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The Signial of intelligent loading units and Tri-Staple™ 2.0 intelligent cartridges have applications in abdominal, gyncologic, pediatric and thorneie surgery for resection and creation of anastomosis. They may be used for transection and resection of liver substance, hepatic vasculature and for transection and resection of panereas.

The Signia™ Intelligent Loading Units and Tri-Staple™ 2.0 Intelligent Cartridges place staggered rows of titanium staples and simultaneously divides the tissue so that three staggered rows of staples are placed on either side of the cut line. The size of the staples is determined by the selection of the following single use Tri-Staple™ 2.0 Intelligent Cartridges:

Tri-Staple™ 2.0 Intelligent Cartridge, Extra Thin/Vascular:

• · Cartridge color: Gray three rows of 2.0 mm titanium staples on either side of the cut line.
  Tri-Staple TM 2.0 Intelligent Cartridge, Vascular/Thin:
• · Cartridge color: White three rows of 2.5 mm titanium staples on either side of the cut line.
  The Tri-Staple™ 2.0 Intelligent Cartridge is available in 45 mm and 60 mm lengths. Tri-Staple™ 2.0 Intelligent Cartridges are able to be loaded into the Signia™ Intelligent Loading Unit of corresponding length by the User. Signia™ Intelligent Loading Units may be used up to twelve times in the same procedure. Signia™ intelligent Loading Units with Tri-Staple™ 2.0 Intelligent Cartridges, similar to the predicate devices, are compatible with Covidien's GIA™ Universal staplers, Endo GIA™ Universal staplers, Endo GIA™ Ultra Universal staplers, iDrive™ Ultra powered staplers with Endo GIA™ adapters and Signia™ staplers with Signia™ linear adapters.

In the recently cleared Signia™ Staplers and Signia™ Linear Adapters (K160176), the communication of the Tri-Staple™ 2.0 Cartridges and Signia™ Loading Units with the Signia™ Stapler was announced, leading to the addition of the "Intelligent" descriptor in the Trade/Proprietary Name of the devices noted above.

The provided text is a 510(k) summary for the Tri-Staple™ 2.0 Intelligent Cartridges. It focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing rather than clinical performance data. Therefore, the document does not contain the information requested regarding acceptance criteria for an AI/device, a study proving device meets acceptance criteria, sample sizes for test/training sets, data provenance, number/qualifications of experts, adjudication methods, MRMC studies, standalone performance, or grand truth establishment methods for an AI system.

The document describes the device, its intended use, technological characteristics, and a series of non-clinical tests performed to demonstrate safety and performance in comparison to a predicate device. These tests primarily evaluate mechanical properties, material biocompatibility, and electrical safety, not the performance of an AI or diagnostic algorithm.

Here's a breakdown of what is present in the document, and why it doesn't fit the requested format for an AI/diagnostic device's acceptance criteria and study:

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

• This document discusses "applicable technical design specifications and performance requirements" and "applicable medical device performance standards" but does not provide a specific table of acceptance criteria and reported performance for an AI component. The tests listed are for the mechanical and material aspects of a surgical stapler cartridge.

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

• The document mentions "bench tests using simulated tissue medium" and "in vivo and ex vivo tests using canine animal models." It does not provide specific sample sizes for these tests in the context of an "AI test set" or give details on data provenance (country of origin, retrospective/prospective). The tests are for the physical device itself.

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 as the submission is not for a diagnostic AI device requiring expert-established ground truth. The "ground truth" for the stapler relates to its mechanical function, hemostasis, and staple formation, assessed through engineering and animal studies.

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

• Not applicable; this relates to human expert review in diagnostic studies, which is not described here.

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 study was done, as this is not an AI diagnostic device. The submission explicitly states: "This premarket submission did not rely on the assessment of clinical performance data to demonstrate substantial equivalence."

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

• Not applicable; "standalone performance" typically refers to an AI algorithm's diagnostic accuracy. The "intelligent" descriptor for the cartridges refers to communication between the cartridge and the stapler, not an AI algorithm performing a diagnostic task.

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

• The "ground truth" for this device would be objective measurements from manufacturing specifications, bench testing (e.g., staple formation integrity, knife cutting completeness, firing force), and animal studies (e.g., visual assessment of hemostasis, burst pressure measurements). It's not a diagnostic ground truth like pathology or expert consensus.

8. The sample size for the training set:

• Not applicable. This device is a surgical stapler cartridge, not an AI model requiring a training set.

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

• Not applicable for the same reason as above.

In summary, the provided document is an FDA 510(k) summary for a surgical device (stapler cartridges) demonstrating substantial equivalence through non-clinical performance and technological characteristics, not an AI or diagnostic device requiring the types of studies and ground truth methodologies described in your prompt.

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