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The Cervical Spine Truss System (CSTS) Interbody Fusion Device is indicated for use in skeletally mature patients with Degenerative Disc Disease (DDD) of the cervical spine at one level or two contiguous disc levels. DDD is defined as discogenic back pain with degeneration of the disc confirmed by patient history and radiographic studies. CSTS Interbody Fusion Devices are used as an adjunct to fusion in the cervical spine and are placed via an anterior approach at the C2 to T1 disc levels using autograft and/or allogenic bone graft comprised of cancellous and/or corticocancellous bone graft. Patients should have received 6 weeks of non-operative treatment prior to treatment with the devices. If the device is being used without the CSTS Integrated Plate, supplemental fixation must be used.

The device is an open architecture truss design mathematically formulated to provide structural support with open space throughout the implant for bone through 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 heights, footprints, and lordotic angles. The CSTS Integrated Plates come in multiple heights and orientations to accommodate the patient's anatomy. Screws are inserted through the anterior portion of the Integrated Plates into adjacent vertebral bodies for bony fixation. The Integrated Plates have a rotating locking tab to prevent back-out of the screws.

This FDA 510(k) clearance letter for the Cervical Spine Truss System (CSTS) is for a physical medical device (an intervertebral body fusion device), not an AI/software device. Therefore, the information typically required to describe the acceptance criteria and study proving an AI/software device's performance (as outlined in the prompt's requested information points) is not present in this document.

The document discusses the mechanical and safety performance of the physical device and its substantial equivalence to a predicate device, but it does not contain details about:

• Acceptance criteria for an algorithm's performance
• Sample sizes for data used in AI model testing
• Expert involvement in ground truth establishment for AI
• Adjudication methods for AI test sets
• MRMC studies for AI assistance
• Standalone AI performance
• Training set details for AI

The 510(k) summary explains that the purpose of this specific submission (K251117) is to "add additional configurations to the integrated plate product offering and to provide the integrated plate as sterile packaged." It then states that "The modifications made to the CSTS Integrated Plate for this submission did not create a new worst-case condition for any mechanical or MR safety tests. The results of the predicate testing are applicable to the subject devices and no further testing was required."

In summary, based solely on the provided FDA 510(k) clearance letter for the Cervical Spine Truss System (CSTS), it is not possible to answer the detailed questions about acceptance criteria and study design for an AI/software device's performance because the device itself is a physical implant, not an AI or software product.

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The Anterior Spine Truss System - Stand Alone (ASTS-SA) Interbody Fusion Device is a stand-alone interbody fusion device indicated for use in skeletally mature patients with Degenerative Disc Disease (DDD) of the lumbosacral spine at one or two contiguous disc levels. Each interbody fusion device is intended to be used with three titanium alloy screws or anchors which accompany the implant. Hyperlordotic implants (>20° lordosis) are intended to be used with supplemental fixation (e.g. posterior fixation). DDD is defined as discogenic back pain with degeneration of the disc confirmed by patient history and radiographic studies. ASTS-SA Interbody Fusion Devices are used as an adjunct to fusion in the lumbosacral spine and are placed via an anterior approach at the L2 to S1 disc levels using autograft and/or allogenic bone graft comprised of cancellous and/or corticocancellous bone graft. Patients should have received 6 months of non-operative treatment prior to treatment with the devices. These DDD patients may also have up to Grade I spondylolisthesis or retrolisthesis at the involved level(s). When using the ASTS-SA Interbody with fixation anchors, the device must be used with supplemental fixation.

The device is an open architecture truss design mathematically formulated to provide structural support with open space throughout the implant for bone through 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 to accommodate the patient's anatomy. Screws or anchors are inserted through the anterior portion of the implant into adjacent vertebral bodies for bony fixation.

This looks like a 510(k) summary for a medical device called the "Anterior Spine Truss System - Stand Alone (ASTS-SA)". Unfortunately, this document does not provide the type of information you're asking for regarding acceptance criteria for a device performance study in the context of AI/ML.

Here's why and what's missing:

• Device Type: The ASTS-SA is an interbody fusion device, which is a physical implant used in spinal surgery. It's not an AI/ML-based diagnostic or therapeutic device. Therefore, performance studies for this device focus on mechanical properties, biocompatibility, and clinical outcomes related to fusion, not AI model performance metrics like sensitivity, specificity, AUC, etc.
• Performance Standards: The document lists performance standards such as ASTM F2077 (for mechanical testing) and ASTM F2267 (for subsidence testing), and expulsion testing. These are physical hardware tests, not software performance tests.
• No AI/ML Component: There is no mention of any artificial intelligence or machine learning component in the "Description," "Technological Characteristics," or "Performance Standards" sections.

Therefore, I cannot extract the requested information about acceptance criteria and AI model performance studies (e.g., sample size, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth, training set details) because this document pertains to a physical medical implant, not an AI/ML-driven device.

The questions you are asking are highly relevant to AI/ML device submissions to regulatory bodies like the FDA, but they do not apply to this specific medical device summary.

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The Cervical Spine Truss System (CSTS) Interbody Fusion Device is indicated for use in skeletally mature patients with Degenerative Disc Disease (DDD) of the cervical spine at one level or two contiguous disc levels. DDD is defined as discogenic back pain with degeneration of the disc confirmed by patient history and radiographic studies. CSTS Interbody Fusion Devices are used as an adjunct to fusion in the cervical spine and are placed via an anterior approach at the C2 to T 1 disc levels using autograft and/or allogenic bone graft comprised of cancellous and/or corticocancellous bone graft. Patients should have received 6 weeks of non-operative treatment prior to treatment with the devices. If the device is being used without the CSTS Integrated Plate. supplemental fixation must be used.

The device is an open architecture truss design mathematically formulated to provide structural support with open space throughout the implant for bone through 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 heights, footprints, and lordotic angles. The CSTS Integrated Plates come in multiple heights to accommodate the patient's anatomy. Screws are inserted through the anterior portion of the Integrated Plates into adjacent vertebral bodies for bony fixation. The Integrated Plates have a rotating locking tab to prevent back-out of the screws.

The provided text is a 510(k) summary for a medical device (Cervical Spine Truss System - CSTS). This document focuses on demonstrating substantial equivalence to previously cleared predicate devices, primarily through non-clinical performance testing (mechanical and material testing, and finite element analysis).

It's crucial to understand that 510(k) submissions for devices like the CSTS (intervertebral body fusion devices) typically do not involve clinical studies with human participants in the same way that software-as-a-medical-device (SaMD) or drug trials do. Therefore, the concepts of "acceptance criteria for device performance studies," "sample sizes for test sets (in a clinical context)," "expert adjudication of ground truth," "MRMC studies," "standalone algorithms," and "training sets for AI models" as described in your prompt, are not applicable to this type of regulatory submission or the information contained within this document.

The "study that proves the device meets the acceptance criteria" in this context refers to the non-clinical performance testing that verifies the mechanical integrity and safety of the implant.

Here's how to address your prompt based only on the provided text, while clarifying the differences:

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

The document does not explicitly state numerical "acceptance criteria" for the mechanical tests in a tabular format, nor does it provide specific numerical "reported device performance" values. Instead, it states that the tests were completed "per the following standards" (ASTM F2077, ASTM F2119, ASTM F2182, ASTM F2213, ASTM F2051, and "accepted industry standard" for Expulsion Testing) and that the results "show that the strength of the CSTS interbody and CSTS Interbody with an Integrated Plate is sufficient for its use and is substantially equivalent to legally marketed predicate devices."

Table: Non-Clinical Performance Testing and Outcomes

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

• Sample Size for Test Set: Not specified in terms of number of surgical cases or patient data, as this is not a clinical study. For mechanical testing, "sample size" refers to the number of physical devices or components tested, which is not detailed in this summary.
• Data Provenance: Not applicable in the context of clinical data. The "data" refers to results from laboratory mechanical and MR compatibility testing, and finite element analysis.

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

• This concept is not applicable as the "ground truth" for a mechanical device like this is established by adherence to engineering standards and validated models, not by expert consensus on clinical images or outcomes.

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

• This concept is not applicable in this context.

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

• An MRMC study is not applicable as this is not an AI/imaging device.

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

• This concept is not applicable as this is not an AI/software device.

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

• The "ground truth" (or verification of performance) for this device relies on:
  • Adherence to established ASTM (American Society for Testing and Materials) standards for intervertebral body fusion device mechanical testing and MR compatibility.
  • Validated Finite Element Analysis (FEA), which is a computational method used to predict how a product reacts to real-world forces, heat, vibration, etc.
  • Demonstration of substantial equivalence to previously cleared predicate devices through direct comparison of technological characteristics (indications for use, materials, function, sizes) and performance testing.

8. The sample size for the training set

• This concept is not applicable as this is not an AI/machine learning device.

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

• This concept is not applicable as this is not an AI/machine learning device.

In summary: The provided document is a regulatory submission for a physical medical implant. Its "acceptance criteria" and "proof" relate to meeting established engineering and material standards, and demonstrating substantial equivalence to similar devices already on the market, rather than clinical efficacy studies involving human participants or AI performance metrics.

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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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