 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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K193229
Device Name
Transpara™ 1.6.0
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 TransparaTM.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| ☑ Prescription Use (Part 21 CFR 801 Subpart D) | ☐ Over-The-Counter Use (21 CFR 801 Subpart C) |
CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number.
Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2020 See PRA Statement below.
3
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
Contact person:
Umar Waqas
Office: +31 24 3030045 | +31 24 2020020
Mobile: +31 6 44077104
Mercator II, 7th floor, Toernooiveld 300, 6525 EC Nijmegen, Netherlands
Date:
January 28, 2020
4
2. Device
| Device trade name | Transpara™ 1.6.0 |
|---|---|
| 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™ 1.3.0 (K181704) |
|---|---|
| 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 |
4. Device description
Transpara™ is a software only application designed to be used by physicians to improve interpretation of digital mammography and digital breast tomosynthesis. The system is intended to be used as a concurrent reading aid to help readers with detection and characterization of potential abnormalities suspicious for breast cancer and to improve workflow. 'Deep learning' algorithms are applied to FFDM images and DBT slices 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 during reading (concurrent use):
- a) Computer aided detection (CAD) marks to highlight locations where the device detected suspicious calcifications or soft tissue lesions.
5
- b) Decision support is provided by region scores on a scale ranging from 0-100, with higher scores indicating a higher level of suspicion.
- c) Links between corresponding regions in different views of the breast, which may be utilized to enhance user interfaces and workflow.
- d) An exam score which categorizes exams on a scale of 1-10 with increasing likelihood of cancer. The score is calibrated in such a way that approximately 10 percent of mammograms in a population of mammograms without cancer falls in each category.
Results of Transpara™ are computed in processing server which accepts mammograms or DBT exams 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. Use of the device is supported for images from the following modality manufacturers: FFDM (Hologic, Siemens, General Electric, Philips, Fujifilm) and DBT (Hologic, Siemens). Common destinations are medical workstations, PACS and RIS. Transpara™ is offered as a virtual machine and runs on pre-selected standard PC hardware as well as a dedicated virtual machine cluster. 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 and digital breast tomosynthesis.
6
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™ 1.6.0 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 judqement. Support for DBT has been added in the indications for use of the subject device compared to the indications for use of the predicate device. The algorithmic components have been updated to improve detection accuracy for FFDM and to enable processing of DBT. The subject device also supports Fujifilm FFDM systems, which was cleared in K192287 (Transpara™ 1.5.0).
The overall design of Transpara™ 1.6.0 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.
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 1.6.0. ScreenPoint applied the following voluntary FDA recognized standards and guidelines:
| Standard ID | Standard Title | FDA Recognition # |
|---|---|---|
| ISO 14971:2007 | Medical Devices - Application Of Risk | |
| Management To Medical Devices | 5-40 | |
| IEC 62304:2015 | Medical Device Software - Software Life | |
| Cycle Processes | 13-79 | |
| DEN180005 | Decision summary with special controls | |
| for class II radiology device |
7
The following quidance documents were used to support this submission:
- . Guidance for Industry and FDA Staff - Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices (Issued on May 11, 2005)
- Computer-Assisted Detection Devices Applied to Radiology Images and ● Radioloqy Device Data - Premarket Notification [510(k)] Submissions (Issued on July 3, 2012)
- . Guidance for Industry and FDA Staff - Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data - Premarket Approval (PMA) and Premarket Notification [510(k)] Submissions (Issued on July 3, 2012)
Transpara™1.6.0 is a software-only device. The level of concern for the device is determined as Moderate Level of Concern.
Stand-alone performance testing
Based on results of verification and validation tests it is concluded that Transpara™ is effective in the detection of soft lesions and calcifications at an appropriate safety level in mammograms acquired with mammography devices for which the software has been validated.
Verification testing consisted of software unit testing, software integration testing and software system testing. The verification test showed that the software application satisfied the software requirements.
Validation testing consisted of determining stand-alone performance of the algorithms in Transpara 1.6.0 using an independent multi-vendor test-set of mammography and DBT exams acquired from multiple centers. This test dataset included mammography and DBT exams of women undergoing mammography acquired with devices from five manufacturers: Hologic, GE, Philips, Siemens, and Fujifilm. Validation testing confirmed that algorithm performance is non-inferior or better in comparison to Transpara 1.3.0 for the four manufacturers for which the device was already cleared and that for Fuijfilm performance was non-inferior to the performance achieved on the pooled test data of devices cleared for use with the predicate device.
Based on results of verification and validation tests it is concluded that Transpara 1.6.0 is effective in the detection of soft lesions and calcifications at an appropriate safety level.
8
8. Summary of clinical tests
A pivotal reader study was conducted with Transpara 1.6.0. This study was designed to provide evidence for safety and effectiveness of the device.
The objective of the reader study was to determine whether the performance of radiologists detecting breast cancer in DBT exams increases when they concurrently use Transpara compared to when they read DBT exams unaided. In the study, both conditions are tested with a fully-crossed, multi-reader multi-case retrospective study performed by eighteen MQSA qualified radiologists. The study was conducted with an enriched sample of 240 Siemens Mammomat DBT exams, including 65 exams with breast cancer, 65 exams with benign abnormalities, and 110 normal exams.
The primary hypothesis for the study was superior breast-level area under the receiver operating characteristic curve (AUC, ROC) between conditions when radiologists use Transpara™ to read DBT exams at a significance level alpha 0.05.
Secondary objectives of the reader study were to determine if the use of Transpara™ as an aid leads to: 1) reading time reduction, 2) non-inferior or higher sensitivity, 3) noninferior or higher specificity, and 4) reading time reduction on normal exams. In addition, it was tested if standalone AUC performance of Transpara™ was non-inferior to the average AUC performance of the readers.
Statistical analysis of the reader study results showed that the primary objective and all pre-specified endpoints of the study were met. Radiologists significantly improved their detection performance when using Transpara™ to read DBT exams, with the average AUC increasing from 0.833 to 0.863 (P = 0.0025), while reading time was significantly reduced and superior sensitivity was obtained with Transpara™.
Results of the clinical study provide evidence for safety and effectiveness of Transpara™ when used in accordance with the indications for use.
9. Conclusions
The data presented in this 510(k) includes all required information to support the review by FDA. Standalone performance tests with FFDM demonstrate that Transpara™ 1.6.0 achieves non-inferior or better detection performance compared to the predicate device. For application with DBT, a clinical reader study and standalone tests demonstrate that the device is safe and effective.
9
ScreenPoint has applied a risk management process in accordance with FDA recognized standards to identify, evaluate, and mitigate all known hazards related to Transpara 1.6.0. 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.
Considering all data in this submission, the data provided in this 510(k) supports the safe and effective use of Transpara 1.6.0 for its indications for use and substantial equivalence to the predicate device.