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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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The Capio™ SLIM Suture Capturing Device is intended for use in general suturing applications during surgery to assist in the placement of suture material in tissues at the operative site. The Capio™ SLIM Suture Capturing Device is to be used with sensory and/or direct visual control.

The Capio SLIM Suture Capturing Device is also intended for use as an aid in the insertion, placement, fixation, or anchoring of surgical mesh during urogynecologic procedures.

The Capio™ SLIM Suture Capturing Device is urogynecologic mesh instrumentation used with urogynecologic surgical mesh. The subject device is also intended for use in general suturing applications during surgery to assist in placement of suture material in tissues, and assist in insertion or anchoring of surgical mesh for transabdominal pelvic organ prolapse repair.

The Capio™ SLIM Suture Capturing Device is a sterile, single use device and is designed to be used by the physician to facilitate the placement of sutures in difficult to access locations during open surgical procedures. The Capio™ SLIM device is also designed to throw, catch and retrieve sutures in one step. The device consists of a one-handactivated plunger at the proximal end, a tubular shaft and a curved needle driver at the distal end. Depressing the plunger actuates the carrier and drives the suture/dart through the tissue. The needle is automatically caught by the device's needle catch mechanism for easy suture tying, or the device may be re-loaded for additional suture throws.

This document is a 510(k) summary for a medical device (Capio™ SLIM Suture Capturing Device) and primarily focuses on demonstrating substantial equivalence to a predicate device. It is not a study that proves the device meets specific acceptance criteria in the way a clinical or performance study of an AI/imaging device would.

Based on the provided text, here is an analysis regarding "acceptance criteria" and "study that proves the device meets the acceptance criteria":

This document does NOT describe the acceptance criteria or a study proving device performance in the context of an AI/imaging diagnostic device.

Instead, it describes the non-clinical performance tests conducted to demonstrate that the device meets applicable design and performance requirements and supports substantial equivalence to a predicate device. The "acceptance criteria" in this context refer to the successful completion and passing of these engineering and safety tests.

Here's how to interpret the request with the provided information:

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

The document lists various non-clinical performance tests. For each test, the "reported device performance" is implicitly that the device passed or met the requirements, thus demonstrating sufficient performance for its intended use and compliance with special controls. Specific numerical criteria or results are not provided for each test in this summary.

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

This information is not provided in the document. The tests described are primarily engineering, mechanical, and biological tests on device prototypes or manufactured units, not on a "test set" of patient data.

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 and therefore not provided. The ground truth for these non-clinical tests is based on established engineering standards, material science, and biological testing protocols, not expert medical opinion on patient data.

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

This information is not applicable and therefore not provided. Adjudication methods are relevant for studies involving human interpretation or uncertain diagnoses, which is not the case for these non-clinical device tests.

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

There was no MRMC comparative effectiveness study done. This device is a physical surgical instrument, not an AI or imaging device that assists human readers.

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

There was no standalone (algorithm only) performance study done. This device is a physical surgical instrument, not an algorithm.

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

As explained previously, for the non-clinical tests, the "ground truth" is based on:

• Engineering standards and specifications: For dimensional, functional, compression, tensile, deflection, needle retention, catch pull, and actuation tests.
• Established protocols for sterilization and packaging integrity: For sterilization validation, package integrity, and distribution challenge tests.
• Standardized biocompatibility test methods: For cytotoxicity, sensitization, irritation, acute systemic toxicity, and material-mediated pyrogenicity.
• Industry standards for accelerated and real-time aging: For shelf-life testing.

8. The sample size for the training set

This is not applicable as this is not an AI/machine learning device.

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

This is not applicable as this is not an AI/machine learning device.

In summary, the provided document describes the engineering, mechanical, sterility, packaging, and biocompatibility performance data for a physical surgical device, demonstrating that it meets design requirements and supports substantial equivalence. It does not contain information typically associated with clinical studies or AI device performance evaluation.

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