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The AnyCheck IMT-100 is indicated for use in measuring the stability of implants in the oral cavity and maxillofacial region.

The AnyCheck IMT-100 Dental Implant Mobility Measuring Instrument is a system designed to measure dental implant mobility (stability) in the oral cavity and maxillofacial region. The AnyCheck IMT-100 is a portable, handheld instrument to observe non-invasively the fixation stability of an implant at alveolar bone. The implant stability test (iST) is performed using vibration generated by mechanical contacting of a tapping rod. The AnyCheck IMT-100 numerically indicates a scale result of iST. The higher number means better stability of the target implant.

The provided text describes the 510(k) submission for the AnyCheck IMT-100 Dental Implant Mobility Measuring Instrument. However, it explicitly states that "This submission does not include clinical performance data, similar to the predicate devices." Therefore, information regarding acceptance criteria and a study proving the device meets these criteria in the context of clinical performance (e.g., impact on human readers, expert ground truth, multi-reader studies) is not available within this document.

The document focuses on non-clinical tests to demonstrate substantial equivalence to predicate devices. Here's a breakdown of the relevant information provided:

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

Since no clinical performance data is presented, a table of acceptance criteria for clinical performance and reported device performance in a clinical setting cannot be created from this text.

However, the text mentions non-clinical tests were performed against the company's specified design requirements. The generalized "acceptance criteria" for these non-clinical tests would be:

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

• Test set sample size: Not applicable for clinical performance study as none was conducted. For non-clinical tests, specific sample sizes are not detailed, but it's implied that sufficient samples were used for the various engineering and biocompatibility tests.
• Data provenance: Not applicable for clinical data. For non-clinical tests, the tests were conducted by a certified laboratory (in compliance with ISO standards), implying laboratory-based testing rather than patient data.

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

Not applicable, as no clinical study with human expert review was conducted.

4. Adjudication method for the test set:

Not applicable, as no clinical study with human expert review was conducted.

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 MRMC study was conducted. The device is a "Dental Implant Mobility Measuring Instrument" and not an AI-assisted diagnostic tool that would typically involve human readers.

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

The device itself is a measurement instrument, not an algorithm in the sense of AI software. Its "standalone performance" refers to its ability to accurately measure implant stability. The non-clinical tests (measurement accuracy, output scale, reproducibility) would constitute a form of standalone performance evaluation for its intended function.

7. The type of ground truth used:

• Clinical: Not applicable.
• Non-clinical: The ground truth for the non-clinical tests would have been established by calibrated laboratory standards and established engineering and biocompatibility test protocols (e.g., known electrical safety limits, recognized cytotoxicity assays, precisely manufactured test objects for accuracy/reproducibility).

8. The sample size for the training set:

Not applicable. This device is a physical measuring instrument, not a machine learning model that requires a training set.

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

Not applicable, as there is no training set for a machine learning model.

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