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Demetech Absorbable Poliglecaprone 25 Surgical Suture is indicated for use in general soft tissue approximation and/or ligation, but not for use cardiovascular surgery, microsurgery, ophthalmic surgery and neurological tissue.

DemeCAPRONE (Poliglecaprone 25) is a synthetic monofilament absorbable surgical suture composed of Poly (glycolic-co-caprolactone) copolymer (PGCL) and is supplied un-dyed and dyed with D&C Violet #2 below 0.1wt%. DemeCAPRONE (Poliglecaprone 25) synthetic absorbable suture is available in sizes 6-0 through 1 (metric sizes 0.7 - 4). DemeCAPRONE (Poliglecaprone 25) Surgical Suture meets the requirements established by the United States Pharmacopeia (U.S.P.) for synthetic absorbable surgical sutures except for diameter.

The provided document describes the 510(k) summary for DemeCAPRONE (Poliglecaprone 25) Synthetic Monofilament Absorbable Suture. This document focuses on demonstrating substantial equivalence to predicate devices for a surgical suture, not an AI/ML powered medical device. Therefore, much of the requested information regarding AI/ML device studies (e.g., sample sizes for training/test sets, expert adjudication, MRMC studies, standalone performance) is not applicable to this submission.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text, while acknowledging the inherent differences for a sutures submission:

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

The acceptance criteria for DemeCAPRONE (Poliglecaprone 25) suture are based on demonstrating equivalence to legally marketed predicate devices and conformance to established standards, primarily the United States Pharmacopeia (U.S.P.) for synthetic absorbable surgical sutures, and ISO 10993 for biocompatibility.

2. Sample sizes used for the test set and the data provenance:

• Sample Size for Test Set: The document does not specify exact sample sizes for each non-clinical test (e.g., number of sutures tested for tensile strength, length, etc.). However, it states that "Non-clinical testing was conducted on the device per FDA's Special Control Guidance Document: Surgical Sutures, to prove conformance to the requirements of USP for synthetic absorbable suture, biocompatibility testing in accordance to ISO 10993-1 and invivo and in-vitro resorption." This implies that the sample sizes used were in accordance with the specified standards and guidance documents.
• Data Provenance: The studies were non-clinical (laboratory and animal studies for biocompatibility, in-vivo/in-vitro resorption). The location of the testing facilities is not explicitly stated, but the applicant is Demetech Corporation, Miami Lakes, FL, USA.
• Retrospective or Prospective: The testing would have been prospective, as new tests were performed on the DemeCAPRONE suture to demonstrate its properties.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• This question is not applicable for this type of device submission. Ground truth, in the context of AI/ML, refers to human expert annotations. For a surgical suture, the "ground truth" is adherence to physicochemical and biological standards (e.g., USP monographs, ISO 10993). The "experts" would be the scientists and technicians conducting the non-clinical tests and interpreting the results against established specifications, not medical domain experts creating ground truth for an algorithm.

4. Adjudication method for the test set:

• This is not applicable. Adjudication methods are used in consensus agreement among human annotators, which is not relevant for the objective physical and chemical testing of a surgical suture. The results are quantitative measurements compared against predefined acceptance limits.

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:

• This is not applicable. An MRMC study is for evaluating observer performance (e.g., radiologists interpreting images). This submission is for a physical medical device (suture) and does not involve human "readers" or AI assistance.

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

• This is not applicable. This describes the performance of a standalone AI algorithm. The DemeCAPRONE suture is a physical device, not an algorithm.

7. The type of ground truth used:

• The "ground truth" for the device's performance is established by objective, established standards and methodologies.
  • Pharmacopeia Standards (USP): These define the chemical, physical, and pharmaceutical quality of drugs and medical devices. For sutures, this includes specifications for tensile strength, diameter, length, sterility, etc.
  • ISO 10993 Biocompatibility Standards: These define methods for evaluating the biological response to medical devices.
  • Predicate Device Performance: Part of the substantial equivalence relies on demonstrating that the new device's properties (including resorption) are similar to those of previously cleared predicate devices.

8. The sample size for the training set:

• This is not applicable. There is no "training set" in the context of this 510(k) submission for a surgical suture. Training sets are used for machine learning models.

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

• This is not applicable. As there is no training set for an AI/ML model, the concept of establishing ground truth for it does not apply.

In Conclusion:

The DemeCAPRONE suture's acceptance is based on demonstrating substantial equivalence to predicate devices and conformance to recognized consensus standards (USP) and international standards (ISO 10993). The "study" proving this involves a battery of non-clinical (laboratory and animal) tests rather than clinical trials or AI/ML-specific performance studies. The primary evidence presented is the declaration that DemeCAPRONE "meets or exceeds" the relevant performance requirements and is "Same" in composition, design, and manufacturing process to established predicate devices.

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