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Nova Resin dual cure, self adhesive resin cementation of all indirect restorations including ceramic, resin and metal-based inlays, onlays, bridges, posts and veneers. *

Additional indications include core-buildup material, pit and fissure sealant, and cementation of crowns restorations to implants.

• Adhesive application on the prep is required for veneer cementation using Nova Resin dual cure, self adhesive resin cement.

Resin Cement dual cure, self adhesive resin cement for the cementation of indirect restorations made of high strength all ceramic, composite, metal-ceramic and metal.

When Resin Cement dual cure, self adhesive resin cement is used, additional bonding agents are not required. The automix double syringe enables saving time direct application of the luting cement into restoration.

This document, an FDA 510(k) summary for the "NOVA RESIN dual cure, self adhesive resin cement," describes the device and its equivalence to a predicate device, but it does not describe an AI/ML device or its associated acceptance criteria and study data.

The document focuses on the physical and chemical properties of a dental cement. The "acceptance criteria" and "reported device performance" provided in the table are for these physical properties (e.g., film thickness, flexural strength), not metrics related to AI/ML performance like sensitivity, specificity, or AUC.

Therefore, I cannot extract the information requested regarding a study proving an AI/ML device meets acceptance criteria, as this document does not pertain to an AI/ML device.

Here's why the requested information cannot be found in the provided text:

• No mention of AI/ML device or algorithm: The entire document describes a dental cement and its physical properties. There is no mention of algorithms, machine learning, artificial intelligence, or any form of software that processes data for diagnostic or predictive purposes.
• Acceptance criteria are for physical properties: The table provided lists metrics such as Film Thickness, Working Time, Flexural Strength, etc. These are standard engineering and material science metrics for dental cements, not performance metrics for an AI system.
• No "test set," "training set," "ground truth," or "experts" in the AI context: These terms are specific to the validation of AI/ML models. The document refers to "non-clinical performance testing" which are bench tests, not clinical studies involving human experts or patient data for AI validation.
• No MRMC study: A Multi-Reader Multi-Case study is a type of clinical trial specifically designed to evaluate the impact of AI on human reader performance in medical imaging. The provided document does not mention any clinical studies of this nature.
• No "standalone performance": "Standalone" performance refers to the algorithm's performance without human intervention. This is irrelevant for a dental cement.

In summary, this document is a regulatory submission for a dental material, not an AI/ML medical device.

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Self-adhesive Resin Cement is intended for the cementation of indirect restoratives including ceramic, composite and metal-based inlays, onlays, crowns, bridges, and posts.

Self-adhesive Resin Cement consists of a base paste and a catalyst paste, mixed to form a dual-curing cement. The Cement is available in five shades and is delivered in double-barrel syringes and unit dose systems.

The provided 510(k) summary for K073173 describes a Self-adhesive Resin Cement and focuses on its substantial equivalence to predicate devices (Unicem, K020256 and MaxCem, K041474). This type of device (dental cement) does not typically involve the kinds of acceptance criteria and performance studies (e.g., diagnostic accuracy, multi-reader multi-case studies) that are common for AI/ML-enabled devices or diagnostic tools.

Instead, the acceptance criteria for a dental cement would revolve around physical and chemical properties, biocompatibility, and adhesive performance as defined by relevant industry standards and comparison to predicate devices. The study to prove it meets acceptance criteria would be a series of laboratory tests demonstrating these properties.

Based on the provided text, here's an attempt to answer the questions, keeping in mind the nature of the device:

Acceptance Criteria and Device Performance for Self-adhesive Resin Cement (K073173)

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary for K073173 does not provide specific numerical acceptance criteria or detailed performance data in a tabular format. The review focuses on substantial equivalence based on the use of previously approved components and general performance and biocompatibility claims.

However, based on the text, the implicit acceptance criteria and reported performance are as follows:

Note: Specific quantitative values for properties like bond strength, film thickness, radiopacity, or water sorption, which would typically be part of a dental cement’s performance evaluation, are not present in this summary document. The substantiation is primarily qualitative and comparative to existing devices.

2. Sample Size for the Test Set and Data Provenance

The document does not specify sample sizes for any tests conducted. The "performance data provided" is mentioned but not detailed. Given the nature of a dental cement, a "test set" in the context of an AI/ML device (e.g., patient data) is not applicable here. Instead, it refers to samples of the cement material itself that underwent laboratory testing.

The data provenance is not explicitly stated (e.g., country of origin of data). Such testing is typically conducted in a laboratory setting by the manufacturer or a contracted lab. There is no indication of retrospective or prospective patient data studies.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This question is not applicable to this device. Dental cements are evaluated based on physical, chemical, and biological properties, not by expert interpretation of images or clinical outcomes that require a "ground truth" derived from human experts in the way that AI/ML diagnostics do.

4. Adjudication Method for the Test Set

This question is not applicable to this device. Adjudication methods like 2+1 or 3+1 are used for resolving disagreements among human readers/experts in diagnostic studies, which is not relevant for a dental cement.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not done. MRMC studies are used to evaluate the diagnostic performance of human readers, with and without AI assistance, typically in medical imaging. This is not pertinent to a dental cement.

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

This question is not applicable to this device. There is no algorithm or AI component in this self-adhesive resin cement. It is a physical material.

7. The Type of Ground Truth Used

The term "ground truth" as it applies to diagnostic or prognostic studies is not applicable here. The "truth" for a dental cement is established through standardized laboratory tests that measure its physical, chemical, and biological properties against established benchmarks or predicate device performance. For example:

• Physical Property Tests: ASTM or ISO standards for bond strength, film thickness, setting time, etc.
• Biocompatibility: In vitro and in vivo toxicology tests (e.g., ISO 10993 series).

8. The Sample Size for the Training Set

This question is not applicable to this device. There is no AI model requiring a training set. The "development" of the cement involves formulation and testing, not machine learning.

9. How the Ground Truth for the Training Set was Established

This question is not applicable to this device, as there is no training set or AI model.

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