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The FUSE Cage is indicated for intervertebral body spinal fusion procedures in skeletally mature patients with degenerative disc disease (DDD) at one or two contiguous levels from L2-S1. DDD is defined as discogenic back pain with degeneration of the disc confirmed by patient history and radiographic studies. These DDD patients may also have up to Grade I spondylolisthesis or retrolisthesis at the involved level(s). FUSE implants are to be used with autogenous bone graft and implanted via an open posterior approach. The FUSE Cage is to be used with supplemental fixation. Patients should have at least six (6) months of non-operative treatment prior to treatment with an intervertebral cage.

The FUSE is rectangular in shape and it has a honeycomb structure thru the implant in the superior/inferior direction. There is an oval opening in the M/L direction. There is a roughened surface on the superior and inferior surfaces. There are neutral, 4° and 8° lordosis configurations. The posterior end has a threaded hole for attaching insertion instruments, while the other end is solid and tapered.

The provided text describes a medical device, the AMT FUSE Cage, and its 510(k) summary for FDA clearance. However, it does not contain any information about acceptance criteria, specific device performance metrics, or any study design that would "prove" the device meets such criteria in the context of AI/machine learning or diagnostic accuracy.

The information provided is typical for a 510(k) submission for a non-active implantable medical device, focusing on substantial equivalence to a predicate device based on design, materials, function, and mechanical testing.

Therefore, I cannot populate the requested table or answer most of the questions, as the source document does not contain this type of information.

Here's what I can extract and explain:

Summary of Information from the Provided Text:

The document is a 510(k) premarket notification for the AMT FUSE Cage, a device intended for intervertebral body spinal fusion. The FDA determined the device is substantially equivalent to legally marketed predicate devices.

Missing Information:

The provided text does not contain any information related to:

• Acceptance criteria for performance metrics (e.g., sensitivity, specificity, accuracy).
• Reported device performance (in terms of diagnostic accuracy or AI performance).
• Sample size used for a test set or training set.
• Data provenance (country of origin, retrospective/prospective).
• Number of experts or their qualifications for ground truth.
• Adjudication methods.
• Multi-reader multi-case (MRMC) comparative effectiveness studies.
• Standalone algorithm performance studies.
• Type of ground truth used (pathology, outcomes data, expert consensus).
• How ground truth for a training set was established.

This is because the device described is a physical implant (a spinal cage), not an AI/ML-driven diagnostic or image analysis tool. The "study" mentioned refers to non-clinical mechanical tests, not clinical performance or diagnostic accuracy.

What is included (and why the requested AI/ML-related questions are not applicable):

The document states:

• Device Name: FUSE Cages
• Intended Use: Intervertebral body spinal fusion procedures in skeletally mature patients with degenerative disc disease (DDD) at one or two contiguous levels from L2-S1. To be used with autogenous bone graft and supplemental fixation via an open posterior approach, after 6 months of non-operative treatment.
• Material: Commercially pure titanium.
• Function: Posterior stabilization of the lumbar spinal column with the technique of Posterior Lumbar Interbody Fusion (PLIF).
• Comparison to Predicate: "The AMT FUSE Cage has the same indications and material, and similar designs as previously cleared devices." (Predicate device: WAVE (K080401)).
• Nonclinical Tests: "Tests performed according to ASTM F2077/F2267 indicate that the AMT FUSE Cage meets required mechanical strengths." These are mechanical strength tests relevant to the physical stability and integrity of the implant, not performance related to AI or diagnostic accuracy.

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