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The SILS™ Port is indicated for multiple instrument or camera access to the abdominal cavity through a single incision for performing minimally invasive laparoscopic procedures. The SILS™ Port provides the ability to use three conventional laparoscopic trocars and an insufflation port through a single incision while providing the ability to maintain pneumoperitoneum.

Three laparoscopic trocars and an insufflation tube bound by a flexible port.

This document describes a 510(k) premarket notification for the Covidien SILS™ Port, a surgical trocar. The information provided is primarily for regulatory clearance and does not contain the detailed study results typically associated with establishing specific performance criteria for AI/ML devices. Therefore, I cannot fully complete all sections of your request based solely on the provided text.

Specifically, this submission is for a medical device (surgical port/trocar), not an AI/ML diagnostic or assistive device. The "performance data" section in the summary mentions "In-vitro and in-vivo testing has been performed in support of the intended use of this device," but it does not specify acceptance criteria, effect sizes, reader studies, ground truth, or sample sizes in the way one would for an AI/ML product.

However, I will extract and infer what I can from the provided text to address your points as best as possible.

Acceptance Criteria and Study for Covidien SILS™ Port (Surgical Trocar):

Based on the provided 510(k) summary, detailed acceptance criteria and a study design for AI/ML performance metrics are not applicable or provided, as this is a physical medical device (surgical port/trocar). The "acceptance" is primarily based on substantial equivalence to predicate devices and meeting safety and material biocompatibility standards.

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

Since this is a physical medical device and not an AI/ML algorithm, the concept of a "device performance" table as you might expect for an AI/ML product (e.g., sensitivity, specificity) is not present. Instead, performance relates to its functional use and safety. The document states:

For the remainder of your questions, the information is not present in the provided 510(k) summary as these questions are typically relevant to AI/ML device studies, not traditional medical device clearance.

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

• Not Applicable / Not Provided: This information is not typically part of a 510(k) summary for a physical medical device in the context of an AI/ML algorithm. The document mentions "in-vitro and in-vivo testing" but does not detail the sample sizes or provenance for these tests in this summary.

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 / Not Provided: As this device is not an AI/ML algorithm requiring expert interpretation of outputs, this information is not relevant or included in the 510(k) summary.

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

• Not Applicable / Not Provided: This concept is not relevant to the clearance of this physical medical device.

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 / Not Provided: This type of study is specifically for AI/ML diagnostic or assistive devices and is not relevant to the SILS™ Port.

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

• Not Applicable / Not Provided: The SILS™ Port is a physical surgical tool, not an algorithm.

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

• Not Applicable / Not Provided (in the context of AI/ML): For a physical device, "ground truth" might relate to physical tests (e.g., material strength tests, leakage tests), but this is not specified in the summary nor is it the "ground truth" relevant to AI/ML performance.

8. The sample size for the training set

• Not Applicable / Not Provided: There is no AI/ML algorithm to train for this device.

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

• Not Applicable / Not Provided: There is no AI/ML algorithm to train for this device.

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