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The Posterior Spine Truss System (PSTS) Interbody Fusion Device is indicated for use 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. Patients should have received 6 months of non-operative treatment prior to treatment with the devices. The device must be used with supplemental fixation and must be used with autograft and/or allogenic bone graft comprised of cancellous and/or corticocancellous bone graft. These DDD patients may also have up to Grade I spondylolisthesis or retrolisthesis at the involved level(s)

The device is an open architecture truss design mathematically formulated to provide structural support with open space throughout the implant for bone growth and fusion. The 4WEB additive manufacturing process provides a hierarchical surface roughness. The implant is made from Ti6Al4V alloy. The device is available in a variety of sizes and lordotic angles to accommodate the patient's anatomy.

The provided FDA 510(k) K171351 document describes a medical device, the Posterior Spine Truss System (PSTS) Interbody Fusion Device, but does not contain information about the acceptance criteria or a study proving that the device meets those criteria in the context of AI/ML or diagnostic performance.

This document is a marketing clearance notification for a physical medical implant, not a software device or an AI/ML diagnostic tool. Therefore, the questions related to sample size for test/training sets, experts, ground truth, MRMC studies, or standalone algorithm performance are not applicable to this submission.

The "Performance Standards" section refers to mechanical and material performance testing for the implant itself, not diagnostic accuracy or AI performance. The tests listed (ASTM F2077, F2267, F2119, F2052, F2213, F2182) are standard tests for intervertebral body fusion devices, assessing physical properties like static/dynamic compression, shear, torsion, subsidence, and MR compatibility. These are not related to diagnostic accuracy or AI performance metrics.

In summary, this document does not provide the information requested regarding acceptance criteria and studies for an AI/ML-driven device.

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The Lateral Spine Truss System (STS) is indicated for use in skeletally mature patients with Degenerative Disc Disease (DDD) at one or two contiguous levels from L2-L5. DDD is defined as discogenic back pain with degeneration of the disc confirmed by patient history and radiographic studies. Patients should have received 6 months of non-operative treatment prior to treatment with the devices. The device must be used with supplemental fixation and must be used with autograft bone. These DDD patients may also have up to Grade I spondylolisthesis or retrolisthesis at the involved level(s).

The device is an open architecture truss design mathematically formulated to provide structural support with open space throughout the implant for bone growth and fusion. The device is available in a variety of sizes to accommodate the patient's anatomy. The implant is made from Ti6Al4V alloy (ASTM F-1108).

This document refers to a 510(k) premarket notification for a medical device called the "LATERAL Spine Truss System." It is a regulatory submission, not a study report, and therefore does not contain details about specific acceptance criteria or a study that "proves" the device meets acceptance criteria in the way a clinical trial or performance study would.

Instead, the document details the regulatory process by which the device was found substantially equivalent to existing devices. Substantial equivalence in the FDA 510(k) pathway means proving that a new device is as safe and effective as a legally marketed predicate device. This is often demonstrated through comparative analysis of technological characteristics, materials, indications for use, and sometimes non-clinical performance testing.

Here's an attempt to answer your questions based on the provided text, while also highlighting what information isn't present:

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

The document does not provide a table of acceptance criteria in the typical sense (e.g., target accuracy, sensitivity, specificity values) for a diagnostic AI device, nor does it present "reported device performance" in terms of clinical outcomes or diagnostic metrics. This is a spinal implant, and the "performance" discussed is in terms of mechanical testing and equivalence to predicates.

Here's what can be inferred for mechanical testing:

2. Sample size 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. The document describes mechanical testing, not a clinical study involving a "test set" of patient data in the context of AI. The "test set" would typically refer to a dataset used to evaluate an AI model's performance on unseen data.

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. This document is for a spinal implant, and the assessment of its substantial equivalence relies on mechanical testing and comparison to predicate devices, not on expert ground truth labeling of medical images or diagnostic classification.

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

This information is not provided. This concept is relevant for studies involving human readers or AI in diagnostic tasks, where disagreements in labels or interpretations might need an adjudication process. It does not apply to the mechanical testing 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

This information is not provided. MRMC studies are used to evaluate diagnostic systems, especially AI-assisted ones, and compare them against human performance. This document is for a physical orthopedic 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 provided. The device is a physical implant, not an algorithm. Therefore, "standalone" algorithm performance is not applicable.

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

For the mechanical testing, the "ground truth" implicitly refers to established engineering standards and measurements (e.g., ASTM F2267-04) and the performance characteristics of the predicate devices. The goal is to show the new device performs equivalently or acceptably according to these engineering benchmarks, rather than clinical ground truth like pathology or outcomes data in the context of an AI study.

8. The sample size for the training set

This information is not provided. The concept of a "training set" applies to machine learning algorithms. This document is for a physical medical device.

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

This information is not provided as it's not applicable. There is no "training set" in the context of this regulatory submission for an orthopedic implant.

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