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Used for all crown, bridge, and orthodontic impression techniques.

The Impression Material is a kind of addition-cure rubber impression material composed of vinyl polysiloxane and various fillers, with neutral smell and applicable to impression in dentistry. It consists of base and catalyst, in which putty base contains Vinyl polysiloxane, Methylhydrogensiloxane dimethylsiloxane, dimeticone, white oil and silicon. Catalyst mainly contains vinyl polysiloxane, platinum catalyst, dimeticone, white oil and silicon. The product is provided non-sterile.

Based on the provided text, the document is a 510(k) summary for a dental "Impression Material" and does not describe an AI/ML-based device. Therefore, many of the requested criteria (e.g., sample size for test/training sets, number of experts for ground truth, MRMC studies, human-in-the-loop performance, etc.) are not applicable to this submission.

The document discusses the substantial equivalence of the "Impression Material" to a predicate device based on non-clinical tests and material properties.

Here's an analysis of the provided information, noting where the requested criteria are not applicable:

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

The relevant information is in "Table 2 Performance parameter" on page 5.

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 explicitly stated for each test, but standard material testing typically involves a set number of samples for each specific property evaluation. This is a material, not a "device" in the AI/ML sense, so "test set" does not refer to a data cohort.
• Data Provenance: The tests are non-clinical, likely conducted in a laboratory setting. The manufacturer is "Zhengzhou Huaer Electro Optics Technology Co., Ltd" in China, so the testing was presumably performed there or by an outsourced lab. Given it's material testing, it would be "prospective" in the sense that samples were prepared and tested to demonstrate compliance.

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

• Not Applicable. This is not an AI/ML device requiring expert ground truth for image interpretation or diagnosis. The "ground truth" for material properties is established by adherence to defined ISO standards (ISO 4823 for Dentistry - Elastomeric impression materials, and ISO 10993 for biocompatibility), which specify the test methodologies and acceptable ranges.

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

• Not Applicable. As explained above, this is material property testing, not an AI/ML diagnostic or image-analysis device.

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

• Not Applicable. This is not an AI/ML device. No MRMC study was performed. The submission explicitly states: "No clinical study is included in this submission." (Page 4, Section G)

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

• Not Applicable. Not an AI/ML device.

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

• The ground truth for this device is based on established quantifiable physical and biological properties as defined by International Organization for Standardization (ISO) standards:
  • ISO 4823 Dentistry - Elastomeric impression materials for physical properties.
  • ISO 10993 Biological Evaluation of Medical Devices (Parts 5, 10, 11) for biocompatibility.
• The "acceptance criteria" are derived directly from these standards, meaning the material must perform within the specified ranges to be considered acceptable for its intended use.

8. The sample size for the training set

• Not Applicable. This is not an AI/ML device, so there is no "training set."

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

• Not Applicable. As there is no training set for an AI/ML model, there is no ground truth establishment method for it. The product's compliance is shown through non-clinical testing against industry standards.

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