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The HK-WQ-II are indicated for manipulation of the uterus, and injection of fluids during laparoscopic gynecologic procedures such as lapacervical hysterectomy, minilap tubal ligation, laparoscopic tubal occlusion or diagnostic laparoscopy.

The HK-ZQ-I and HK-ZQ-II are intended for use in laparoscopic procedures where it is desirable to delineate the vaginal fornices and the surgeon intends to remove or access intraperitonial tissue through the vagina by use of a colpotomy or culdotomy incision; such as laparoscopically assisted vaginal hysterectomies, total laparoscopic hysterectomies, while maintaining pneumoperitoneum by sealing the vagina while a colpotomy is performed.

There are four versions of the subject device. HK-WQ-I and HK-WO-II have a curved central guide rod, while the HK-ZQ-I and HK-ZQ-II have a central guide rod which can be rotated (or "flexed") up or down using a dial on the handle.

Each device is a sterile, disposable, single-patient use device which consists of a manipulator shaft with an inflatable intrauterine balloon at the proximal end. The intrauterine balloon is inflated by liquid via a standard syringe injected through the check valve. The internal injection tip of the manipulator shaft is open to allow direct intrauterine introduction of medicine liquid via the injector port. The device also incorporates a vaginal sealing balloon which is inflated by liquid via a standard syringe injected through the check valve to seal the vagina and maintain pneumoperitoneum. The location of the uterus can be adjusted by the handle.

Each device version can be used with a cervical cup. During use, the open-end of the cup rests on the cervix. There are two versions of the cup- a ceramic version and a polyphenylene sulfite version. For both materials, the cup is available in four different sizes - 30mm, 35mm, 40mm and 46mm.

This document is a 510(k) premarket notification for uterine manipulators (HK-WQ-I, HK-WQ-II, HK-ZQ-I, and HK-ZQ-II). As such, it primarily focuses on demonstrating substantial equivalence to predicate devices rather than proving a device meets specific acceptance criteria through a clinical study with detailed performance metrics.

Therefore, much of the requested information (like acceptance criteria, specific performance metrics, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth provenance) is not present in this type of regulatory submission, as these devices are not AI/ML-based.

However, I can extract the information that is available regarding non-clinical performance tests:

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

The document does not provide a table of acceptance criteria with specific numerical targets. Instead, it lists the types of non-clinical performance tests conducted to demonstrate that the subject devices perform similarly to their predicate devices and meet safety requirements. The "reported device performance" is implied to be successful, as the conclusion states that the devices are substantially equivalent.

Non-Clinical Performance Tests Conducted:

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

This information is not provided in the document. The tests are non-clinical (laboratory/benchtop) and do not involve human or patient data in the sense of a clinical study.

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)

This is not applicable as the studies are non-clinical performance and safety tests, not studies requiring expert interpretation or ground truth establishment in a diagnostic context.

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

Not applicable for non-clinical performance 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. This device is a manual surgical instrument, not an AI/ML-based diagnostic or assistive technology.

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

Not applicable. This device is a manual surgical instrument, not an algorithm.

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

Not applicable. For non-clinical tests, "ground truth" would be the measured physical properties against a standard or specification.

8. The sample size for the training set

Not applicable. There is no training set as this is not an AI/ML device.

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

Not applicable. There is no training set.

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The device is indicated for use as a receptacle for the collection of tissue, organs and calculi during general and laparoscopic procedures.

The proposed device, Specimen Retrieval Bag, is comprised of retracted bag, opening support, outer introducer shaft, outer introducer handle, inner introducer handle, string and pull loop. The device is available in a series of models. The difference between each model is the retracted bag size and volume. The device is provided sterile and single use.

The provided text describes a 510(k) premarket notification for a Specimen Retrieval Bag. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study proving the device meets specific performance acceptance criteria for a novel medical device.

Therefore, many of the requested categories for a study proving acceptance criteria cannot be extracted because such a study was not presented in this document.

Here's what can be extracted and what cannot:

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

The document provides a comparison of the proposed device against a predicate device, focusing on technological characteristics and compliance with general standards. It does not explicitly state "acceptance criteria" for a study to prove performance but rather lists the tests performed to demonstrate substantial equivalence to the predicate.

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

The document does not provide details on the sample sizes used for the non-clinical tests or the data provenance. It only states that "performance tests were performed on both proposed device and predicate device."

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)

This information is not applicable and not provided. The study is a non-clinical, bench-testing evaluation designed to demonstrate substantial equivalence, not a clinical study involving human readers or expert ground truth for interpretation.

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

This information is not applicable and not provided, as there was no clinical study or expert adjudication described.

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 MRMC or clinical study was done. The document explicitly states: "No clinical study is included in this submission."

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

This concept is not applicable as the device is a physical specimen retrieval bag, not an AI algorithm. The tests performed are non-clinical, bench tests.

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

For the non-clinical performance tests, the "ground truth" would be the established scientific and engineering principles and the performance of the predicate device, as well as compliance with recognized standards (e.g., ISO, ASTM). It's not a clinical "ground truth."

8. The sample size for the training set

This information is not applicable as there is no mention of a "training set." The device is a physical medical device, not an AI or machine learning model that requires training data.

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

Not applicable as there is no training set mentioned.

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