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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®.

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. 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.

Here's a breakdown of the acceptance criteria and study details for Transpara 1.7.0, based on the provided FDA 510(k) summary:

Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as quantitative thresholds in the provided document (e.g., "AUC must be >= X"). Instead, the primary acceptance criteria for standalone performance appear to be non-inferiority compared to the predicate device (Transpara 1.6.0) across various performance metrics. The stated goal for AUC was to be "higher [or] non-inferior" compared to the predicate device.

Study Proving Device Meets Acceptance Criteria

The study described is a standalone performance test designed to evaluate the non-inferiority of Transpara 1.7.0 compared to its predicate, Transpara 1.6.0.

1. Sample Size Used for the Test Set and Data Provenance:

   • Total Exams: 7882 non-cancer exams and 1240 cancer exams.
   • 2D FFDM Exams: 4797 non-cancer, 819 cancer.
   • DBT Exams: 3085 non-cancer, 421 cancer.
   • Data Provenance: Acquired from multiple centers in seven EU countries and the US. The data was "independent" and "had not been used for development of the algorithms." This implies a retrospective collection, as it was a pre-existing dataset used for validation. The device manufacturers represented in the dataset were Hologic, GE, Philips, Siemens, and Fujifilm for 2D, and Hologic, Siemens, and Fujifilm for 3D.

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

   • The document does not specify the number of experts or their qualifications used to establish the ground truth for the test set. It mentions that the training algorithms were based on a large database of "biopsy-proven examples," which strongly suggests that the ground truth for clinical relevance was established through biopsy results.

3. Adjudication Method for the Test Set:

   • The document does not explicitly describe an adjudication method for the ground truth of the test set by human experts. The ground truth seems to be established primarily through biopsy results as mentioned in the device description section ("Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue."). For cancer cases, biopsy directly provides the ground truth. For non-cancer cases, it's likely based on follow-up showing no malignancy or negative biopsy.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No, a multi-reader multi-case (MRMC) comparative effectiveness study (i.e., human readers with vs. without AI assistance) was not conducted or reported in this 510(k) summary. The study focused solely on the standalone performance of the algorithm and its comparison to its predicate.

5. Standalone (Algorithm Only) Performance Study:

   • Yes, a standalone performance study was conducted. The results reported (sensitivity, FP rates, and AUCs) are all measures of the algorithm's performance without human intervention.

6. Type of Ground Truth Used:

   • The primary ground truth used for both training and evaluation was biopsy-proven examples of breast cancer, benign abnormalities, and normal tissue. This also implies negative follow-up for normal or benign cases where biopsy wasn't performed. The report explicitly mentions "biopsy-proven."

7. Sample Size for the Training Set:

   • The document states, "Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue." However, it does not specify the exact sample size for the training set.

8. How Ground Truth for the Training Set Was Established:

   • The ground truth for the training set was established using "biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue." This indicates that clinical and pathological confirmation (biopsy results) were used to label the data for training the AI model.

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