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NovaShield "" is indicated for use in patients undergoing nasal/sinus surgery as a space occupying packing to:

• . Separate tissue or structures compromised by surgical trauma;
• Separate and prevent adhesions between mucosal surfaces in the nasal cavity; .
• . Control minimal bleeding following surgery or trauma by tamponade effect, blood absorption and platelet aqgreqation
• . Act as an adjunct to aid in the natural healing process

NovaShield™ is indicated for use as a nasal packing to treat epistaxis.

NovaShield™ is a single use, injectable nasal packing and stent for use following sinus surgery to prevent adhesions, control mild bleeding and provide a level of antibacterial effectiveness. NovaShield™ is composed of formulated chitosan and cellulose ingredients in a fully mixed and hydrated gel form provided in a prefilled delivery system. The syringe delivery system conveys the gel to the patient via an accordion cannula that can be manipulated to assist with the gel application. NovaShield™ is eliminated via hydrolysis and gentle irrigation using saline or water in approximately 7-14 days.

NovaShield™ is intended for use in patients undergoing sinus surgery as a space occupying packing to separate tissue or structures compromised by surgical trauma. NovaShield™ is designed to separate tissue and prevent adhesions between mucosal surfaces during healing in the nasal cavity and for the treatment of mild bleeding from topical surgical wounds and nosebleeds. NovaShield™ may be used for the local management of wounds that are prone to bleeding such as wounds that have been surgically or mechanically debrided and for the management of surgical or traumatic wounds which have been left to heal by secondary intention. NovaShield™ also has been shown in laboratory studies to provide a level of antibacterial activity.

The provided text describes a 510(k) premarket notification for the NovaShield™ Injectable Nasal Packing and Stent. This type of regulatory submission typically focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than conducting extensive clinical studies with specific acceptance criteria for performance metrics like sensitivity, specificity, or reader studies for AI devices.

Therefore, the requested information regarding acceptance criteria, study details (like sample size, ground truth, expert qualifications), MRMC studies, or standalone algorithm performance, as typically found for AI/ML medical devices, is not applicable in this context. The NovaShield™ device is a physical medical device (nasal packing and stent), not an AI/ML diagnostic or prognostic tool.

Here's a breakdown based on the provided document:

1. Table of acceptance criteria and the reported device performance:

This device does not have acceptance criteria in the typical sense of sensitivity, specificity, or other performance metrics usually reported for diagnostic devices or AI solutions. The "acceptance criteria" here are implicitly linked to demonstrating substantial equivalence to predicate devices for its intended use through various tests.

• Acceptance Criteria (Implicit): The device must perform comparably to predicate devices in terms of its intended use functions and safety.
• Reported Device Performance:
  • Acts as a space-occupying packing to separate tissue/structures and prevent adhesions.
  • Controls minimal bleeding by tamponade effect, blood absorption, and platelet aggregation.
  • Acts as an adjunct to aid in the natural healing process.
  • Treats epistaxis.
  • Demonstrates antibacterial activity against various specified bacterial strains (in vitro only, not correlated to clinical effectiveness).

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

• Sample Size: Not explicitly stated in terms of patient numbers or a test set as for a clinical trial for performance metrics. Performance testing involved "bench and animal testing."
• Data Provenance: Not specified, but likely laboratory-based (bench testing) and animal studies.

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 is not a study requiring expert consensus or ground truth establishment in the context of diagnostic interpretation. The "ground truth" for this device would be its physical and biological performance in animal models and in vitro tests, validated by standard scientific methods and potentially surgical outcomes.

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

• Not Applicable. This is not a study requiring adjudication of expert opinions for a 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:

• Not Applicable. This is not an AI device.

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

• Not Applicable. This is not an AI device.

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

• The "ground truth" for this device's performance would be established through physical/material testing (bench testing), in vitro biological assays (antibacterial activity), and in vivo animal studies. For example, for antibacterial activity, the ground truth is the measured reduction in bacterial growth under controlled laboratory conditions. For prevention of adhesions or control of bleeding, it would be observed outcomes in animal models.

8. The sample size for the training set:

• Not Applicable. This is not an AI device, so there is no "training set."

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

• Not Applicable. No training set for an AI algorithm.

In summary: The provided document is a 510(k) clearance letter for a physical medical device. The focus of such submissions is on demonstrating substantial equivalence to existing devices through a combination of performance testing (bench and animal), material characterization, and safety data, rather than clinical trials with specific diagnostic performance metrics typically seen in AI/ML device submissions.

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