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510(k) Data Aggregation
(126 days)
The Leva® Anterior Interbody System is a stand-alone system indicated for intervertebral body fusion procedures in skeletally mature patients with degenerative disc disease (DDD) of the lumbar spine at one or two contiguous levels from L2-S1. Degenerative disc disease is defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies. These DDD patients may have up to Grade I spondylolisthesis or retrolisthesis at the involved level(s). The Leva® Anterior Interbody System is intended to be filled with autogenous bone graft material and used with the bone screws provided. Patients should have at least six (6) months of non-operative treatment prior to treatment with an intervertebral body fusion device.
The Leva® Anterior Interbody System is family of stand-alone lumbar intervertebral body fusion devices fabricated from Ti-6A1-4V per ASTM F1472 and Ti-6A1-4V ELI per ASTM F136. The device includes integrated plate/screw fixation available in two configurations. In both device configurations, the surgeon inserts the bone screws through the anterior plate screw holes to provide fixation to the vertebral bodies. The device is intended to be filled with autogenous bone graft material. Both implant configurations are provided in various dimensions to accommodate the anatomical needs for a range of patients. The implants have convex endplates to conform to the bony endplates of the patient and ridges on the endplates to resist expulsion.
The provided text describes a medical device, the Leva® Anterior Interbody System, and its clearance process with the FDA. However, it does not contain information regarding acceptance criteria or a study proving the device meets those criteria in the context of an AI/ML model.
The text is a 510(k) premarket notification for a traditional medical device (an intervertebral body fusion device) and focuses on demonstrating substantial equivalence to predicate devices, primarily through mechanical performance testing and material composition.
Therefore, I cannot extract the requested information (acceptance criteria, study details, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth details, or training set information) because the provided document is not about an AI/ML device or its performance evaluation.
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