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The VERTACONNECT TLIF cage is indicated for inter-vertebral body fusion of the lumbar spine, from L2 to S1, in skeletally mature patients who have had six months of non-operative treatment. The device is intended for use at either one level or two contiguous levels for the treatment of degenerative disc disease (DDD) with up to Grade I spondylolisthesis. DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies. The device is intended for use with supplemental fixation and is intended for use with autograft to facilitate fusion.

The VERTACONNECT TLIF Spreadable Lumbar Cage is a spacer for implantation in a prepared intervertebral disc space of the lumbar spine. Its design offers stable contact surfaces, toothed implant/bone surfaces and large cage windows. The open implant design supports a bony construction of the intervertebral disc space. The implants consist of a cage and a pre-assembled expansion element.

The provided text is a 510(k) Summary for a medical device (VERTACONNECT TLIF cage) and primarily focuses on demonstrating substantial equivalence to predicate devices, rather than an acceptance criterion and study proving device performance against that criterion for an AI/ML-enabled medical device.

The document explicitly states: "No clinical studies were performed" and the strength of the device was determined through non-clinical testing. The "acceptance criteria" and "study that proves the device meets the acceptance criteria" in the context of AI/ML are not applicable here because this is a traditional medical device (an intervertebral cage) that does not incorporate AI/ML.

Therefore, I cannot extract the information requested as it pertains to AI/ML device performance from this document. The document describes:

• Non-clinical testing: Axial compression, shear-compression, torsion, subsidence, expulsion testing, sterility validation, packaging validation, and biocompatibility testing.
• Comparison to predicate devices: The device is considered substantially equivalent to predicate devices based on intended use, design, materials, mechanical safety, and performance as demonstrated through non-clinical tests.

No information regarding AI/ML capabilities, test sets, expert ground truth, multi-reader multi-case studies, or training sets for an algorithm is present in this 510(k) summary.

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