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SimpliSeal is a root canal sealer for the permanent sealing of root canals following established endodontic procedures and may be used in conjunction with the auxiliary materials in the root canal (i.e. gutta percha points). SimpliSeal is intended for use by qualified healthcare personnel trained in its use.

SimpliSeal is a root canal sealer delivered in a dual-barrel syringe. This epoxy based resin sealer is easy to use with gutta-percha points and can also be used with established and more recent root canal sealing techniques. SimpliSeal with colaonent obturation of root canals when combined with obturation points.

The provided text describes the "SimpliSeal" root canal sealer and its substantial equivalence to predicate devices, focusing on physical and biocompatibility tests. However, it does not contain a study that proves the device meets specific acceptance criteria in the context of AI or machine learning performance as requested. The document is a 510(k) summary for a medical device approval by the FDA, which relies on demonstrating equivalence to existing legally marketed devices, rather than a clinical study with detailed performance metrics against specific acceptance criteria for AI.

Therefore, many of the requested fields regarding AI/ML study design, sample sizes, ground truth establishment, expert involvement, and comparative effectiveness studies are not applicable to this document.

Here's a breakdown of what can be extracted and what information is missing based on the provided text:

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

The document lists various tests performed on SimpliSeal and states that "All testing is within specification and the device performs as designed." It also compares SimpliSeal's physical properties to predicate devices (Adseal and AH-Plus) implying these shared properties serve as the performance benchmarks. However, the specific quantitative acceptance criteria (e.g., "fluidity must be X-Y mm") are not explicitly stated within the text, only the type of test (e.g., "ISO 6876 fluidity"). The reported performance is a qualitative statement of meeting specifications rather than specific numerical results.

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

This information is not provided in the document. The document describes laboratory tests, not a clinical study involving a test set of data in the context of AI.

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)

This information is not provided as the device is not an AI/ML diagnostic tool, and the "ground truth" for its performance is based on standardized laboratory testing rather than expert-labeled clinical data.

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

This information is not provided as the device is not an AI/ML diagnostic tool, and no multi-reader adjudication process is mentioned for its performance assessment.

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

This information is not provided. The device is a root canal sealer, not an AI-assisted diagnostic tool, so an MRMC study is not relevant.

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

This information is not applicable. The device is a physical product (root canal sealer), not an algorithm.

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

The "ground truth" for this device's performance is established by standardized laboratory test methods (ISO 6876 and ISO 10993 series). The performance is assessed against the specifications outlined in these international standards for dental materials and biocompatibility.

8. The sample size for the training set

This information is not provided and is not applicable as the device is not an AI/ML model.

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

This information is not applicable as the device is not an AI/ML model.

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