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Polyester Surgical Sutures are indicated for use in general soft tissue approximation and/or ligation, including use in cardiovascular, ophthalmic orthopedic and neurological procedures.

Polyester Nonabsorbable Surgical Suture, USP size 9-0 through 9 available as undyed, dyed D & C Green No. 6 and as a co-braid of the undyed and dyed. The suture is sterile, braided and is provided in a variety of lengths, with or without pledgets, with or without needles and may be supplied in a variety of cut lengths or on ligating reels.

This submission is for a surgical suture and therefore the concept of AI/ML performance acceptance criteria and study design does not apply. The provided text describes the device, its intended use, and its substantial equivalence to a predicate device, relying on established performance standards for surgical sutures rather than AI/ML-specific evaluations.

Here's why the AI/ML-focused questions are not applicable:

• Device Type: The device is a "Polyester Nonabsorbable Surgical Suture." This is a tangible medical device used for physically joining tissues, not a software algorithm, diagnostic imaging tool, or any other device that would typically involve AI/ML.
• Evaluation Basis: The determination of substantial equivalence is based on:
  • Detailed device description.
  • Performance testing (implied by conformance to standards).
  • Conformance with voluntary performance standards (e.g., ANSI/AAMI/ISO 10993-1 Biological Evaluation of Medical Devices, USP Section XXV - Nonabsorbable Surgical Sutures, and the Guidance Document "Guidance for Surgical Suture 510(k)s"). These are standards for the physical and biological properties of sutures, not for algorithmic performance.
• Lack of AI/ML Metrics: The document does not mention any metrics like sensitivity, specificity, AUC, F1-score, precision, recall, or any other terms typically associated with AI/ML model performance.
• No "Readers" or "Ground Truth" as defined for AI: The concepts of human "readers" interacting with an AI, "ground truth" derived from expert consensus for image interpretation, or "training sets" and "test sets" for machine learning are absent from the provided text.

Therefore, since this device does not involve AI/ML, I cannot provide details on:

1. A table of acceptance criteria and reported device performance related to AI/ML.
2. Sample sizes for test sets or data provenance for AI/ML.
3. Number and qualifications of experts for AI/ML ground truth.
4. Adjudication method for AI/ML test sets.
5. MRMC comparative effectiveness study or human reader improvement with AI.
6. Standalone AI algorithm performance.
7. Type of ground truth used for AI/ML.
8. Sample size for AI/ML training set.
9. How ground truth for AI/ML training set was established.

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