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Myriad™ Particles is indicated for use in the management of the following wounds:

• · partial and full-thickness wounds
• · pressure ulcers
• venous ulcers .
• diabetic ulcers .
• chronic vascular ulcers .
• . tunneled/undermined wounds
• Surgical wounds (donor sites/grafts, post-Moh's surgery, post-laser surgery, podiatric, . wound dehiscence)
• . trauma wounds (abrasions, lacerations, partial-thickness burns, and skin tears)
• · draining wounds

Myriad™ Particles is derived from an extracellular matrix primarily composed of ovine collagen and is supplied as a sterile particulate.

The provided document is a 510(k) premarket notification for a medical device called "Myriad™ Particles." This type of submission is for demonstrating substantial equivalence to a legally marketed predicate device, rather than proving a device meets specific clinical performance acceptance criteria through the kind of study described in the prompt.

Therefore, the document does not contain the information requested regarding acceptance criteria and performance studies. The key points from the document that illustrate this are:

• No Clinical Performance Data Used for Substantial Equivalence: Section 5.6 explicitly states: "Substantial equivalence was not based on an assessment of clinical performance data." This means a human clinical study to prove performance was not conducted for this 510(k) submission.
• Focus on Substantial Equivalence through Technological Characteristics and Non-Clinical Data: The document relies on comparing the technological characteristics of Myriad™ Particles to its predicate device (Endoform™ Dermal Template) and presenting non-clinical (bench and biocompatibility) testing. The primary difference highlighted is the device presentation (powder vs. sheet format).
• No mention of AI/ML components: The device is a wound dressing, and there is no indication that it incorporates any artificial intelligence or machine learning components. Therefore, an MRMC comparative effectiveness study or standalone algorithm performance assessment would not be applicable.

In summary, the provided text does not describe a study designed to prove the device meets specific acceptance criteria in the way a clinical performance study for an AI/ML device would. It focuses on demonstrating substantial equivalence to an existing device through non-clinical means.

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