(17 days)
The Coalescent Surgical U-CLIP™ is intended for endoscopic and non-endoscopic general soft tissue and prosthetic material approximation/attachment and/or ligation and the creation of anastomoses in blood vessels, grafts and other tubular structures; including use in cardiovascular and coronary artery bypass grafting procedures.
The Coalescent Surgical U-CLIP™ device is a self-closing clip for vascular anastomosis and tissue approximation/attachment applications. The U-CLIP consists of specially designed vascular clips optionally connected to needles via flexible members. These clips may not include coils surrounding a Nitinol wire core. This design allows precise placement of clips prior to closure and facilitation of an interrupted "suture" technique through elimination of knot tying. The device is fabricated from standard medical and implantable grade materials. Accessories include Clip delivery and removal devices as well as vessel retention tools.
Due to the nature of the provided text, which is an FDA 510(k) summary for a medical device and not a study report, it does not contain the specific acceptance criteria or the detailed results of a study as typically presented for AI/ML device evaluations. This document focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against quantified acceptance criteria for a novel AI/ML application.
Therefore, many of the requested items (e.g., sample sizes for test/training, number of experts, adjudication methods, MRMC study results, ground truth types for AI/ML) are not applicable or extractable from this document.
However, I can extract information related to the device characteristics and the claim of equivalence.
Here's a breakdown of the available information:
1. Table of Acceptance Criteria and Reported Device Performance:
The document doesn't define explicit numerical acceptance criteria for AI/ML performance metrics. Instead, it states:
| Criterion | Reported Device Performance |
|---|---|
| Functional Characteristics | "In vitro and in vivo data have confirmed that functional characteristics are substantially equivalent to the predicate device cited as well as applicable USP suture testing requirements. All data fell well within, both, internal specification requirements, as well as external standard requirements and device performance expectations." |
2. Sample Size Used for the Test Set and Data Provenance:
- Not Applicable. This document describes a medical device, not an AI/ML algorithm. The "test set" would refer to the in vitro and in vivo studies performed, but specific sample sizes are not provided in this summary.
- Data Provenance: The document does not specify the country of origin for the data or whether it was retrospective or prospective.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:
- Not Applicable. As this is not an AI/ML study, there is no mention of experts establishing a "ground truth" in the context of an algorithmic evaluation. The evaluation is based on standard biomedical testing.
4. Adjudication Method for the Test Set:
- Not Applicable. No information on adjudication methods for an AI/ML test set is present.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:
- Not Applicable. This is not an AI/ML device, so an MRMC comparative effectiveness study in the context of human readers analyzing AI output is not relevant nor mentioned.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:
- Not Applicable. This is a physical medical device, not an algorithm.
7. The Type of Ground Truth Used:
- The "ground truth" for this device's performance relies on established in vitro and in vivo biological and mechanical testing methods, and adherence to applicable USP suture testing requirements and internal/external standard requirements. It's not a "ground truth" in the sense of expert-annotated data for an AI/ML system.
8. The Sample Size for the Training Set:
- Not Applicable. This document does not describe the development or training of an AI/ML algorithm; therefore, a "training set" in that context is not relevant.
9. How the Ground Truth for the Training Set Was Established:
- Not Applicable. See point 8.
§ 878.4300 Implantable clip.
(a)
Identification. An implantable clip is a clip-like device intended to connect internal tissues to aid healing. It is not absorbable.(b)
Classification. Class II.