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The Margolin HSG Cannula is for the delivery of contrast media or saline into the uterine cavity during Hysterosalpingography (HSG) or Sonohysterography (SHG) for examination of the uterus and fallopian tubes. When used for HSG, the Margolin HSG Cannula can be used for evaluation of tubal patency.

The Goldstein Sonohysterography Catheter is used to access the uterine cavity for the delivery of saline for Sonohysterography (SHG).

The Margolin HSG Cannula and Goldstein Sonohysterography Catheter are single lumen uterine catheters that facilitate access to the uterus to deliver fluid (contrast media or saline) during diagnostic procedures. Both feature a silicone "acom" wedge that seals the cervical canal and maintains the position of the catheter during the procedure.

This is a 510(k) summary for medical devices (Margolin HSG Cannula and Goldstein Sonohysterography Catheter) and, as such, focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed clinical study for novel performance metrics with acceptance criteria, expert ground truth, or statistical power.

Therefore, many of the requested data points (e.g., effect size of human readers with/without AI, sample size for training set, specific study design for clinical performance against pre-defined acceptance criteria, detailed ground truth establishment for a test set) are not presented in this document because they are not typically required for a 510(k) submission for these types of devices. The "performance data" section rather lists engineering and biocompatibility tests.

However, based on the provided text, I can extract the following information:

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

The document states: "All predetermined acceptance specifications were met in the following tests." However, the specific numerical acceptance criteria for each test are not explicitly detailed in this summary. The performance is reported as meeting these unspecified specifications.

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

The document does not specify the sample sizes for the "performance data" tests. The data provenance is not mentioned, as these are primarily engineering and bench tests, not clinical studies in a specific geographical location. The studies are prospective in the sense that they are conducted on manufactured devices to test design specifications.

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)

Not applicable. These are engineering performance tests, not clinical performance studies requiring expert ground truth for interpretation (e.g., image analysis, diagnosis).

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

Not applicable. This concept applies to clinical studies where multiple reviewers' opinions need to be reconciled to establish a ground truth, which is not the case for these bench tests.

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

Not applicable. These are medical devices (cannulas and catheters) used for fluid delivery, not AI-powered diagnostic or assistive tools. Therefore, an MRMC study related to human readers improving with AI assistance is entirely irrelevant to this document.

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

Not applicable. As stated above, these are physical medical devices, not algorithms.

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

For the performance tests listed, the "ground truth" is defined by the technical specifications and standards (e.g., ISO 11135-1:2007 for sterilization, ASTM D4169-16 for transportation, ASTM F2096-11 for bubble leak, ISO 10993 series for biocompatibility, and "Cook's predetermined acceptance specifications" for dimensional, patency, tensile, and force tests). There's no clinical ground truth established by experts or pathology for these types of tests.

8. The sample size for the training set

Not applicable. There is no AI or machine learning model being "trained" for these medical devices.

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

Not applicable for the same reason as point 8.

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