Search Results

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.

Ask a specific question about this device

The Reliance Lumbar IBF System is indicated for intervertebral body fusion of the spine in skeletally mature patients. The device systems are designed for use with autogenous bone graft to facilitate fusion. One device may be used per intervertebral space. The implants are intended to be used with legally cleared supplemental spinal fixation cleared for the implanted level. Additional supplemental fixation is not necessary for the Lumbar IBF-S if the integrated screws are implanted.

The Reliance Lumbar IBF System, when used as an Intervertebral Body Fusion device is also intended for use at either one level or two contiguous levels in the lumbar spine, from L2 to S1, for the treatment of degenerative disc disease (DDD) with up to Grade 1 spondylolisthesis. DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies. The lumbar device is to be used in patients who have had six months of non-operative treatment.

The Reliance Lumbar IBF System is comprised of lumbar intervertebral body fusion devices and associated surgical instruments. This submission adds a line extension, the Reliance Lumbar IBF-S devices, which consist of spacers made of PEEK Optima LT1 with Tantalum markers, and integrated screws, faceplates, and locking plates manufactured from titanium alloy.

The provided text describes a medical device, the Reliance Lumbar IBF System, and its regulatory clearance process (510(k)). It does not include details about acceptance criteria, device performance metrics, or any clinical studies involving human readers or AI algorithms. The "Performance Data and Substantial Equivalence" section only mentions mechanical testing performed on the device to establish substantial equivalence to predicate devices, focusing on physical properties rather than diagnostic performance.

Therefore, I cannot extract the requested information from the provided text as it is not present. The document focuses on regulatory approval based on substantial equivalence to existing medical devices, not on the diagnostic performance or AI-assisted interpretation of medical images.

Ask a specific question about this device