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The aprevo® TLIF-C Articulating System is interbody fusion in skeletally mature patients and is to be used with supplemental fixation instrumentation cleared for use in the lumbar spine. The aprevo® TLIF-C Articulating System is indicated for use as an adjunct to fusion at one or more levels of the lumbar spine in patients having an ODI >40 and diagnosed with severe symptomatic adult spinal deformity (ASD) conditions. These patients should have had six months of non-operative treatment. The device is intended to be used with autograft and/or allogenic bone graft comprised of cancellous and/or cortico-cancellous bone graft. These implants may be implanted via a variety of open or minimally invasive approaches.

The aprevo® TLIF-C Articulating System is indicated for use at one or more levels of the lumbosacral spine as an adjunct to fusion in patients with the following indications: degenerative disc disease (DD), disc herniation (with myelopathy and/or radiculopathy), spondylolisthesis, deformity (degenerative scoliosis or kyphosis), spinal stenosis, and failed previous fusion (pseudarthrosis). DDD is defined as discogenic back pain with degeneration of the disc as confirmed by history and radiographic studies. These patients should be skeletally mature and have had at least six (6) months of non-operative treatment. aprevo® TLIF-C Articulating System devices are to be filled with autograft bone and/or allogenic bone graft composed of cancellous and/or corticocancellous bone. These devices are intended to be used with supplemental fixation systems that have been cleared for use in the thoracolumbosacral spine (e.g., posterior pedicle screw and rod systems). These implants may be implanted via a variety of open or minimally invasive approaches.

The aprevo® TLIF-C Articulating System devices are designed to stabilize the lumbar spinal column and promote fusion. The personalized aprevo® interbodies incorporate patient specific features to allow the surgeon to tailor the deformity correction to the individual needs of the patient and include an aperture intended for the packing of bone graft.

The aprevo® TLIF-C Articulating interbodies are additively manufactured from Ti-6Al-4V ELI titanium alloy per ASTM F3001. The devices are accompanied by an inserter instrument which facilitates the placement of the interbodies. Both the interbody devices and instrument are provided as single use, sterile-packed product to the end user.

The provided text describes a medical device, the aprevo® TLIF-C Articulating System, and its substantial equivalence to predicate devices, supported by non-clinical testing. However, it does not contain the specific information required to answer the questions about acceptance criteria for a study demonstrating device performance for an AI/ML-enabled medical device. The information provided is primarily focused on the mechanical and physical performance of the interbody fusion device itself, not on the performance of any AI/ML component.

Therefore, I cannot provide a detailed answer to your request regarding acceptance criteria and study details for an AI/ML device based on the given input.

Here's a breakdown of why and what kind of information would be needed:

• No AI/ML Component Described: The text describes a physical interbody fusion device and its associated instruments. There is no mention of any AI or machine learning algorithms, software, or intelligent functions associated with the device. The "technological characteristics" mentioned refer to its mechanical properties and materials, not software performance.
• Non-Clinical Testing is for Physical Device: The "non-clinical testing" listed (Static and dynamic axial compression, Static and dynamic compression-shear, Subsidence, Cadaveric study) are standard tests for orthopedic implants to ensure their mechanical integrity and biocompatibility. These tests do not evaluate AI/ML performance.

To answer your questions, the input would need to describe an AI/ML-enabled medical device and its corresponding performance study, which would typically include metrics like sensitivity, specificity, accuracy, AUC, etc., along with details on the ground truth establishment, expert review, and dataset characteristics.

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