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The Digitex Delivery Device is a single-use device intended for use as an aid in suturing transvaginal pelvic organ prolapse procedures with surgical mesh.

The Digitex® Suture Delivery System is composed of a delivery device and suture cartridge and is designed for use by the physician to facilitate the consistent placement of suture when direct visualization is not possible and/or the anatomical location is difficult to reach. The shaft of the device has been designed to allow for adjusting the angle of the needle housing to further facilitate suture placement in the desired location.

This document (K173527) is a 510(k) Premarket Notification for the Digitex Delivery Device, a specialized surgical instrument. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a standalone study with acceptance criteria for an AI/algorithm.

Therefore, the requested information regarding acceptance criteria, study details, sample sizes, expert involvement, ground truth, and MRMC studies for an AI device is NOT available in this document. The document describes non-clinical performance testing for a physical medical device.

However, I can extract the information relevant to the device's non-clinical performance testing and its acceptance (as stated in the document), though it's not structured in the way you asked for an AI/algorithm study.

Here's what can be extracted based on the provided text, rephrased to fit your prompt as much as possible, while clarifying that this is for a physical device, not an AI:

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

The document mentions that "The results of all performance testing met pre-defined acceptance criteria as applicable and are acceptable." However, the specific quantitative acceptance criteria or the raw performance data for each test are not detailed in this summary. It only states that the results met the criteria.

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

This document describes non-clinical physical device testing. Sample sizes for these engineering tests and data provenance in terms of country of origin or retrospective/prospective nature are not specified in this 510(k) summary. These types of details would typically be found in the full test reports submitted to the FDA, not in the public 510(k) 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)

This is not applicable to the non-clinical physical device testing described. The "ground truth" for these tests relates to engineering specifications and material standards, not expert medical consensus on diagnostic images.

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

Not applicable. This is not a study involving human readers or expert adjudication of medical cases.

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 document is for a physical surgical device, not an AI or imaging diagnostic tool. No MRMC study was performed or is relevant.

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

Not applicable. This is not an algorithm or software device.

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

The "ground truth" for the non-clinical performance tests is based on engineering specifications, material standards (e.g., ISO 10993-1 for biocompatibility), and pre-defined acceptance criteria for physical device performance (e.g., sterilization efficacy, package integrity, mechanical strength).

8. The sample size for the training set

Not applicable, as this is not an AI/machine learning device. No training set was used.

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

Not applicable.

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