Search Results

The SurgTech Thoracolumbosacral (TLS) Posterior Fixation System is intended to provide pedicle and non-pedicle immobilization and stabilization of the posterior non-cervical spine (T1-S2/Ilium) in skeletally mature patients as an adjunct to fusion in the treatment of the following acute and chronic instabilities or deformities: degenerative disc disease (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies), spondylolisthesis, trauma (i.e., fracture or dislocation), spinal stenosis, curvatures (i.e., scoliosis, kyphosis and/or lordosis), tumor, pseudarthrosis, and/or failed previous fusion.

The SurgTech Thoracolumbosacral (TLS) Posterior Fixation System consists of longitudinal members (rods), anchors (hooks and screws), interconnections (cross connector) and fasteners in a variety of sizes to accommodate differing anatomic requirements.

This document describes a 510(k) premarket notification for the "SurgTech Thoracolumbosacral (TLS) Posterior Fixation System," a spinal implant, not an AI/ML powered device.

Therefore, the requested information about acceptance criteria and study design relevant to AI/ML device performance (e.g., sample size for test set, number of experts for ground truth, adjudication method, MRMC study, standalone performance, training set details) is not applicable and cannot be found within the provided text.

The document focuses on demonstrating substantial equivalence to predicate devices through mechanical testing and comparison of technological characteristics.

Here’s the information that is present regarding the device's performance and testing:

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

2. Sample size used for the test set and the data provenance:

• Not Applicable. The testing performed was mechanical testing of the device itself, not a clinical study on human subjects in the context of an AI/ML model's performance. The "test set" here refers to the device constructs subjected to mechanical stress. The number of constructs tested is not specified, but it's stated they were "worst-case" constructs.
• Provenance: Not specified beyond the tests being conducted on physical device constructs.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not Applicable. This is not an AI/ML diagnostic or predictive device requiring expert ground truth for its performance evaluation in that context. The "ground truth" for mechanical testing is based on established engineering standards (ASTM F1717) and the ability to withstand specified loads without failure, usually validated by engineers.

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

• Not Applicable. Adjudication methods are relevant for clinical assessments, particularly in AI studies where expert consensus on image interpretation or diagnoses is needed. This was mechanical 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:

• Not Applicable. This device is a spine implant, not an AI-assisted diagnostic tool. No MRMC study was performed.

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

• Not Applicable. This device is a physical implant, not an algorithm.

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

• For mechanical testing, the "ground truth" is defined by the acceptance criteria specified in the ASTM F1717 standard (mechanical performance thresholds and failure modes). The device's performance was compared to that of legally marketed predicate devices, implying that their performance under the same standard serves as a comparative benchmark.

8. The sample size for the training set:

• Not Applicable. There is no AI/ML model, hence no training set.

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

• Not Applicable. There is no AI/ML model, hence no training set ground truth.

In summary, the provided document describes a medical device (spinal fixation system) that received 510(k) clearance based on substantial equivalence demonstrated through mechanical testing against established standards and comparison to predicate devices, not through performance evaluation of an AI-powered diagnostic or therapeutic algorithm.

Ask a specific question about this device