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The NuVasive CoRoent XL-F System is indicated for intervertebral body fusion of the spine in skeletally mature patients. The System is designed for use with autogenous bone graft to facilitate fusion.

The CoRoent XL-F System is intended for use at either one level or two contiguous levels in the lumbar spine, from L2 to L5, 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 lumbar devices are to be used in patients who have had at least six months of non-operative treatment. The system is intended to be used with supplemental internal spinal fixation systems (e.g., pedicle or facet screws) that are cleared by the FDA for use in the lumbar spine in addition to the integrated screws.

The NuVasive CoRoent XL-F System is manufactured from PEEK-Optima® LT-1 conforming to ASTM F2026, MP35N conforming to ASTM F562, and titanium alloy conforming to ASTM F136 and ISO 5832-3. The implants are available in a variety of different shapes and sizes to suit the individual pathology and anatomical conditions of the patient.

The provided text describes a 510(k) premarket notification for the NuVasive CoRoent XL-F System, which is an intervertebral body fusion device. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving that the device meets specific acceptance criteria through a study with performance metrics in the typical sense of a novel medical AI/software device.

Therefore, the requested information elements related to performance criteria, sample sizes, ground truth establishment, expert involvement, and MRMC studies are not applicable to this document. The submission relies on non-clinical performance data (engineering analyses and cadaveric study) to demonstrate safety and effectiveness relative to existing devices.

Here's a breakdown of what can be extracted and why other elements are not applicable:

1. A table of acceptance criteria and the reported device performance

• Not Applicable in the traditional sense. For a 510(k) for an intervertebral body fusion device, "acceptance criteria" are typically related to meeting established performance standards for mechanical properties and biocompatibility, as demonstrated through engineering analyses and material testing, rather than clinical performance metrics (like sensitivity, specificity, accuracy) for an AI/software device. The document states that the objective was to demonstrate substantial equivalence to predicate devices.
• Reported Device Performance (based on non-clinical studies):
  • Axial Compression Finite Element Analysis: "Results demonstrate that the subject CoRoent XL-F System presents no new worst-case for performance testing."
  • Compression Shear Finite Element Analysis: "Results demonstrate that the subject CoRoent XL-F System presents no new worst-case for performance testing."
  • Wear Debris Analysis: No specific performance reported, but implied to be acceptable for substantial equivalence.
  • Subsidence Analysis: No specific performance reported, but implied to be acceptable for substantial equivalence.
  • Clinical Literature Analysis: Used for comparison, not direct performance measurement of the device itself.
  • Cadaveric Study: "Did not identify any new risks associated with the subject device."

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not Applicable. No test set in the context of an AI/software device performance study was used. The studies conducted were engineering analyses (Finite Element Analysis) and a cadaveric study.
  • For the cadaveric study, the sample size is not specified in the provided text.
  • Data provenance for FEA is theoretical modeling; for the cadaveric study, the source/provenance is not detailed.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not Applicable. This device is a physical implant, not an AI/software device that requires human expert annotation for ground truth establishment. Engineering analyses and cadaveric studies are evaluated by engineers and medical professionals specialized in those fields, but not in the context of "ground truth" for a test set.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not Applicable. There was no "test set" in the context of a diagnostic or predictive performance study, and therefore no adjudication method as described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• Not Applicable. This is a physical intervertebral body fusion device, not an AI or imaging diagnostic tool. Therefore, an MRMC study is irrelevant.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Not Applicable. This device does not contain software or electrical equipment, as explicitly stated in section F. It is a physical implant.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Not Applicable in the typical sense of AI/software device. For this physical device, the "ground truth" for its safety and effectiveness is established through adherence to engineering standards (e.g., ASTM F2026, F562, F136, ISO 5832-3 for material composition), and the findings of the finite element analyses and cadaveric study which confirm it performs similarly to predicate devices without introducing new risks. Clinical literature analysis also contributes to supporting the established safety and effectiveness of similar devices.

8. The sample size for the training set

• Not Applicable. This device does not involve a "training set" in the context of machine learning.

9. How the ground truth for the training set was established

• Not Applicable. As there is no training set for machine learning involved with this device.

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