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StitchKit® Suture Delivery Canister facilitates minimally invasive robotic surgery by transporting suture to the operative field and removing used needles after suturing. The suture contained within the StitchKit® device is intended for soft tissue approximation where use of the specific absorbable or non-absorbable sutures contained within it is appropriate.

StitchKit® COMBO is a suture delivery canister which facilitates endoscopic robotic surgery by introducing multiple strands of different suturing materials to the surgical site at one time and allowing for the safe retrieval of the needles. It is sized to be passed through a ≥12 mm trocar. As suturing is completed with each strand, the used needle is placed into a disposal compartment within the canister for safekeeping until the entire canister is removed through the trocar using the attached retrieval string. It is supplied sterile in a foil pouch. Each StitchKit® version contains strands of existing legallymarketed Suturing Materials, cleared for use within the StitchKit®.

This looks like a 510(k) summary for a medical device called "StitchKit® COMBO Suture Delivery Canister". Based on the provided text, the device is explicitly a suture delivery system and not an AI or imaging device, so the requested information about AI performance, ground truth, expert adjudication, etc., is not applicable.

Here's a breakdown of why the requested information cannot be provided given this document:

• No AI component: The document describes a "suture delivery canister which facilitates minimally invasive robotic surgery by transporting suture to the operative field and removing used needles after suturing." There is no mention of artificial intelligence, machine learning, image analysis, or any diagnostic capabilities.
• Physical device, not a diagnostic algorithm: The device handles physical sutures and needles. The performance data listed (knot-pull tensile testing, diameter, needle attachment, biodegradation testing, pyrogen testing, physico-chemical analysis, stability evaluations) are all related to the physical properties and safety of the surgical sutures and the delivery system itself, not to the performance of a software algorithm.

Therefore, since the device is not an AI/ML medical device, the following points from your request are not applicable and cannot be found in the provided text:

1. A table of acceptance criteria and the reported device performance (The "performance data" mentioned refers to physical product testing, not AI performance metrics)
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
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)
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
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
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
8. The sample size for the training set
9. How the ground truth for the training set was established

In summary, this document describes a traditional medical device (suture delivery system) and does not provide any information relevant to the performance or validation of an AI/ML component.

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The device is indicated for use in soft tissue approximation or ligation and ophthalmic surgery, but not in cardiovascular or neural tissue.

Polysorb* Synthetic Absorbable Suture with Modified Packaging shall remain identical to the predicate device, an absorbable poly(glycolide/L-lactide) surgical suture. with the addition to the packaging of an injection port to the inner foil pouch.

The provided document is a 510(k) summary for a medical device, specifically an absorbable surgical suture. It describes the device, its intended use, and its equivalence to a predicate device. It does not present a study that establishes acceptance criteria for new functionality or a new AI/software-driven device. Instead, it refers to "in-vitro studies" to demonstrate substantial equivalence to a predicate device for an existing product with a modified packaging.

Therefore, many of the requested categories are not applicable to the information provided in this 510(k) summary. I will fill in the applicable information and note when a section is not applicable.

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

The document does not specify quantitative acceptance criteria. It states that "In-vitro studies were performed...to demonstrate that the functional performance of the suture is substantially equivalent to the predicate device when pre-wetted with sterile saline (0.9% NaCl) using the injection port for up to 12 hours at room temperature prior to use." This implies that the performance after pre-wetting should be comparable to the predicate device.

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 510(k) summary.
• Data provenance: Not specified, but the studies were "in-vitro."

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. This device is a surgical suture, and the studies were "in-vitro" to assess physical properties and performance, not diagnostic interpretations requiring expert ground truth establishment.

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

Not applicable. See point 3.

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 is not an AI/software-driven device.

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

Not applicable. This is not an AI/software-driven device.

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

Not applicable. The equivalence was based on "functional performance" in "in-vitro studies," likely involving mechanical testing or other objective physical measurements, rather than clinical ground truth as understood in diagnostic AI.

8. The sample size for the training set

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

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

Not applicable. See point 8.

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