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• Restoration of minimally invasive cavity preparations (including occuusal pits and fissures)
• Conservative anterior restorations (Classes III and IV)
• Base/liner under direct restorations
• Repair of fillings and veneers

AXCEL FLOW is a visible light cure resin-based flowable restorative dental composite material, comprised of a Bis-GMA based resin, barium glass filler and fumed silica, suitable for class III, class IV and class V restorations. It utilizes the same resin components found in AXCEL restorative composite. The cured product has high strength and a high surface luster.

The provided text describes a 510(k) premarket notification for a dental restorative material called AXCEL FLOW. It does not contain information about acceptance criteria, device performance, sample sizes, expert ground truth establishment, adjudication methods, or comparative effectiveness studies of an AI-powered device.

This document is a regulatory submission for a physical dental material, not a software or AI-driven diagnostic device. Therefore, it does not address the specific points requested in your prompt related to AI/software performance evaluation.

To answer your request, I need a different type of document, specifically one that describes a study evaluating an AI or software device.

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• For direct restorations, for use in all classes of cavities, including veneers and incisal edge repairs.
• For indirect anterior and posterior restorations, including inlays, onlays, and veneers.

AXIOM is a light-cured hybrid dental restorative composite material, comprised of a Bis-GMA based resin, barium glass filler and fumed silica, suitable for dental filings. The cured product has a high surface luster, high strength and good wear resistance.

Here's a breakdown of the information you requested, based on the provided text:

This submission is for a dental restorative composite material, not an AI-powered diagnostic device. Therefore, many of the requested categories (such as sample sizes for test/training sets, expert ground truth, MRMC studies, standalone performance, etc.) are not applicable in this context. The provided text describes a 510(k) submission for a physical medical device (dental filling material) and focuses on demonstrating substantial equivalence to predicate devices through comparative bench testing and material safety.

Acceptance Criteria and Reported Device Performance

Key takeaway: The acceptance criteria for this device are based on demonstrating comparable physical and safety properties to existing, legally marketed predicate devices through specific bench tests.

Information Not Applicable to This Device (as it is not an AI/diagnostic device)

1. Sample size used for the test set and the data provenance: Not applicable. The "test set" in this context refers to samples of the material used in laboratory bench tests, not patient data for a diagnostic algorithm.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for a physical material's properties is established through standardized laboratory measurements, not expert review of images or data.
3. Adjudication method for the test set: Not applicable. Laboratory test results are objective measurements.
4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not applicable. This is for an AI diagnostic algorithm, not a dental filling material.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm.
6. The type of ground truth used: For the physical/safety tests, the ground truth is the objective measurement from the specified laboratory tests (e.g., a measured MPa for flexural strength). For claims of "similar to predicate," the ground truth is the established performance characteristics of the predicate devices.
7. The sample size for the training set: Not applicable. This is not an AI/machine learning device.
8. How the ground truth for the training set was established: Not applicable.

Summary of the 510(k) process for AXIOM:

The manufacturer, Tri Dental Innovators, submitted AXIOM, a light-cured hybrid dental restorative composite, for 510(k) clearance. Their primary method of demonstrating safety and effectiveness was through showing substantial equivalence to existing predicate devices (3M Z100 Restorative and Kerr Corporation Prodigy 4). This was achieved by:

• Component Comparison: Stating that all components of AXIOM are found in legally marketed predicate devices and share similar technological characteristics based on monomer chemistry.
• Comparative Bench Testing: Conducting specific laboratory tests (diametral strength, flexural strength, Young's modulus, shrinkage, and hardness) to show that AXIOM performs comparably to the predicate devices.
• Biocompatibility Testing: Demonstrating that AXIOM was non-cytotoxic and non-mutagenic.

The FDA reviewed this information and determined that AXIOM was substantially equivalent for its intended use (all classes of cavities), allowing it to be marketed.

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ALM S.A. Axcel™ Surgical Lights are intended to be used to provide visible illumination of the surgical area or the patient.

The Axcel™ Surgical Light is a new product designation intended to identify a family of surgical lights that will use a similar set of design principals as the PrismAlix® (PRX) Series Surgical Light. The primary predicate device focuses on the PRX4000, which is the smaller lighthead within the PRX Series. Additionally, the Axcel light design also uses some design features from the Angenieux AX4 Surgical Light. Comparisons are made between it and these two predicate devices.

The current Axcel™ Family of Products and Configurations are shown in Table 1 below, along with their associated FDA Product Code(s). There are three configurations and comprise of two (2) ceiling mounted light system configurations (single and two lighthead) and one (1) wall mount system with one lighthead.

This 510(k) summary for the ALM Axcel™ Surgical Light describes the device and its substantial equivalence to predicate devices, but it does not include information about specific acceptance criteria or an explicit study proving the device meets them, as it is understood in the context of performance metrics like sensitivity, specificity, or accuracy that are common in AI/ML medical devices.

Instead, the submission focuses on demonstrating compliance with safety standards and the substantial equivalence of modifications compared to existing predicate devices.

Therefore, the specific quantitative details requested in the prompt regarding acceptance criteria, performance metrics, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance are not available in the provided document.

However, I can extract information about the types of tests conducted and what the "acceptance criteria" can be inferred to be in this regulatory context.

Here's a breakdown of the available information based on your prompt:

1. A table of acceptance criteria and the reported device performance

Since this is a submission for a surgical light, the "acceptance criteria" are primarily related to safety, electrical standards, and functional equivalence to predicate devices, rather than performance metrics like sensitivity or specificity. The document doesn't provide a table of performance results against numerical acceptance criteria in the way an AI/ML device submission would.

Inferred "Acceptance Criteria" (based on standards compliance and substantial equivalence argument):

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 for Test Set: Not applicable in the context of this device and submission type. The testing was for compliance with engineering and electrical safety standards, not a statistical performance test against a "test set" of medical data.
• Data Provenance: Not applicable. The "data" here refers to test results from engineering and safety assessments, not clinical data.

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. Ground truth, in the sense of expert consensus on medical findings, is not relevant for a surgical light's safety and electrical compliance testing. The "ground truth" here is the adherence to engineering standards.

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

• Not applicable. This type of adjudication is for resolving discrepancies in expert interpretations of medical data, which is not part of this submission.

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

• No. An MRMC study is not relevant for a surgical light. This device is not an AI/ML diagnostic tool or an aid for human interpretation of medical cases.

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

• No. This device is a physical surgical light, not an algorithm. While it contains "Software... determined as a Minor Level of Concern," the submission does not detail standalone performance of this software in a clinical context. Its function is to control light intensity and on/off, not to make diagnostic or therapeutic decisions.

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

• The "ground truth" in this context is the established engineering and electrical safety standards (e.g., UL 2601-1, CSA C22.2 No. 601.1, IEC 60601-2-41, EN 60601-1-2) and FDA guidance for software. The device's performance is measured against these technical specifications and regulatory requirements.

8. The sample size for the training set

• Not applicable. This is not an AI/ML device that requires a training set of data.

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

• Not applicable.

Summary regarding "the study that proves the device meets the acceptance criteria":

The "study" proving the device meets the acceptance criteria, in this context, consists of nonclinical testing for conformance to recognized safety and electrical standards, along with a comparative analysis of its design and features against legally marketed predicate devices. The conclusion rests on demonstrating that the device is substantially equivalent to existing devices and adheres to relevant safety regulations. The formal "approval" is the FDA's 510(k) clearance letter.

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