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MagnetOs Granules is an implant intended to fill bony voids or gaps of the skeletal system, i.e., the extremities, pelvis and posterolateral spine. MagnetOs Granules may be used standalone or mixed with autograft, blood, and/or bone marrow. These osseous defects may be surgically created or the result of traumatic injury to the bone and are not intrinsic to the stability of the bony structure. MagnetOs Granules resorbs and is replaced with bone during the healing process.

MagnetOs Granules is a synthetic, resorbable, osteoconductive bone void filler for the repair of bony defects. MagnetOs Granules consists of 65-75% tri-calcium phosphate (TCP -Ca3(PO4)2) and 25-35% hydroxyapatite (HA - Ca10(PO4)c(OH)2) granules with a porous trabecular structure that resembles the interconnected porosity of human cancellous bone. MagnetOs Granules guide the three-dimensional regeneration of bone in the defect site into which it is implanted. New bone will be deposited on the surface of the graft when placed next to viable host bone. The graft resorbs and is replaced by bone during the natural process of bone remodeling. MagnetOs Granules is a ready-to-use product. MagnetOs Granules is provided in vials in a range of product volumes. MagnetOs Granules is gamma-sterilized and sterile packaged for single use only.

The provided text details the 510(k) summary for MagnetOs Granules, but it does not contain the specific information required to answer the prompt regarding acceptance criteria and a study proving a device meets these criteria in the context of an AI/ML medical device.

The document describes a bone void filler and its clinical evaluation (a randomized non-inferiority trial for posterolateral fusion), along with bench testing for material properties. This is a traditional medical device submission, not an AI/ML device.

Therefore, I cannot populate the table or answer most of the questions as the information is not present in the provided text. The document does not discuss:

• A table of acceptance criteria and reported device performance for an AI/ML model.
• Sample sizes used for a test set in an AI/ML context.
• Data provenance (e.g., country of origin, retrospective/prospective) for AI/ML data.
• Number of experts, their qualifications, or adjudication methods for ground truth in an AI/ML study.
• Multi-reader multi-case (MRMC) comparative effectiveness studies for AI/ML.
• Standalone performance of an algorithm.
• Training set details for an AI/ML model.

The text focuses on the substantial equivalence of a physical bone void filler device to predicate devices based on material properties, manufacturing, and clinical performance in a human trial without AI assistance.

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