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When used as a vertebral body replacement, BASIS™ Vertebral Body Spacers are intended to be used in partial corpectomy procedures to aid in surgical correction and stabilization of the spine. The device is indicated for use in the thoracolumbar spine (T1-L5) to replace and restore height of a resected vertebral body or portion thereof, excised for the treatment of tumor or trauma (i.e., fracture). BASIS™ Vertebral Body Spacers must be used with supplemental fixation. Additionally, BASIS™ Vertebral Body Spacers are intended to be used with bone graft.

The BASIS™ Spinal System components included in this submission consist of various lengths and widths of vertebral body spacers as well as ancillary instrument sets. The BASIS™ Device is a spacer that inserts between vertebral bodies in the anterior thoracic and lumbar spine. The BASIS™ Spinal System implant components are made from titanium alloy. BASIS™ Vertebral Body Spacers must be used with additional anterior and/or posterior spinal instrumentation to augment stability.

The provided text is a 510(k) summary for the BASIS™ Spinal System – Vertebral Body Spacers. It describes a medical device and its intended use, but it does not contain information about acceptance criteria or a study proving the device meets those criteria in the context of an AI/ML-based device.

The document is a premarket notification for a physical medical implant (vertebral body spacers made of titanium alloy) and focuses on demonstrating substantial equivalence to a legally marketed predicate device (HOURGLASS™ VBS K033926).

Therefore, I cannot provide the requested information from the given text as it is not relevant to AI/ML device testing and validation.

Here's why the requested information cannot be extracted from the provided text:

• Device Type: The BASIS™ Spinal System is a physical implant (vertebral body spacer), not an AI/ML-based diagnostic or assistive device.
• Approval Pathway: The document describes a 510(k) clearance based on "substantial equivalence" to a predicate device, a pathway that typically focuses on material safety, mechanical properties, and comparability to existing devices, rather than performance metrics for an AI algorithm.
• Lack of Study Details: There is no mention of accuracy, sensitivity, specificity, or other performance metrics, nor details about a test set, ground truth, experts, or statistical methodologies commonly associated with AI/ML device studies.
• No "AI" or "Algorithm" Related Terminology: The document does not use any terms related to artificial intelligence, machine learning, algorithms, or software performance evaluation.

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