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"Dimer Acid Derived Nanohybrid Composite" is a light cure restorative material indicated for direct esthetic restoration of anterior and posterior teeth. It provides simple esthetics since it is formulated in shades matching the VITA Lumin Shade guide. This low shrinkage nanohybrid composite can be used in all classes of direct dental restorations, Class I, II, III, IV, and V.

Dimer acid derived nanohybrid composite is for use in all dental cavity classes. The composite is a single light cured paste with a different monomer system chemistry than other filling composites that are currently available. The new resin system produces exceptionally high conversion values with a very low shrinkage.

This submission appears to be a 510(k) premarket notification for a dental composite material, not an AI/ML medical device. Therefore, the information requested about acceptance criteria and studies related to AI/ML device performance (such as sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance) is not applicable to this document.

The document describes a "Dimer Acid Derived Nanohybrid Composite" and primarily focuses on its intended use for dental cavity classes and claiming substantial equivalence to predicate devices, which is a common pathway for conventional medical devices.

Here's a breakdown of why an AI/ML-focused answer isn't possible from the provided text:

• No mention of AI/ML: The document does not contain any keywords or descriptions indicating the device incorporates artificial intelligence, machine learning, image analysis algorithms, or any form of computational intelligence.
• Device Type: It's a "Tooth shade resin material" or "light cure restorative material," which is a physical dental material, not a software or diagnostic imaging device.
• Substantial Equivalence: The basis for clearance is "substantial equivalence" to existing predicate devices based on functional similarity and indications for use. This process for conventional devices typically involves comparing material properties, performance characteristics (e.g., shrinkage, conversion values), and safety data, rather than AI performance metrics.

Therefore, I cannot provide a table of acceptance criteria and device performance, or details about sample sizes, experts, adjudication, or MRMC studies, as these concepts are not relevant to the information presented in this 510(k) summary.

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