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The Singh Colpotomizer System is indicated for use by a surgeon in laparoscopic procedures where uterine manipulation and visualization of the position of the vaginal fornices for colpotomy incisions is required. The surgeon makes the colpotomy incisions to access or remove intraperitoneal tissue.

The Singh Colpotomizer System is comprised of a reusable uterine manipulator with cervical screw attachment, a reusable sliding uterine tip (inner rod), reusable sliding and rotating funnels with a lip acting as a colpotomizer, an O ring and screw to hold the funnel in place, and a vaginal plug. The funnels are available in two sizes: 35 and 40mm.

In Laparoscopic Hysterectomy, the Uterine Cannula is inserted into the Uterus. The Vaginal Funnel slides onto the Uterine Cannula and is rotated manually during the operation to lift the Vaginal Wall for identification and incision during Laparoscopic Hysterectomy. This identifies the uterine arteries and ureters during this procedure.

Stainless steel and medical grade plastics are used in the manufacture of the subject device.

The provided text describes a submission for a medical device called the "Singh Colpotomizer System" for 510(k) clearance, asserting substantial equivalence to the previously cleared "Koh Colpotomizer System." The document focuses on non-clinical and clinical data to support this claim, rather than defining explicit acceptance criteria and then proving the device meets those criteria with a study.

Instead, the submission presents data from the device's clinical use and then concludes that these data, along with non-clinical testing, show the device does not raise new questions of safety and effectiveness compared to the predicate device. Therefore, the "acceptance criteria" can be inferred from the adverse events and successful outcomes reported.

Here's an attempt to structure the information based on your request, acknowledging that explicit "acceptance criteria" tables are not directly provided in the text in the way you've requested for AI device evaluations.

Inferred Acceptance Criteria and Reported Device Performance

Given the nature of this 510(k) submission, the "acceptance criteria" are not explicitly stated as numerical thresholds for device performance like sensitivity or specificity. Instead, they are implicitly tied to maintaining safety and effectiveness comparable to the predicate device, with a focus on acceptable rates of complications and successful procedure completion.

Note: The "acceptance criteria" above are inferred from the adverse events and successful outcomes reported to demonstrate that the device is safe and effective and does not raise new questions compared to its predicate. Specific numerical thresholds for "low incidence" are not defined in the provided text.

Study Information

Due to the nature of this being a 510(k) submission for a surgical instrument rather than an AI/software device, many of the requested categories (like sample size for test set, ground truth experts, MRMC studies, standalone performance, training set details) are not directly applicable or explicitly detailed in the provided text. However, I will answer what can be inferred or directly stated.

1. Sample size used for the test set and the data provenance:

   • Sample Size: 2148 cases.
   • Data Provenance: Clinical performance data from the clinical use of the device in Western Australia since 2002. This is retrospective data from real-world use.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • This information is not provided as the study is a retrospective review of clinical use. The "ground truth" for reported adverse events and successful completion would have been established by the operating surgeons and medical records, rather than a panel of independent experts reviewing cases for a specific "ground truth" determination in the context of an AI device.

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

   • This is not applicable and not mentioned. The data are from routine clinical use and records, not an adjudicated test set in the context of an AI algorithm.

4. 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, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted as this device is a surgical instrument, not an AI or imaging diagnostic tool.

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

   • No, this is not applicable for a surgical instrument. The device is used by a surgeon who is always "in-the-loop."

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

   • The "ground truth" for the reported outcomes (adverse events, successful procedure completion, pneumoperitoneum maintenance) is based on outcomes data from routine clinical practice and medical records, as recorded by the operating surgeons and healthcare facilities.

7. The sample size for the training set:

   • This concept is not applicable. The device is a physical surgical instrument; there is no "training set" in the context of machine learning. The clinical performance data (2148 cases) represents real-world usage data.

8. How the ground truth for the training set was established:

   • Not applicable, as there is no "training set" in the machine learning sense for this device.

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