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The Lateral Spine Truss System (LSTS) 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. All LSTS Interbody Fusion Devices 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. Interbodies with 18° lordosis or greater must be used with the 4WEB Lumbar Spine Truss System Plating Solution (LSTS-PS) with integrated fixation. If using the 1-hole 4WEB LSTS-PS with integrated fixation, additional supplemental fixation is required (e.g. posterior fixation). 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.

This document is a 510(k) summary for the 4WEB Lateral Spine Truss System (LSTS) Interbody Fusion Device. It focuses on the mechanical performance of the device rather than the performance of an AI/ML diagnostic algorithm. Therefore, the questions regarding acceptance criteria and studies for AI/ML performance metrics are not directly applicable to this submission.

However, I can extract the information related to the device's performance testing based on the provided text.

1. Table of Acceptance Criteria and Reported Device Performance

Specific tests performed to demonstrate compliance:

• Axial screw pushout per ASTM F543
• Static axial compression per ASTM F2077
• Static compression shear per ASTM F2077
• Dynamic axial compression fatigue per ASTM F2077
• Dynamic compression shear fatigue per ASTM F2077
• Expulsion testing
• MR Conditional testing

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 applicable. The device is a surgical implant, and its performance is evaluated through mechanical testing and finite element analysis, not through a test set with patient 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 is not applicable. Ground truth for a physical device's mechanical properties is established through adherence to engineering standards (ASTM) and scientific principles, not expert consensus on patient data.

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

This is not applicable. Mechanical testing does not involve adjudication.

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 is not applicable. This is a medical device (interbody fusion device), not an AI/ML diagnostic or assistive technology.

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

This is not applicable. This document is about a physical medical device, not an algorithm.

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

The "ground truth" for this device's performance is established by engineering standards (e.g., ASTM F543, ASTM F2077), which define the methods and criteria for evaluating mechanical properties like strength, fatigue, and expulsion. Finite element analysis (FEA) is also used to simulate mechanical behavior and establish "ground truth" in terms of predicted performance under various conditions, validated against established engineering principles.

8. The sample size for the training set

This is not applicable. The document discusses a physical medical device, not an AI/ML algorithm. Finite Element Analysis (FEA) is a computational method that doesn't involve a 'training set' in the machine learning sense.

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

This is not applicable. As stated above, this is about a physical device and not an AI/ML algorithm.

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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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