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This modification expands the intended use statement for the HP SONOS 100CF Ultrasound Imaging System to include obstetrics and gynecology applications.

This 510(k) submission is to add an endovaginal transducer and a new EV(EndoVaginal)/Pelvic study type to the HP SONOS 100CF Ultrasound Imaging System.

This document is a 510(k) summary for a modification to an ultrasound imaging system. It focuses on demonstrating substantial equivalence to predicate devices for regulatory approval, rather than providing a detailed study that proves the device meets specific acceptance criteria in the manner typically seen for novel AI/software devices. Therefore, much of the requested information cannot be found in this summary.

Here's a breakdown of what can and cannot be extracted:

• Acceptance Criteria and Reported Device Performance: This document does not present specific quantitative acceptance criteria or reported device performance in the form of a table for clinical effectiveness. The acceptance is based on compliance to general safety standards and substantial equivalence to predicate devices.
• Sample size used for the test set and the data provenance: Not applicable. There is no mention of a clinical test set with specific data provenance for performance evaluation.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for clinical performance evaluation is not discussed.
• Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.
• 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: Not applicable. This summary predates common AI integration in medical devices and does not describe such a study. The device is an ultrasound imaging system, not an AI-powered diagnostic tool.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an AI algorithm.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable for clinical performance. The "ground truth" here is compliance with safety standards and functional equivalence to predicate devices.
• The sample size for the training set: Not applicable. This is not a machine learning device that requires a training set.
• How the ground truth for the training set was established: Not applicable.

Based on the provided 510(k) summary, here's what can be stated:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicit and revolve around:

2. Sample size used for the test set and the data provenance: Not applicable as this submission relies on compliance to standards, predicate device comparison, and internal validation, not a clinical performance test set with retrospective/prospective data or specific data provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.

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, an MRMC comparative effectiveness study was not done. The device is an ultrasound imaging system, and this submission predates the widespread use of AI in medical imaging.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This device is an imaging system, not a standalone algorithm.

7. The type of ground truth used:
* Safety: Compliance with established medical device safety standards (IEC 601, UL 544).
* Functionality/Effectiveness: Verification that performance specifications are met through software validation, and comparison to the known performance of predicate devices.
* Acoustic Output: Measured acoustic output data.

8. The sample size for the training set: Not applicable. This is not a machine learning device that requires a training set.

9. How the ground truth for the training set was established: Not applicable.

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