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LaparoGuard is indicated for use with compatible laparoscopic devices. LaparoGuard can be used in general laparoscopy wherever a laparoscope is indicated for use.

LaparoGuard allows surgeons to virtually annotate a safe anatomical volume ("safe zone") inside the body cavity of a patient during a laparoscopic surgery. A compatible laparoscope, such as the ConMed IM3300 (K031098), captures video feed that is processed by LaparoGuard, which tracks surgical instruments and the safe zone boundaries to display a real-time overlay on a compatible laparoscope monitor, such as the Stryker Vision Elect 21" (K081995). Surgical teams receive visual and auditory notifications throughout the procedure whenever a tracked instrument has exited the safe zone.

The provided text describes the LaparoGuard device and its substantial equivalence to a predicate device, focusing on non-clinical testing and software validation. However, it does not include detailed information regarding clinical study results, acceptance criteria for performance metrics (like sensitivity, specificity, accuracy), or specifics about a test set and its ground truth establishment involving experts.

Therefore, I cannot populate the table or answer questions 2-9 from the provided input, as that information is not present.

Here's what can be extracted based on the provided text:

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

The document mentions non-clinical testing, but does not provide specific acceptance criteria or reported performance values for metrics typically found in clinical studies (e.g., accuracy, sensitivity, specificity, etc. related to its primary function of "safe zone" monitoring). It does state adherence to standards for accuracy, but not the specific thresholds or measured performance of the device against those thresholds.

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. The document mentions "system integration testing using anatomical phantoms" but does not detail a clinical test set with human data.

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 provided.

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, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

There is no mention of an MRMC study or AI assistance to human readers. The device is purely a monitoring system providing visual and auditory notifications.

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

The device itself is a "standalone" system in its monitoring function, providing alerts to the surgeon. The "system accuracy using an accuracy measurement phantom" and "system integration testing using anatomical phantoms" could be considered standalone performance tests in a controlled environment, but detailed results and specific metrics are not given.

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

For the non-clinical testing mentioned (accuracy measurement phantom, anatomical phantoms), the ground truth would be based on the known, precisely defined physical properties of these phantoms. No mention of expert consensus, pathology, or outcomes data is made for establishing ground truth for any performance claims.

8. The sample size for the training set

This information is not provided. The document focuses on verification and validation of the software and system, not on a machine learning model's training set.

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

This information is not provided.

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