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SenoBright HD is an extension of the existing indication for diagnostic mammography with Senographe Pristina. The SenoBright HD application shall enable contrast enhanced breast imaging a dual energy technique. This imaging technique can be used as an adjunct following mammography and ultrasound exams to help localize a known or suspected lesion.

The subject of this submission is a modification of Senographe Pristina FFDM system (cleared in K162268) that will introduce a new imaging option called SenoBright HD. This imaging option has been previously cleared for Senographe Essential FFDM system in K103485,marketed as SenoBright Contrast Enhanced Spectral Mammography (CESM). The dual energy exposures will be done following an iodine based contrast injection of the patient and with a single breast compression. The new mode of operation for Senographe Pristina system is referred to as SenoBright HD Contrast Enhanced Spectral Mammography (CESM) due to the nature of taking an exposure with the x-ray spectrum optimized for general mammographic imaging and a second exposure with the x-ray spectrum optimized for the iodine based contrast image. The main modification of the Senographe Pristina system is the addition of software feature to control the low and high energy images and post processing and recombination of those images to create FFDM like image and recombined image. These two images allow the visualization of the breast tissue in a way that is typical and familiar for mammographic imaging and the x-ray contrast enhancement in the breast at the same time.

Here's an analysis of the provided text to extract information about the acceptance criteria and the study that proves the device meets them:

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

The document does not explicitly state quantitative acceptance criteria in a table format, nor does it provide specific reported device performance metrics against such criteria. Instead, it makes a general statement about "appropriate performance" and "good" image quality.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: The document only mentions a "Clinical image evaluation was performed" without specifying the number of images or cases in the test set.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of Experts: "expert radiologists" (plural) were used, but the exact number is not specified.
• Qualifications of Experts: Only "expert radiologists" is stated, without details on their specific experience (e.g., years of experience, board certifications).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not specify an adjudication method for the test set. It only mentions that an evaluation was performed by "expert radiologists."

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

A multi-reader multi-case (MRMC) comparative effectiveness study focusing on human readers improving with AI vs. without AI assistance was not done. The device description explicitly states: "The SenoBright HD application shall enable contrast enhanced breast imaging a dual energy technique. This imaging technique can be used as an adjunct following mammography and ultrasound exams to help localize a known or suspected lesion." This implies it is a diagnostic imaging tool, not an AI-assisted reader tool.

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

The device itself is an imaging system (Full-field digital mammography system with contrast enhancement), not an algorithm. Therefore, a "standalone algorithm only" performance study is not directly applicable in the way it would be for an AI-CAD device. The "Clinical image evaluation" mentioned was likely to assess the quality and utility of the images produced by the device, which would then be interpreted by human readers.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The document states "A Clinical image evaluation was performed showing that image quality of SenoBright HD in a clinical setting is good as assessed by expert radiologists." This implies that the ground truth for the image quality assessment was expert radiologist assessment/consensus. It does not mention pathology or outcomes data as ground truth for this specific evaluation, as the evaluation focused on image quality rather than diagnostic accuracy against a definitive truth for lesions.

8. The sample size for the training set

The document does not provide any information about a specific "training set" or its size. The device is a modification of an existing FFDM system, introducing new software features for image acquisition and processing. This suggests that the development likely involved engineering and image processing adjustments rather than deep learning model training in the conventional sense that would require a distinct "training set."

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

As no training set is mentioned for an AI model, this question is not applicable based on the provided text. The "ground truth" for the development of the image processing might have involved phantom studies and clinical images used for optimization, but these are not described as a formal "training set" with established ground truth in the context of machine learning.

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