Search Results

Transpara Density is a software application intended for use with data from compatible digital breast tomosynthesis systems. Transpara Density utilises deep learning artificial intelligence algorithms to automatically determine volumetric breast density (VBD), breast volume, and an ACR BI-RADS 5th Edition breast density category to aid health care professionals in the assessment of breast tissue composition. It is not a diagnostic aid.

Transpara Density is a software module that uses artificial intelligence techniques to assess breast density in mammography (DM) and breast tomosynthesis (DBT) images and provide support to radiologists in this task. The novel methods of Transpara Density, extend the capabilities of computer aided detection systems for mammography by providing radiologists with decision support via the output of density assessment.

The Transpara Density outputs are:

• Density Grade, in accordance with categories defined in the ACR BI-RADS Atlas 5th Edition (A = almost entirely fat; B = scattered fibroglandular densities; C = heterogeneously dense; and D = extremely dense)
• Volumetric Breast Density in % .
• . Breast volume in cm3

Transpara Density is designed as an optional feature of Transpara. To operate in a clinical environment the software must be embedded in a software application that generates output in standardized formats (e.g. DICOM) and handles communication with external devices (such as PACS systems).

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly derived from the performance goals demonstrated in the clinical studies.

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

• Accuracy (Physics Model & MRI):
  • Physics Model Comparison: 5,468 exams.
  • MRI Comparison: 190 exams.
• Reproducibility (CC vs. MLO, Left vs. Right Breast, FFDM vs. DBT):
  • CC vs. MLO and Left vs. Right Breast: 10,804 exams.
  • FFDM vs. DBT: 433 exams (where images of both modalities were available).
• Agreement with Human Readers (Main Study): 800 women (400 DM and 400 DBT examinations).
• Data Provenance:
  • The test data originated from multiple clinical centers in the US, UK, Turkey, and five EU countries (Netherlands, Sweden, Germany, Spain, Belgium, Italy).
  • The data collection sites are described as "representative for regular breast cancer screening and diagnostic assessment in hospitals."
  • The studies were retrospective, using existing data. The human reader study implies a retrospective collection of images to be reviewed.

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

• Number of Experts: Eight (8) MQSA-qualified radiologists.
• Qualifications of Experts: "MQSA-qualified radiologists according to the ACR BI-RADS Atlas 5th Edition." (MQSA stands for Mammography Quality Standards Act, indicating they are qualified to interpret mammograms clinically in the US).

4. Adjudication Method for the Test Set

• Panel Majority Vote: For each exam, a panel majority vote of the eight radiologists was computed to serve as the reference standard.
• Tie Resolution: Ties in the panel majority vote were resolved by taking the majority vote of the three most experienced radiologists in the panel.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described as being done to assess human reader improvement with AI assistance. The study focused on the standalone performance of the Transpara Density device against human reader consensus, not how the AI assists human readers.

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

• Yes, extensive standalone performance testing was done. The entire "Summary of non-clinical performance data" section describes the device's performance in terms of accuracy, reproducibility, and agreement with human readers, all reflecting the algorithm's direct output. The conclusion explicitly states: "Standalone performance tests demonstrated that requirements were met."

7. The Type of Ground Truth Used

• Expert Consensus (Proxy for Ground Truth): For the agreement with human readers, the ground truth for the ACR BI-RADS 5th Edition breast density category was established by a panel majority vote of eight MQSA-qualified radiologists, with tie-breaking by the three most experienced.
• Physics-Based Model / MRI Measurements (Reference for Accuracy): For the volumetric breast density (VBD) and breast volume (BV) accuracy assessments, the ground truth was based on:
  • A validated physics-based model described in literature (van Engeland 2006).
  • Volumetric measurements from breast MRI studies in the same patients.

8. The Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set. It only mentions that the "test data was not used for algorithm training and was not accessible to members of the research and development team."

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

• The document does not provide details on how the ground truth for the training set was established. It only indicates that the test data was separate from the training data.

Ask a specific question about this device