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The Signia™ stapler, when used with the Signia™ circular adapters and Tri-Staple™ 2.0 circular single use reloads, has applications throughout the alimentary tract for the creation of end-to-side, and side-to-side anastomoses in both open and laparoscopic surgeries.

The Tri-Staple™ 2.0 black circular reloads place a circular triple staggered row of titanium staples. After staple formation, the knife blade resects the excess tissue, creating a circular anastomosis such as endto-end, end-to-side, or side-to-side anastomosis as the user sees fit. The new circular reloads will be offered for an extra thick tissue thick is identified by the black staple quide. The circular reloads deploy three height-progressive rows of 4.0 mm. 4.5 mm staples. The Tri-Staple™ technology incorporated in the black reload is essentially the same as the legally-marketed K192330 in terms of reload design. The Tri-Staple™ 2.0 black circular reloads are provided sterile for single use, and available in three lumen sizes: 28, 31, and 33 mm. The Tilt-Top™ anvil is available with all circular reloads.

The Tri-Staple™ 2.0 black reloads are for use with the previously-marketed Signia™ Circular Adapter. as part of the Signia™ stapler. The Signia™ stapler, when used with the Signia™ circular adapters and Tri-Staple™ 2.0 circular single use reloads, is a battery powered microprocessor controlled surgical stapler that provides push-button powered operations and firing of compatible reloads.

The provided text discusses the Tri-Staple™ 2.0 Black Circular Reloads (for use with Signia™ Circular Adapters). Based on the information present, here's a breakdown of the acceptance criteria and the study that proves the device meets them:

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

The document doesn't explicitly state "acceptance criteria" in a tabulated format with specific functional thresholds directly next to "reported device performance." Instead, it highlights various performance tests and evaluations conducted to demonstrate substantial equivalence to predicate devices, inferring that meeting these study outcomes signifies acceptable performance.

However, based on the "Summary of Studies" and the "Substantial Equivalence" table, we can infer some key performance aspects and their assessment:

• Bench top testing
• Ex-vivo testing
• In-vivo pre-clinical testing
• Chronic GLP study (healing metrics, anastomotic index) |
  | Shelf-Life/Stability | Device maintains intended performance over its specified shelf-life. | Stability/Shelf-Life study for single-use devices. |
  | Biocompatibility | Meets ISO 10993 series and FDA 2016/2020 guidance. | Biocompatibility evaluation conducted in accordance with FDA 2020 guidance and ISO 10993-1. |
  | Usability | Meets usability requirements as per FDA guidance and IEC 62366-1. | Usability study performed following FDA's 2016 guidance as well as IEC 62366-1. |
  | Software Functionality | Software performs as intended, verified and validated. | Software verification & validation activities completed following FDA's guidance documents and IEC 62304. |
  | Sterilization | Achieves minimum Sterility Assurance Level (SAL) of 10-6. | Ethylene oxide (EO) sterilization validation for single-use devices with a minimum SAL of 10-6. Previously demonstrated compliance remains unimpacted. |
  | Disinfection | Device can be effectively disinfected. | Disinfection validation performed per FDA 2015 reprocessing guidance. |
  | Reliability | Reusable components maintain performance over their extended life. | Reliability data supporting the extended end of life of the reusable devices. |
  | Electrical Safety & EMC | Meets relevant ANSI/AAMI ES 60601-1, IEC 60601-1, and IEC 60601-1-2 standards. | Electrical safety testing repeated per ANSI/AAMI ES 60601-1 & IEC 60601-1 and electromagnetic compatibility (EMC) testing per IEC 60601-1-2. |
  | MR Safety | MR characteristics are maintained. | Testing repeated per the latest applicable standards, previously cleared via K182475. |

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 does not specify the sample sizes used for the various tests (bench top, ex-vivo, in-vivo, usability, etc.). It also does not explicitly state the country of origin or whether the data was retrospective or prospective for these non-clinical studies. Given it's a 510(k) submission, the studies are typically conducted by the manufacturer (Covidien/Medtronic) and are usually prospective engineering, preclinical, or human factors studies designed for regulatory submission.

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

The document does not provide information on the number of experts or their qualifications for establishing ground truth for any of the studies mentioned. For pre-clinical animal studies (GLP), typically veterinarians and pathologists would be involved, and for usability, medical professionals, but specifics are not given.

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

The document does not describe any adjudication method for establishing ground truth in its studies. This type of detail is more common for clinical trials involving human interpretation of medical images or outcomes, which is not the primary focus of these non-clinical performance studies for a surgical stapler.

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:

The document does not mention any multi-reader multi-case (MRMC) comparative effectiveness study. This type of study is relevant for AI-powered diagnostic or interpretive devices involving human readers (e.g., radiologists interpreting images), which is not the nature of this surgical stapler reload. The device is a mechanical stapling device with a "powered stapling technology" and "software-controlled tissue compression," not an AI diagnostic assistant for human interpretation.

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

While the device has "software-controlled tissue compression," it is a surgical instrument used by a human surgeon. Therefore, a "standalone algorithm only" performance, in the sense of a diagnostic AI device, is not applicable or discussed. However, the software verification and validation activities essentially represent the "standalone" performance assessment of the integrated software components within the device's operational context.

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

The ground truth for the various non-clinical studies would likely be established as follows:

• Bench top/Ex-vivo: Engineering specifications, physical measurements (e.g., staple height, tissue gap, burst pressure, leak tests), and defined experimental outcomes.
• In-vivo/Chronic GLP study: Histopathological analysis (pathology records), clinical observation of healing progress, measurements of anastomotic integrity, and gross examination performed by trained veterinary professionals and pathologists.
• Usability: Observation of user interaction, task completion rates, error rates, and user feedback against predefined usability goals and safety criteria.
• Biocompatibility: Laboratory testing against ISO standards for material toxicity, sensitization, etc.

8. The sample size for the training set:

The document does not mention a "training set" in the context of machine learning, as this is a mechanical surgical device with integrated software for control, not an AI model that requires a training dataset for learning. The software is developed and validated through traditional software engineering V&V processes.

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

As no "training set" in the AI/ML sense is indicated, the concept of establishing ground truth for it is not applicable to this device. The software's correct functioning is established through design specifications, verification, and validation against intended logic and operational parameters.

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