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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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Monoswift™ is a monofilament, synthetic absorbable suture indicated for use in soft tissue approximation and/or ligation, but not for use in cardiovascular or neurological surgery, microsurgery, or ophthalmic surgery.

Monoswift™ is a monofilament, synthetic absorbable suture indicated for use in soft tissue approximation and/or ligation, but not for use in cardiovascular or neurological surgery, microsurgery, or ophthalmic surgery.

This looks like a 510(k) summary for a medical device (Monoswift™ Synthetic Absorbable Suture), which is a premarket submission made to the FDA to demonstrate that the device is at least as safe and effective as a legally marketed predicate device.

The provided document does not contain the kind of detailed study information (like sample sizes, expert qualifications, adjudication methods, or MRMC studies) that would be present for evaluating a diagnostic AI/ML device. The document primarily focuses on demonstrating substantial equivalence to predicate devices based on design verification and USP requirements for needle attachment tensile strength.

Therefore, for this specific input, the requested information cannot be fully provided because it pertains to a different type of device evaluation (e.g., for AI/ML diagnostic software) than what is presented here.

However, I can extract the relevant information that is present in the document.

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

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 for Test Set: Not specified in the provided document.
• Data Provenance: Not specified in the provided document. The manufacturer is CP Medical, Portland, OR, USA. The testing was likely conducted in the US.
• Retrospective or Prospective: Not specified.

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 and not present for this type of device (surgical suture). Ground truth in the context of diagnostic AI/ML refers to diagnoses or classifications established by human experts or definitive tests. For a surgical suture, the "ground truth" relates to its physical and biological performance characteristics, measured through various laboratory and potentially in-vivo tests, rather than expert interpretation of data.

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

• Not applicable and not present for this type of device. Adjudication methods are relevant for resolving discrepancies in expert interpretations in diagnostic studies.

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. MRMC studies are used to evaluate the performance of diagnostic imaging devices (often with AI assistance). This device is a surgical suture.

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

• Not applicable. This is not an algorithm-only device.

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

• For this device, the "ground truth" or reference standard for verification is based on USP (United States Pharmacopeia) requirements for needle attachment tensile strength and comparison of breaking strength retention with predicate devices. This involves laboratory testing and performance metrics of the suture material itself, rather than expert interpretation of patient data or outcomes in the traditional sense of diagnostic devices.

8. The sample size for the training set:

• Not applicable. This device does not use a training set in the context of machine learning.

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

• Not applicable. This device does not use a training set.

In summary, the provided document outlines a 510(k) submission for a surgical suture, where "acceptance criteria" are related to physical and performance characteristics of the material (e.g., tensile strength, absorbability) as measured against established standards (like USP) and comparison to predicate devices, rather than the diagnostic performance metrics typically associated with AI/ML devices.

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AFB is indicated for use in surgical procedures in which soft tissue transection or resection with staple line reinforcement is needed.

The AFB (Absorbable Foam Buttress) Reload is a sterile, single patient use device which consists of two components: a cartridge and an anvil carrier. A strip of buttressing material is attached to both the cartridge and anvil carrier. AFB reinforces the staple line in soft tissue while it is simultaneously being cut and stapled. AFB is prepared from a synthetic, bioabsorbable copolymer similar to that used in synthetic absorbable sutures.

The AFB is integrated into endoscopic and open linear cutting/transecting staplers and reload cartridges. The two product reload cartridge configurations are suitable for the Endopath™ Endoscopic Linear Cutter used for VATS cases and the Linear Cutter used for OPEN cases.

This document is a 510(k) summary for the AFB (Absorbable Foam Buttress) Reload device. It describes the device, its intended use, and its substantial equivalence to predicate devices, but it does not contain acceptance criteria or a study that evaluates device performance against such criteria.

The document primarily focuses on:

• Device Description: What the AFB Reload is and how it functions.
• Predicate Devices: Listing similar devices already on the market to establish substantial equivalence.
• Non-Clinical and Pre-Clinical Testing: General statements about testing performed (polymer composition, viscosity, thickness, tensile strength, toxicology, biocompatibility, absorbability), but no specific performance metrics or acceptance criteria are provided.
• FDA Clearance: The letter from the FDA indicating clearance based on substantial equivalence.

Therefore, I cannot provide the requested information because the provided text does not include acceptance criteria or the details of a study that proves the device meets specific performance criteria. The 510(k) clearance process relies on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving performance against predefined acceptance criteria through an explicit study reported in this summary.

To answer your request, a document detailing specific performance studies with acceptance criteria would be needed.

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