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The ResCUBE System is a temporary implant for use in orthopedic surgery. The system is intended to provide temporary stabilization as a bone anchor during the development of solid bony fusion and in the repair of bone fractures. The indications for use include the following applications:

1. Spinal trauma surgery, that can be treated with sublaminar, interspinous or facet wiring techniques.
2. Spinal reconstructive surgery, incorporated into constructs for the purpose of correction of spinal deformities such as scoliosis, kyphosis and spondylolisthesis.
3. Spinal degenerative surgery, as an adjunct to spinal fusions.

The ResCUBE™ Ligament Fixation System (ResCUBE System) is a temporary spinal implant intended to provide temporary stabilization as a bone anchor during the development of solid bony fusion and aid in the repair of bone fractures. The ResCUBE System consists of the following components: a ligament allowing correction and stabilization of the levels, after tensioning; a malleable leader allowing guidance of the ligament during surgery and attached to the ligament by a PET sheath; and a low-profile connector holding the tension on the ligament.

The provided text is a 510(k) Summary for the ResCUBE™ Ligament Fixation System. It details information typically provided to the FDA for medical device clearance. However, it does not contain the specific type of acceptance criteria and study data related to AI/algorithm performance that you are asking for.

The document discusses performance testing for the ResCUBE System, but this refers to physical bench testing of the device's mechanical properties, not the performance of an AI or algorithmic component.

Therefore, most of the information requested in your prompt regarding acceptance criteria for AI, sample sizes for test/training sets, expert ground truth, adjudication methods, MRMC studies, or standalone algorithm performance, cannot be extracted from this document, as it appears the ResCUBE™ Ligament Fixation System is a physical medical implant, not an AI-powered device.

Here's what can be extracted based on the document's content:

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

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

This information is not provided in the document as the testing refers to physical device performance and not data-driven AI performance.

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)

This information is not provided as the ground truth for physical device testing is typically based on engineering specifications and direct measurements, not expert consensus on interpretations of data.

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

This information is not provided. This concept generally applies to human review of data, which is not the focus of this device's performance testing.

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

A MRMC study was not done, as this device is a physical implant and does not involve AI assistance for human readers.

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

This information is not applicable as the device is not an algorithm.

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

The ground truth for the performance testing mentioned (static and dynamic tension testing) is likely based on engineering specifications and direct physical measurements against established standards (IEC 62366: 2007, ISO 14630: 2012) and the manufacturer's own predetermined specifications.

8. The sample size for the training set

This information is not applicable as there is no training set for a physical implant device without AI components.

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

This information is not applicable as there is no training set for a physical implant device without AI components.

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