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The FemRx™ Focused Monopolar (FMP) offersTAR™ System is intended for gynecologic hysteroscopic / electro-surgical use by trained professional gynecologists in hospital or office environments. It is used to resect and / or ablate endometrial tissue.

INDICATIONS: Abnormal Uterine Bleeding, Submucous Fibroids, Endometrial Polyps

The FemRx Focused Monopolar (FMP) @ERASTAR™ System [with Dispersive Electrode Option] is identical to FemRx's currently cleared FMP offRASTAR™ System with the exception that it includes the option of using a standard dispersive skin electrode. When used in this way it is basically identical to our original oreRASTAR™ System except that it incorporates a PEARL electrode (Physiologic endometrial ablation / resection loop). This loop is partially insulated to "focus" the current toward the tissue and avoid the reduction in effect associated with conventional devices. The use of isotonic solutions (e.g., NaCl or Ringers Lactate) is strongly recommended in order to reduce the risks associated with fluid overload.

The FMP offers TAR System differs from currently marketed bi-polar devices in that it continues to have a single active electrode (wire loop or Star loop) as opposed to two active electrodes. The return electrode (the metallic outer Morcellator housing of the device OR a standard dispersive pad located on the patients skin) has such a large surface area relative to the active electrode that there can be no tissue coagulating (much less cutting or ablating) effect at its surface.

This 510(k) summary (from 1997) for the FemRx Focused Monopolar (FMP) OPERASTAR™ System with Dispersive Electrode Option provides limited information regarding formal acceptance criteria or detailed study methodologies as would be expected from more recent submissions. Based on the provided text, here's what can be extracted and what remains unknown:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not specified for any study mentioned.
• Data Provenance:
  • "In vitro extirpated uteri studies" - suggests laboratory studies using excised human or animal uteri. No country of origin is mentioned.
  • "clinical testing" - prospective vs. retrospective is not specified. No country of origin is mentioned.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

• This information is not provided. The summary mentions "in vitro extirpated uteri studies" and "clinical testing" but does not detail how ground truth was established for these studies, nor the number or qualifications of experts involved.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

• This information is not provided.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

• No MRMC study is explicitly mentioned. The studies focus on device performance equivalence rather than human reader improvement with AI assistance (as this is a medical device, not an AI/imaging device).

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• This device is a surgical instrument, not an algorithm. Therefore, the concept of "standalone algorithm performance" is not applicable here. The performance described is that of the physical device itself.

7. The Type of Ground Truth Used

• For the "in vitro extirpated uteri studies," the ground truth likely involved direct observation, measurement, and histological analysis of tissue effects (resection depth, ablation depth, coagulation patterns).
• For the "clinical testing," the ground truth for "equivalent performance" would likely have been based on clinical outcomes, such as efficacy of endometrial ablation/resection, safety profiles, and possibly physician reports of ease of use or tissue manipulation.

8. The Sample Size for the Training Set

• This concept is not applicable as this is a physical medical device, not a machine learning algorithm.

9. How the Ground Truth for the Training Set Was Established

• This concept is not applicable as this is a physical medical device, not a machine learning algorithm.

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