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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. 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.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for Transpara 1.7.2:

Acceptance Criteria and Device Performance Study for Transpara 1.7.2

The primary study conducted to prove the device meets acceptance criteria was a standalone performance test demonstrating non-inferiority to the predicate device (Transpara 1.7.0).

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the non-inferiority claims to the predicate device in terms of breast cancer detection performance (sensitivity and ROC AUC) at specified false positive rates. While specific pass/fail thresholds for non-inferiority margin are not explicitly given in this document, the statement "non-inferior to the performance of the predicate device Transpara 1.7.0" implies that metrics must meet or exceed the predicate's performance within a defined statistical margin.

Conclusion: The study explicitly states, "Based on standalone testing it was concluded that Transpara 1.7.2 breast cancer detection performance for 2D and 3D mammograms of compatible devices is non-inferior to the performance of the predicate device Transpara 1.7.0."

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: 10,690 exams.
  • FFDM: 5,867 exams (4,841 Normal, 149 Benign, 877 Cancer)
  • DBT: 4,823 exams (3,988 Normal, 240 Benign, 595 Cancer)
• Data Provenance:
  • Acquisition: Acquired from multiple centers, collected from multiple clinical centers.
  • Geographic Origin: Seven EU countries and the US.
  • Retrospective/Prospective: The document does not explicitly state if the data was retrospective or prospective. However, the mention of "normal follow-up of at least one year" for inclusion of normal exams strongly suggests a retrospective collection of existing patient data.
  • Manufacturer Diversity: Included images from different manufacturers (2D: Hologic, GE, Philips, Siemens, Giotto and Fujifilm; 3D: Hologic, Siemens, General Electric and Fujifilm).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document does not specify the number of experts used to establish the ground truth or their qualifications. It only states that the training algorithms were "trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue." For the test set, it mentions "biopsy-proven cancer regions" and "normal follow-up of at least one year" for normal exams, indicating a reliance on clinical outcomes rather than expert consensus for ground truth.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method for establishing ground truth for the test set. The reliance on "biopsy-proven" and "normal follow-up" suggests that ground truth was clinical outcome-based rather than expert-adjudicated review of images for the testing purposes.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, a MRMC comparative effectiveness study was not reported as part of this 510(k) summary to directly show human readers improve with AI vs. without AI assistance. The study described is a standalone performance test comparison to a predicate device.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, a standalone performance test was conducted and described in detail. "Standalone performance tests were conducted to demonstrate substantial equivalence with the predicate device."

7. The Type of Ground Truth Used

The ground truth used for the test set appears to be primarily clinical outcome data:

• "biopsy-proven cancer regions" for positive cases.
• "normal follow-up of at least one year" for normal cases.

8. The Sample Size for the Training Set

The document mentions that "Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue," but it does not provide a specific sample size for the training set.

9. How the Ground Truth for the Training Set Was Established

The ground truth for the training set was established through:

• Biopsy-proven examples: For breast cancer and benign abnormalities.
• Clinical outcomes/follow-up: For examples of normal tissue.

This aligns with the ground truth establishment method for the test set, leveraging definitive clinical diagnoses (biopsy) and confirmed negative follow-up for normal cases.

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