(60 days)
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.
This submission is proposing a software update to SenoBright HD consisting of an improvement of the existing "recombination" algorithm with the New Image Recombination Algorithm (NIRA) by adding a local estimation of breast thickness in the images recombination to account for the non-uniformity of the breast thickness, and by compensating for potential patient movement between the 2 CESM acquisitions (Low Energy and High Energy).
SenoBright HD (K172404) is the name of Senographe Pristina FFDM system allowing to perform Contrast Enhanced Spectral Mammography (CESM) application.
The CESM acquisition technique consists in acquiring two images (one High Energy and one Low Energy) in sequence and under the same breast compression after patient injection with an iodinated contrast media. The two images are then recombined through a post-processing algorithm.
This design change is a software and labeling only option, compatible with SenoBright HD installed base and does not require any hardware modification on the Senographe Pristina platform.
Here's a breakdown of the acceptance criteria and study details for the SenoBright HD device with the New Image Recombination Algorithm (NIRA), based on the provided document:
1. Table of Acceptance Criteria & Reported Device Performance
The acceptance criteria are not explicitly stated in a quantitative manner (e.g., "must achieve X% sensitivity"). Instead, the document focuses on demonstrating non-inferiority or improvement compared to the predicate device (SenoBright HD K172404) and showing clinical acceptability. The performance is reported in terms of assessments made by radiologists.
| Acceptance Criterion (Implicit) | Reported Device Performance (SenoBright HD with NIRA) |
|---|---|
| Equivalence/Improvement in Contrast Uptake Visibility | Assessed equivalent or better in 97% of the cases. |
| Reduction/Equivalence in Artifacts Visibility | Assessed equivalent or lower in 99% of the cases. |
| Improvement in Overall Clinical Image Quality | Assessed superior in more than 98% of the cases. |
| Clinical Image Acceptability (for NIRA images) | Illustrated through objective criteria defined and evaluated by radiologists. (Specific quantitative results for this are not provided, only that it was "illustrated"). |
| Non-clinical Performance (Phantoms) | Demonstrated that SenoBright HD with NIRA performs at least as well as the cleared device (K172404) and brings Image Quality improvements. Demonstrated reduction of artifacts in case of patient motion or breast thickness non-uniformity to increased lesion visibility. (Specific quantitative results for phantom studies are not provided in this summary). |
| Safety and Effectiveness | Demonstrated through full verification testing, additional performance testing, and clinical image evaluations. Concluded that NIRA for SenoBright HD is substantially equivalent to the predicate device and raises no new questions of safety and effectiveness. |
2. Sample Size and Data Provenance
- Test Set Sample Size:
- Clinical Image Evaluation (Initial Acceptability): 10 images
- Clinical Image Evaluation (Comparative): 50 clinical images
- Data Provenance: The document does not explicitly state the country of origin for the clinical data used in the studies. Given GE Healthcare's presence in France (as per the submitter's address), it is plausible the data could originate from there or other international sites. The data is described as "clinical images," implying retrospective data from a clinical setting. It is not stated whether it was prospective or retrospective, but the description "clinical images" often implies retrospective collection for such comparative studies.
3. Number of Experts and Qualifications
- Number of Experts: 3 independent MQSA-qualified radiologists used for both clinical image evaluations.
- Qualifications: "MQSA-qualified radiologists." MQSA (Mammography Quality Standards Act) qualification indicates that these radiologists meet specific federal standards for interpreting mammograms in the United States, including training, experience, and continuing education requirements. The specific years of experience for each expert are not provided.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method (like 2+1, 3+1). It states that the evaluations were "performed by 3 independent MQSA-qualified radiologists." This suggests that each radiologist independently assessed the images, and the reported percentages (97%, 99%, 98%) likely represent the proportion of cases where at least two out of three (or possibly all three) agreed on the assessment, or an aggregatetion of individual assessments, but the specific consensus/adjudication rule is not detailed.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study and Effect Size
- A comparative effectiveness study was performed as a form of reader study, but it wasn't a "human readers improve with AI vs without AI assistance" MRMC study in the sense of AI assisting the human reader.
- Instead, this study compared the quality of images generated by the new algorithm (NIRA) against the quality of images generated by the previous algorithm (Predicate SenoBright HD), both assessed by human readers.
- Therefore, there's no direct "effect size of how much human readers improve with AI vs without AI assistance" as the AI (NIRA) is the image generation method being evaluated, not an assistance tool for the human reader's diagnostic performance. The human readers are evaluating the output of the AI.
6. Standalone Performance (Algorithm Only)
The document primarily focuses on the output quality of the algorithm as perceived by human readers, rather than a quantifiable "standalone" diagnostic performance (e.g., sensitivity/specificity for detecting lesions). The phantom testing is a form of standalone performance evaluation for image quality metrics, but not for diagnostic accuracy in a clinical context.
7. Type of Ground Truth Used
The ground truth for the clinical image evaluations was effectively the expert consensus/assessment of the image quality metrics (contrast uptake visibility, artifact visibility, overall image quality) by the 3 MQSA-qualified radiologists. There is no mention of pathology or outcomes data being used as the clinical ground truth for lesion presence/absence for diagnostic performance evaluation, as the study was focused on image quality assessment.
8. Sample Size for the Training Set
The document does not provide any information regarding the sample size used for training the New Image Recombination Algorithm (NIRA). This is typical for premarket notifications where the focus is on verification and validation of changes rather than the internal development details of the algorithm itself.
9. How Ground Truth for Training Set was Established
The document does not provide any information on how the ground truth for the training set (if any was used for supervised learning of NIRA) was established. As NIRA is described as an "evolution" of an existing recombination algorithm, it might involve engineering improvements rather than a machine learning model that requires a labeled training set in the typical sense. It states NIRA accounts for "non-uniformity of the breast thickness" and compensates for "potential patient movement," suggesting algorithmic improvements based on physical principles and image characteristics rather than purely data-driven supervised learning.
§ 892.1715 Full-field digital mammography system.
(a)
Identification. A full-field digital mammography system is a device intended to produce planar digital x-ray images of the entire breast. This generic type of device may include digital mammography acquisition software, full-field digital image receptor, acquisition workstation, automatic exposure control, image processing and reconstruction programs, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II (special controls). The special control for the device is FDA's guidance document entitled “Class II Special Controls Guidance Document: Full-Field Digital Mammography System.”See § 892.1(e) for the availability of this guidance document.