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510(k) Data Aggregation
(117 days)
Membrane Tacks and Membrane Screws are intended for fastening membranes during bone regenerative treatment. These membrane fastening means are in direct body contact, intended for temporary use only. Membrane fastening means are submerged and clinically implanted more than 30 days with an expected duration of three to nine months or until bone regeneration is complete taking into account which membrane and bone grafting materials are being used.
Membrane Tacks and Membrane Screws are regenerative membrane fastening devices available in 3mm length. The Membrane Tack is used in conjunction with a Tack Positioning Instrument, while the Membrane Screw is applied with the Neoss Implant Inserter. Regenerative membranes are designed to prevent ingrowth of gingival soft tissue into bony defects, in order to facilitate the bone formation during the repair process of the defect. Membrane Tacks and Screws are made of titanium while related instruments are made in stainless steel. The Membrane fastening devices are single-use only while the instruments are for multiple use.
I am sorry, but the provided text does not contain information about the acceptance criteria and the study that proves a device meets the acceptance criteria in the context of AI or machine learning models.
The document is a 510(k) premarket notification for "Membrane Screws and Membrane Tacks," which are physical medical devices used in bone regenerative treatment. The content focuses on demonstrating substantial equivalence to predicate devices based on material composition, dimensions, intended use, and general performance characteristics (sterilization, shelf life, biocompatibility, instrument cleaning, and insertion/removal forces).
Therefore, I cannot provide the requested information, which includes details about:
- A table of acceptance criteria and reported device performance for an AI/ML device.
- Sample size and data provenance for test sets.
- Number and qualifications of experts for ground truth.
- Adjudication methods.
- MRMC studies and effect sizes.
- Standalone algorithm performance.
- Type of ground truth used (pathology, outcomes data, etc.).
- Training set sample size and ground truth establishment.
The document explicitly states that "Clinical data is not required to establish substantial equivalence in this submission," further indicating a lack of clinical study data relevant to AI/ML performance evaluation.
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