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The AMR Mini-Laparoscope is indicated for visualization and accessing the abdomen during laparoscopic procedures such as (but not limited to):

• Unexplained pelvic pain .
• Infertility Work-up .
• . Tubal sterilization
• . Diagnosis and/or treatment of ectopic pregnancy
• . Evaluation, diagnosis and/or treatment of pelvic tumors, including myomata (less than 16 weeks gestational size.)
• Evaluation of congenital anomalies of the pelvic organs .
• . Retrieval of foreign bodies
• Determination of the presence and extent of pelvic endometriosis .
• Determination of the presence and extent of pelvic inflammatory disease (if not in . acute stage)
• Access to abdomen for surgical procedures such as LAVH ●
• Visualization, diagnosis and/or treatment of perforate abdominal (pelvic) organs. .

The AMR Mini-laparoscope is designed and indicated for visualization of body cavities during laparoscopic procedures. The device features a stainless steel body containing light fibers encased in epoxy, an aluminum eyepiece, a light post for purposes of connecting a light source and a means of connecting to a video monitor.

This document describes a 510(k) premarket notification for the Applied Medical Mini-Laparoscope, and as such, it does not contain the detailed study information typically found for AI/ML-driven devices. The submission relates to a medical device for visualization during laparoscopic procedures, a physical instrument, not a software algorithm.

Therefore, most of the requested information regarding acceptance criteria and studies (especially those related to AI performance, ground truth, expert adjudication, and comparative effectiveness) is not applicable to this type of traditional medical device submission.

Here's a breakdown of what can be extracted and what is not applicable:

1. Table of acceptance criteria and the reported device performance

The document mentions that mechanical and biocompatibility tests were performed. However, it does not provide specific acceptance criteria or quantitative performance results in a table format.

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

• Not applicable. This pertains to studies of algorithms, not physical devices undergoing mechanical and biocompatibility testing. The submission does not detail specific "test sets" in the context of data for an algorithm.

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. This is relevant for AI/ML performance evaluation, not for the physical and biological testing of a conventional medical device like a laparoscope.

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

• Not applicable. As above, this is for AI/ML ground truth establishment.

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. This study type is for AI/ML-assisted diagnosis/interpretation, which is not what this device is. The device is a direct visualization tool.

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

• Not applicable. There is no algorithm being evaluated in a standalone capacity.

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

• Not applicable. The "ground truth" for a physical device's performance is typically established through engineering standards, test methods, and potentially animal or human cadaver studies for efficacy, rather than expert consensus on interpretive data.

8. The sample size for the training set

• Not applicable. There is no algorithm requiring a "training set."

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

• Not applicable. There is no algorithm requiring a "training set."

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