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Permanent obturation of the root canal following vital pulp-extirpation.
Permanent obturation of the root canal following removal of infected or necrotic pulp and placement of intracanal dressings.
Injectable Root Canal Bioceramic Sealer (nRoot SP) is suitable for use in the single cone and lateral condensation technique.

Injectable Root Canal Bioceramic Sealer (nRoot SP) is a convenient premixed ready-to-use injectable white hydraulic cement paste developed for permanent root canal filling and sealing applications. nRoot SP is an insoluble, radiopaque on a calcium silicate composition, which requires the presence of water to set and harden. nRoot SP does not shrink during setting and demonstrates excellent physical properties.

nRoot SP is packaged in a preloaded syringe and is supplied with disposable Intra Canal tips. nRoot SP may be delivered into the canal via the disposable tips or it can be delivered via traditional methods. Injectable Root Canal Bioceramic Sealer (nRoot SP) is available in five preloaded syringe mode that provide different in specification in 5g,3g,2g,1g and 0.5g. The only difference between the types are the net weight.

The performance of the Injectable Root Canal Bioceramic Sealer (nRoot SP) conforms to ISO 6876:2012 Dentistry - Root canal sealing materials.

The provided document is a 510(k) clearance letter from the FDA for a medical device called "Injectable Root Canal Bioceramic Sealer (nRoot SP)". This document primarily focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing. It explicitly states that "No clinical study is included in this submission."

Therefore, based only on the provided text, it is not possible to describe the acceptance criteria and a study that proves the device meets those criteria from the perspective of an AI or human-in-the-loop performance evaluation. The device in question is a dental material, not an AI or imaging device that would typically undergo such a study.

The questions you've asked (about sample size, data provenance, experts, adjudication, MRMC studies, standalone performance, ground truth, and training sets) are highly relevant for AI/ML-based medical devices or devices that require human interpretation of data for their function.

Given the content of the provided 510(k) clearance letter, I cannot fulfill your request for details regarding AI/ML-specific acceptance criteria and study designs because the device is a dental material, and the provided document explicitly states no clinical studies were performed.

If you intended to provide a different document related to an AI/ML medical device, please provide that document.

However, to directly address the intent of your questions, and assuming hypothetically that such a device (an AI/ML device) were being submitted and these details would be present in its submission, here's what could be inferred about the type of information that would be necessary to answer your questions, based on typical regulatory submissions for AI/ML devices:

Hypothetical General Information for an AI/ML Medical Device (Not Applicable to the Provided Document)

• Device Type: This type of information (acceptance criteria, test sets, experts, MRMC, etc.) is typically provided for AI/ML-based diagnostic or assistive devices, especially those that analyze medical images (e.g., AI for detecting pneumonia on X-rays, or AI for stroke detection on CT scans).
• Regulatory Pathway: For such devices, a 510(k) submission would typically include a detailed performance study section.

If this were an AI/ML device, the answers to your questions would typically be structured as follows (but again, this is not information present in the provided document):

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

   • Acceptance Criteria Examples (for an AI/ML device): Sensitivity > X%, Specificity > Y%, AUC > Z, F1 score > A, meeting non-inferiority/superiority to a predicate/human experts on certain metrics, etc.
   • Reported Device Performance: The actual measured metrics from the validation study.

2. Sample sized used for the test set and the data provenance:

   • Sample Size: e.g., N=1000 cases.
   • Data Provenance: e.g., Multi-center, retrospective study from hospitals in the US, UK, and Germany. Or, prospective study collected from 3 different clinics in China.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Number of Experts: e.g., 3 independent board-certified radiologists.
   • Qualifications: e.g., All were board-certified radiologists with at least 5 years of experience in [relevant sub-specialty], specializing in [medical condition] diagnosis.

4. Adjudication method for the test set:

   • Method: e.g., 2+1 (two experts independently read, a third adjudicates disagreements). Or, consensus panel of 3 experts, or majority vote.

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:

   • MRMC study: Yes/No.
   • Effect Size: e.g., "Human readers improved their diagnostic accuracy by an average of 15% (p

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