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DenSeeMammo is a software application intended for use with Full Field Digital Mammography systems.

DenSeeMammo estimates Bl-RADS breast density category by analyzing processed digital 2D mammograms using a fully automated comparison procedure.

DenSeeMammo provides a BI-RADS breast density 5th Edition category to aid radiologists in the assessment of breast density. DenSeeMammo produces adjunctive information. It is not a diagnostic aid since the final assessment of breast density category is made by an MQSA qualified interpreting physician.

DenSeeMammo core software has been built and tested on OS X based computers.

DenSeeMammo graphical use interface software has been built and tested on Windows, OS X and Linux based computers. DenSeeMammo is compatible for images obtained from GE Senographe Essentials and Hologic Selenia Dimension systems.

DenSeeMammo is a software application intended for use with Full Field Digital Mammography systems. DenSeeMammo estimates BI-RADS breast density category by analyzing processed digital 2D mammograms using a fully automated comparison procedure. DenSeeMammo provides a BI-RADS breast density 5th Edition category to aid radiologists in the assessment of breast density. DenSeeMammo produces adjunctive information. It is not a diagnostic aid since the final assessment of breast density category is made by an MQSA qualified interpreting physician. DenSeeMammo core software has been built and tested on OS X based computers. DenSeeMammo graphical use interface software has been built and tested on Windows, OS X and Linux based computers. DenSeeMammo v1.2 is compatible for images obtained from GE Senographe Essentials and Hologic Selenia Dimension systems.

The software use processed digital 2D mammograms in a fully automated comparison procedure that produces a BI-RADS breast density category. The software processes and analyses the image according to proprietary algorithms that allow comparison to qualified databases containing images previously visually assessed by radiologists. For each patient it provides measures of BI-RADS breast density category. DenSeeMammo provides results per patient based on the maximum density category of the two breasts.

The software works in a client-server mode and requires that the user computer be on the same local network as the server. Computations are made on the server part of the system that also provide the graphical user interface to display the results. A web browser is required to display the graphical user interface: to select the patients' images and to display the assessment of the breast density according to the BI-RADS standards and the similar images.

The software was developed using the Java-1.8 language as a 3-component web application. The software was developed following an adapted version of the model – view – controller software architectural pattern.

The device does not contact the patient, nor does it control any life-sustaining devices.

The provided text describes the DenSeeMammo software, a device that estimates BI-RADS breast density category from digital 2D mammograms. The document is a 510(k) summary, specifically for DenSeeMammo v1.2, which is an updated version of the previously cleared DenSeeMammo v1.0.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. A table of acceptance criteria and the reported device performance:

The document does not provide a formal table of explicit acceptance criteria with specific numerical targets for performance metrics (e.g., accuracy, sensitivity, specificity). Instead, it broadly states that "All verification and validation testing was successful in that established acceptance criteria was met for all of the tests conducted."

The performance testing section (Section 9) describes various tests performed to demonstrate the device's functionality and consistency. While it indicates that the tests were successful, it does not report specific quantitative performance metrics for DenSeeMammo v1.2 against radiologists' assessments, nor does it define what "established acceptance criteria" were.

Observed Performance (as described, but without numerical values):

2. Sample sizes used for the test set and the data provenance:

• Test Set Sample Size: The document repeatedly uses the phrase "substantial data sets" when referring to the images used for testing (e.g., "substantial data sets of two views images," "substantial data sets of one view images"). However, it does not specify the exact number of cases or images in these test sets.
• Data Provenance: The document does not explicitly state the country of origin of the data. It mentions compatibility with GE Senographe Essentials and Hologic Selenia Dimension systems, which are widely used globally, but offers no specific geographic information for the data sources. It is also unclear if the data was retrospective or prospective; however, given that it refers to images "previously assessed by MQSA-qualified radiologists," it strongly implies a retrospective collection of existing images.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: The document does not specify the exact number of MQSA-qualified radiologists used to establish the ground truth. It consistently refers to them in the plural ("radiologists").
• Qualifications of Experts: The experts are described as "MQSA-qualified radiologists." MQSA (Mammography Quality Standards Act) qualification is a specific requirement in the United States for interpreting mammography. This indicates a high level of expertise in mammography interpretation.

4. Adjudication method for the test set:

The document mentions "comparison to qualified databases containing images previously visually assessed by MQSA-qualified radiologists." It implies that assessments from these radiologists serve as the ground truth. However, it does not describe any specific adjudication method (e.g., 2+1, 3+1 consensus, or independent reads with a tie-breaker) if there were discrepancies among radiologists for the ground truth establishment. It simply states "previously visually assessed," suggesting a single or pre-determined ground truth per image.

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:

The document does not describe an MRMC comparative effectiveness study designed to assess how human readers improve with AI assistance versus without it. The device is intended to "aid radiologists in the assessment of breast density" and "produces adjunctive information," but the studies described focus on the standalone performance of the algorithm compared to radiologists' assessments, not on the human-AI team performance.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

Yes, a standalone performance assessment was conducted. The "Performance Testing" section states:

• "DenSeeMammo v1.2 software results were compared to visual assessment from MQSA-qualified radiologists."
• "DenSeeMammo v1.2 software was run over substantial data sets... which had been previously assessed by MQSA-qualified radiologists."

This indicates that the algorithm's output (BI-RADS breast density category) was compared directly to the expert-determined ground truth, representing a standalone performance evaluation.

7. The type of ground truth used:

The ground truth used for the test set was expert consensus / visual assessment by MQSA-qualified radiologists using ACR BI-RADS V recommendations. This is explicitly stated: "Comparison to qualified databases containing images previously visually assessed by MQSA-qualified radiologists using ACR BI-RADS V recommendations."

8. The sample size for the training set:

The document does not provide any information regarding the sample size for the training set. It implicitly refers to "qualified databases" that the algorithm uses for "comparison," but it does not specify if these databases also served as training data, nor their size.

9. How the ground truth for the training set was established:

The document does not provide details on how the ground truth for any potential training set was established. It mentions that the software's algorithms "allow comparison to qualified databases containing images previously visually assessed by radiologists." This suggests a similar process to the test set ground truth (visual assessment by radiologists), but specific methodology for a training set is not described.

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