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The X-spine 30 Pedicle Screw System is indicated for the treatment of severe spondylolisthesis (Grade 3 and 4) of the L5-S1 vertebra in skeletally mature patients receiving fusion by autogenous bone graft having implants attached to the lumbar and sacral spine (L3 to sacrum) with removal of the implants after the attainment of a solid fusion.

In addition, the X-spine®M Pedicle Screw System is intended for posterior, non-cervical (T1-S2/ilium) pedicle spinal fixation to provide immobilization of spinal segments in skeletally mature patients as an adjunct to fusion in the treatment of the following acute and chronic instabilities of the thoracic, lumbar and sacral spine: degenerative spondylolisthesis with objective evidence of neurological impairment, fracture, scoliosis, kyphosis, spinal tumor, and failed previous fusion (pseudarthrosis).

The X-spine™ Pedicle Screw System consists of pedicle screws, rods, cross bar connectors, and associated instruments. Various forms and sizes of these implants are available so that adaptations can be made to take into account the pathology and anatomy of an individual patient. The system components are manufactured from Titanium based alloy which complies with ASTM F136. Alternatively, rods are also offered manufactured from Cobalt Chromium alloy which complies with ASTM F1537. The single use only implants are provided non-sterile, and should not be reused under any circumstances.

This document is a 510(k) summary for the X-spine™ Pedicle Screw System, which is a medical device. This type of document is a premarket notification to the FDA to demonstrate that the device is substantially equivalent to a legally marketed predicate device, and thus does not require a full premarket approval application (PMA).

The information provided within this document is typical for a 510(k) submission, which focuses on comparing a new device to existing ones rather than detailing extensive clinical studies or acceptance criteria for independent device performance. Therefore, many of the requested details about acceptance criteria, specific study designs, sample sizes, and ground truth establishment, which are common in standalone clinical studies for novel AI/diagnostic devices, are not explicitly present in this type of regulatory submission.

Here's an attempt to answer your questions based on the provided text, highlighting where information is absent or inferred:

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

The document does not explicitly define "acceptance criteria" in the way a clinical trial for diagnostic performance would. Instead, it focuses on demonstrating substantial equivalence to predicate devices through technological characteristics and mechanical performance. The "performance criteria" are implicitly met if the device is found substantially equivalent.

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

• Sample Size for Test Set: Not applicable in this context. The "test set" here refers to the new device and its components, which underwent engineering evaluations and material testing, not a clinical patient cohort.
• Data Provenance: The document does not describe specific clinical data or patient data being used in a "test set." The evaluation is based on material specifications (ASTM standards) and engineering analysis, not patient-derived data from a specific country or study type.

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)

• Number of Experts & Qualifications: Not applicable. Ground truth, in the sense of expert consensus on diagnostic images or pathology, is not part of this submission. The "ground truth" for the material and mechanical evaluations would be the established material standards (e.g., ASTM) and engineering principles, applied by internal engineers and potentially reviewed by regulatory body experts.

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

• Adjudication Method: Not applicable. There is no "test set" in the sense of patient data requiring adjudication of clinical findings.

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

• MRMC Study: No. This is a submission for a spinal pedicle screw system, a physical implant, not an AI or diagnostic imaging device. Therefore, a MRMC study is not relevant.

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

• Standalone Performance: Not applicable. This is not an algorithm or AI device.

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

• Type of Ground Truth: The "ground truth" is based on:
  • Material Standards: Conformance to established ASTM standards (F136 for titanium alloy, F1537 for cobalt chrome alloy) for biocompatibility.
  • Engineering Principles: Evaluation of structural integrity and functional performance based on engineering analysis of the device's design modifications.
  • Predicate Device Performance: The underlying assumption that the predicate devices are safe and effective, and the new device is substantially equivalent based on these established technological characteristics.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. There is no "training set" in the context of an AI or machine learning model.

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

• Ground Truth for Training Set: Not applicable as there is no training set.

In summary: The provided document is a regulatory submission for a physical medical device (pedicle screw system) and primarily relies on demonstrating substantial equivalence to already cleared predicate devices through material properties and mechanical engineering evaluations, rather than clinical efficacy studies with patient data, AI performance assessments, or complex ground truth establishment methods typically associated with diagnostic or AI-powered devices.

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