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The Intuitive Surgical EndoWrist Stapler 45. Stapler 45 Reloads and other Stapler Accessories (including the bladeless obturators) are intended to be used with the da Vinci Surgical System (Model IS4000) for resection and/or creation of anastomoses in General, Thoracic, Gynecologic, and Urologic surgery. The device can be used with staple line or tissue buttressing material (natural or synthetic).

The subject 12 mm & Stapler Bladeless Obturator (Bladeless Obturator) is used with the 12 mm & Stapler Cannula (Cannula) and 12 mm & Stapler Cannula Seal (Cannula Seal) to facilitate placement of the Cannula in the body wall. The Bladeless Obturator consists of three components - a shaft, a handle, and two latches that allow it to latch onto the Cannula Seal. The handle is made of Radel R-550 while the shaft and the two latches are made of stainless steel. The subject device is a reusable device and is offered in two lengths (standard and long) to match the Cannula lengths and meet users' needs.

The document provided is a 510(k) summary for the Intuitive Surgical 12 mm & Stapler Bladeless Obturators. It describes the device, its intended use, and the performance data used to demonstrate substantial equivalence to a predicate device. However, this document does not contain the level of detail requested in your prompt regarding acceptance criteria, specific study results, sample sizes for test and training sets, expert qualifications, or detailed ground truth methodologies typically found in AI/ML device submissions.

Here's what I can extract and confirm, and what cannot be provided based on the given text:

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

The document states that "Design verification and design validation testing were conducted on the subject device to confirm that the design outputs meet design input requirements and that the device is safe and effective for its intended use." However, specific quantitative acceptance criteria and corresponding reported device performance values are not provided in this summary. Instead, it lists the types of tests performed:

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

The document mentions "tests with an animal model were performed" for design validation. However, the sample size for this animal model study is not provided. Data provenance (country of origin, retrospective/prospective) is also not specified.

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 type of information is typically relevant for studies involving human interpretation (e.g., medical imaging assessment). Since this device is a surgical accessory (obturator), and the performance testing described involves mechanical and physical attributes, as well as an animal model, the concept of "experts establishing ground truth" in the way it's used for AI/ML performance evaluation (e.g., radiologists reviewing images) does not directly apply here. The "ground truth" for these types of tests would be established by the physical and functional specifications of the device.

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

Not applicable in the context of the device and testing described. Adjudication methods are typically used in studies where there's variability in human expert assessment (e.g., multiple radiologists disagreeing on a diagnosis), which is not the nature of the tests for this surgical accessory.

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

This is an AI/ML specific study design. The document describes a traditional medical device (surgical obturator) and its performance validation, not an AI/ML algorithm. Therefore, an MRMC study and AI-assisted human reader improvement are not applicable and not mentioned.

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

This is an AI/ML specific concept. The device is a physical surgical tool; it is not an algorithm, so standalone algorithm performance is not relevant.

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

As explained in point 3, the "ground truth" for this type of device's performance would be derived from:

• Mechanical specifications: e.g., precise dimensions, force tolerances for axial load bearing.
• Functional requirements: e.g., maintaining insufflation, not catching on tissue.
• Animal observation: For the animal model study, the "ground truth" would be the direct observation of the obturator's interaction with tissue and its latching mechanism working as intended (or not).

8. The sample size for the training set

This is an AI/ML specific concept. The device is a physical surgical tool; it is not an AI/ML algorithm, so there is no training set.

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

Not applicable, as there is no training set for this device.

In summary: The provided document is a 510(k) summary for a traditional medical device (a surgical obturator). It outlines performance testing (design verification and validation) to demonstrate substantial equivalence to a predicate device. However, it does not contain the detailed statistical and methodological information (e.g., specific acceptance criteria values, sample sizes for test sets, expert qualifications, AI/ML-specific study designs like MRMC or standalone performance, and detailed ground truth establishment) that would be present in a submission for an AI/ML-driven device. The information provided is typical for a Class II non-AI device where engineering and animal model testing are the primary means of demonstrating safety and effectiveness.

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