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510(k) Data Aggregation
(248 days)
Harvest Conceal ZR Block Out
Harvest Conceal ZR Block Out is indicated for burn-in zirconia shading. The applied liquid is sintered with the zirconia to adjust the restoration to match the natural color of the patient's teeth. The material is intended for professional dental work only.
Harvest Conceal ZR Block Out is a water-based opacifying solution available in 5 shades. white, A1, B1, C1 and D2, for use with a translucent zirconia core. It is indicated for burn-in zirconia shading. The applied liquid is sintered with the zirconia to adjust the restoration to match the natural color of the patient's teeth. Fabrication using the Harvest Conceal ZR Block Out requires an appropriate drying oven and sintering furnace.
The provided text describes a medical device, "Harvest Conceal ZR Block Out," and its substantial equivalence determination by the FDA. However, the document does not contain the specific information requested in the prompt regarding acceptance criteria, study details, sample sizes, expert qualifications, or ground truth establishment relevant to AI/ML device performance.
The document states that:
- No human clinical testing was conducted. This implies there would be no data on multi-reader multi-case studies or effect sizes of human readers with AI assistance.
- The device is a "Liquid Stain for Dental Zirconia Restorations," which suggests it is a material used in the fabrication of dental products, not a diagnostic or prognostic AI/ML device.
- Performance testing was "Bench Testing" for physical characteristics, and "Biocompatibility Testing" was conducted in accordance with ISO standards.
Therefore, I cannot extract the requested information such as a table of acceptance criteria and reported device performance, sample sizes for test sets, number of experts, adjudication methods, MRMC studies, standalone performance, or details about training sets and their ground truth, because this information is not present in the provided text. The document is primarily focused on the device's substantial equivalence to a predicate device based on material properties and intended use, not on the performance of an AI/ML algorithm.
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