(27 days)
The SafeStitch LLC AMID Stapler & Non-Absorbable Staples has applications in general surgery procedures for fixation of mesh, in the repair of hernia defects and in other surgical specialties for the approximation of tissue(s), including skin.
The AMID Stapler is a sterile, single use disposable stapler. The AMID Stapler consists of a manual stapler and 17 titanium staplers. It is designed for the stapling of tissue and mesh, specifically for hernia repairs
The provided text describes a 510(k) premarket notification for the SafeStitch AMID Stapler, a surgical stapler for fixation of mesh in hernia repairs and approximation of tissue. The submission focuses on demonstrating substantial equivalence to predicate devices through bench testing.
Here's an analysis of the requested information based on the provided text:
1. Table of acceptance criteria and the reported device performance:
The document states: "Bench testing was performed to verify the AMID Stapler's performance to internal specifications. In addition, bench testing was also performed to demonstrate that the AMID Stapler is substantially equivalent to the predicate device(s)."
However, the specific acceptance criteria (e.g., minimum staple retention force, maximum staple deployment force, staple formation consistency) and the quantitative reported performance of the AMID Stapler against these criteria are not provided in the given text. It only vaguely mentions "internal specifications" and "substantially equivalent to the predicate device(s)" without detailing what those specifications or equivalence metrics are.
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 mentions "bench testing" but does not specify the sample size used for these tests. There is no information provided about the country of origin of the data or whether the study was retrospective or prospective. Bench testing typically falls under laboratory or engineering studies rather than clinical data from human subjects.
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 to the "Performance Data" described. The performance data discussed is "bench testing" which usually involves engineering measurements and evaluations, not expert opinion or a ground truth established by medical experts in the way clinical studies for diagnostic devices might.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
As with point 3, this is not applicable to the type of performance data (bench testing) described. Adjudication methods are typically used in clinical studies where expert consensus is needed to establish a "ground truth" for ambiguous 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:
The provided text does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. The device is a surgical stapler, not an AI-powered diagnostic tool, so such a study would not be relevant.
6. If a standalone (i.e. algorithm only, without human-in-the-loop performance) was done:
This question is not applicable as the device is a medical device (surgical stapler), not an algorithm or AI system.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
For the bench testing, the "ground truth" would be the established engineering specifications or performance characteristics of the predicate device(s) that the AMID Stapler aimed to demonstrate substantial equivalence to. However, the exact nature of these "ground truths" (e.g., specific tensile strength, staple formation under specific tissue thickness) is not detailed in the provided document.
8. The sample size for the training set:
This is not applicable. Surgical staplers are mechanical devices and typically do not involve "training sets" in the context of machine learning or AI. The product validation involves engineering tests against predefined specifications.
9. How the ground truth for the training set was established:
This is not applicable for the same reasons as point 8.
§ 878.4750 Implantable staple.
(a)
Identification. An implantable staple is a staple-like device intended to connect internal tissues to aid healing. It is not absorbable.(b)
Classification. Class II.