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The LARIAT® III Suture Delivery Device facilitates suture placement and knot tying for use in surgical applications where soft tissue are being approximated and/or ligated with a pre-tied polyester suture.

The LARIAT® III Suture Delivery Device is a one-piece, single-use suture delivery and deployment device with a pre-tied size "0" polyester suture loop that is pre-loaded on a retractable Delivery Snare on the distal end of the device. The pre-tied suture loop is held onto the Delivery Snare and allows for multiple opening and closings of the suture loop without release. Upon tightening of the suture loop, the suture is released from the Delivery Snare. An accessory lumen within the LARIAT III device is designed for aspiration and flushing. The LARIAT III device is compatible with access site diameters of 4.3mm and larger. The suture is itself a cleared medical device as a part of K021019.

The provided document is a 510(k) premarket notification for the LARIAT® III Suture Delivery Device. It details the device's characteristics, intended use, and claims of substantial equivalence to a predicate device (LARIAT II Suture Delivery Device). This type of submission relies on comparisons to already-cleared devices and bench testing rather than clinical study data for device performance metrics related to diagnostic accuracy or clinical outcomes often seen with AI/ML devices. Therefore, a direct application of typical "acceptance criteria" for AI/ML performance metrics and detailed clinical study information is not present in this document.

However, I can extract the information that is available regarding the device's performance through bench testing in support of its substantial equivalence claims.

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

The document does not explicitly state numerical "acceptance criteria" with thresholds for each test, but rather lists the types of bench tests performed to confirm the device performs "according to the stated intended use." The reported device performance is broadly stated as successful completion of these tests.

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

The document specifies "bench testing" was conducted. This typically refers to laboratory-based tests on physical devices or components, not clinical data. Therefore, details like "country of origin of the data" or "retrospective or prospective" do not apply. The sample size for each specific bench test is not provided in this summary.

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

Not applicable. Bench testing of a mechanical device does not typically involve human experts establishing ground truth in the way it would for an AI diagnostic device. The "ground truth" for these tests would be derived from engineering specifications and widely accepted physical/material science principles.

4. Adjudication method for the test set

Not applicable, as this refers to a method used to resolve discrepancies among human experts during ground truth establishment for clinical/diagnostic performance, which is not relevant for this type of bench testing.

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. This device is a surgical tool, not an AI/ML diagnostic or assistive device that would involve human "readers" or AI assistance.

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

Not applicable. This device is a mechanical surgical tool, not an algorithm.

7. The type of ground truth used

For bench testing, the "ground truth" is typically defined by accepted engineering specifications, material properties, and functionality requirements rather than expert consensus, pathology, or outcomes data. For example, a "suture tensile strength test" would have a specified minimum tensile strength (ground truth) that the suture must meet.

8. The sample size for the training set

Not applicable. This device is not an AI/ML model that requires a training set.

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

Not applicable. This device is not an AI/ML model that requires a training set.

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