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Genius AI Detection is a computer-aided detection and diagnosis (CADe/CADx) software device intended to be used with compatible digital breast tomosynthesis (DBT) systems to identify and mark regions of interest including soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in DBT exams from compatible DBT systems and provide confidence scores that offer assessment for Certainty of Findings and a Case Score. The device intends to aid in the interpretation of digital breast tomosynthesis exams in a concurrent fashion, where the interpreting physician confirms or dismisses the findings during the reading of the exam.

Genius AI Detection is a software device intended to identify potential abnormalities in breast tomosynthesis images. Genius Al Detection analyzes each standard mammographic view in a digital breast tomosynthesis examination using deep learning networks. For each detected lesion, Genius AI Detection produces CAD results that include the location of the lesion, an outline of the lesion and a confidence score for that lesion. Genius Al Detection also produces a case score for the entire tomosynthesis exam.

Genius Al Detection packages all CAD findings derived from the corresponding analysis of a tomosynthesis exam into a DICOM Mammography CAD SR object and distributes it for display on DICOM compliant review workstations. The interpreting physician will have access to the CAD findings concurrently to the reading of the tomosynthesis exam. In addition, a combination of peripheral information such as number of marks and case scores may be used on the review workstation to enhance the interpreting physician's workflow by offering a better organization of the patient worklist.

The Genius Al Detection 2.0 now added the CC-MLO Correlation feature. The added feature provides the ability to correlate a suspected lesion in one view with a like finding in the other view and additionally provides a workflow and navigation feature for the interpreting physician.

The provided text describes the regulatory clearance of a medical device, "Genius AI Detection 2.0 with CC-MLO Correlation." While it mentions "acceptance criteria" through the lens of safety and effectiveness, it does not explicitly list quantitative acceptance criteria for the device's performance (e.g., a specific sensitivity or specificity threshold). Instead, it describes internal validation and a standalone evaluation study to demonstrate that the device is "safe and effective."

Here's a breakdown of the requested information based on the provided text:

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

As mentioned, explicit quantitative acceptance criteria are not provided in the document. The document states that the "verification testing showed that the software application satisfied the software requirements." For the standalone evaluation of the CC-MLO Correlation feature, the performance was "estimated in both groups by scoring the detection pairs against the truth pairs and by evaluating the expert radiologist's response, respectively." However, specific performance metrics (e.g., accuracy percentages, sensitivity, specificity for the CC-MLO correlation) are not reported in this summary.

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:
  • For the standalone evaluation of the CC-MLO Correlation feature, the dataset included:
    • 106 biopsy-proven malignant cases.
    • 561 screening negative cases.
    • Additionally, the detection pairs generated by the CC-MLO correlation feature were reviewed on 658 screening negative and biopsied benign cases. (It's unclear if this "658 cases" is a subset or superset of the "561 screening negative cases" mentioned earlier, or an entirely separate review of negative/benign cases for correlation specifically.)
• Data Provenance: The document does not specify the country of origin of the data or whether it was 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: An "expert radiologist" is mentioned in the singular ("an expert radiologist" and "the expert radiologist's response"). This suggests that one expert radiologist was primarily responsible for establishing ground truth for the malignant cases and reviewing detection pairs.
• Qualifications of Experts: The document specifies "expert radiologist" but does not provide details on their specific qualifications, such as years of experience.

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

• The text describes ground truth for malignant cases being established by "an expert radiologist by generating ground truth marks and truth pairs." For the CC-MLO correlation feature, generated detection pairs were "reviewed by an expert radiologist."
• This suggests a single-reader ground truth establishment and review without an explicit multi-reader adjudication method (like 2+1 or 3+1). It seems to be "none" in terms of multi-reader consensus for the test set ground truth.

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 states, "Standalone evaluation testing was also conducted." It focuses on the performance of the algorithm itself and its ability to correlate findings.
• There is no mention of an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. Therefore, no effect size for human reader improvement is provided. The device "intends to aid in the interpretation... in a concurrent fashion, where the interpreting physician confirms or dismisses the findings," implying human-in-the-loop, but no study of this combined performance is detailed here.

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

• Yes, a standalone evaluation was done. The document explicitly states: "Standalone evaluation testing was also conducted." The performance of the CC-MLO Correlation feature was "estimated... by scoring the detection pairs against the truth pairs."

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

• For the malignant cases (106 cases): Ground truth was established by "an expert radiologist by generating ground truth marks and truth pairs." It also mentions these were "biopsy proven malignant cases," indicating pathology was also part of the ground truth for these malignant cases. The "truth pairs" were essentially expert annotations of pathologically confirmed lesions on both orthogonal views.
• For the screening negative cases (561 and 658 cases reviewed): The ground truth was presumably based on their screening negativity, validated by clinical follow-up or expert review. The review of detection pairs was against "the expert radiologist's response," implying expert judgment as ground truth for these negative/benign cases.

8. The sample size for the training set

• The sample size for the training set is not provided in this document. The description focuses solely on the "standalone evaluation of the CC-MLO Correlation feature" which used a specific test dataset.

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

• As the training set sample size is not provided, how its ground truth was established is also not detailed in this document. It only mentions the use of "deep learning networks" which implies a trained model, but the specifics of its training data and ground truth establishment are absent from this summary.

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