Search Results

The SafeStitch AMID Stapler® has application 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 SafeStitch AMID Stapler™ contains 17 titanium staples. The AMID Stapler™ places a staple each time the instrument's handle is squeezed. The staple legs first penetrate the tissue or mesh and then fully form, thus anchoring or approximating the tissue(s) and/or mesh.

This document describes a 510(k) submission for the SafeStitch AMID Stapler®, which is a medical device. The submission focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing. Therefore, the information provided does not align with the typical requirements for describing acceptance criteria and a study for an AI/ML powered medical device.

Based on the provided text, the device is a manual surgical stapler, not an AI-powered device. As such, many of the requested categories (e.g., sample size for test set, data provenance, number of experts, adjudication method, MRMC study, standalone performance, training set size, ground truth for training set) are not applicable.

Here's the relevant information derived from the document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document describes "Design verification testing" to ensure the subject device meets required acceptance criteria and functions equivalently to the predicate device. The results are reported as "Pass" for both the predicate and subject devices, indicating they met the defined criteria. While the specific numerical acceptance thresholds are not provided in this summary, the criteria are implied by the test methods.

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

• Sample Size: Not specified in the provided text. The document states "Design verification testing was conducted," but does not mention the number of units tested.
• Data Provenance: Not specified. The testing was non-clinical, so country of origin of patient data is not applicable. This was likely internal company testing.
• Retrospective or Prospective: Not applicable as this is non-clinical device testing, not a study involving 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):

Not applicable. Ground truth as typically defined for AI/ML devices (e.g., expert consensus on medical images) is not relevant for the non-clinical mechanical testing of a surgical stapler. The "ground truth" for these tests would be the established engineering specifications and performance standards.

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

Not applicable. Adjudication methods are typically used in studies involving subjective assessments or disagreements among human readers/experts, which is not the case for objective mechanical device 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. An MRMC study is relevant for AI-powered diagnostic or assistive devices involving human interpretation. This document describes a manual surgical stapler.

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

Not applicable. This is not an algorithm or an AI system. The testing focused on the mechanical performance of a physical device.

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

The "ground truth" for this device's testing would be engineering specifications and predefined performance criteria for mechanical properties (e.g., specific force required, dimensions of formed staples, pull strength thresholds). The tests were designed to verify the device met these objective, measurable standards.

8. The sample size for the training set:

Not applicable. This is not an AI/ML device, so there is no "training set."

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

Not applicable, as there is no training set for this type of device.

Ask a specific question about this device

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.

Ask a specific question about this device