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510(k) Data Aggregation
(58 days)
ACPII ANTERIOR CERVICAL PLATE SYSTEM
The ACPii Anterior Cervical Plate System is intended for anterior interbody screw fixation of the cervical spine. The system is indicated for use in the temporary stabilization of the anterior spine during the development of cervical spinal fusions in patients with degenerative disc disease (as defined by neck pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), trauma (including fractures), tumors, deformity (defined as kyphosis, lordosis, or scoliosis), pseudarthrosis, and/or failed previous fusions.
The ACPii Anterior Cervical Plate System consists of a variety of shapes and sizes of bone plates (set screws and washers are pre-assembled to plates), screws and associated instruments. Fixation is provided by bone screws inserted into the vertebral body of the cervical spine using an anterior approach. The implant components will be made from titanium alloy as described by ASTM F-136 and may be supplied either sterile or non-sterile.
The provided text describes a 510(k) summary for the ACPii Anterior Cervical Plate System. This is a submission for a medical device (a spinal implant) to the FDA to demonstrate substantial equivalence to a legally marketed predicate device. The information requested in your prompt (acceptance criteria, details of a study using AI, human readers, ground truth establishment, sample sizes, etc.) is typically associated with the evaluation of AI/ML-based medical devices or diagnostic tools, often involving performance metrics like sensitivity, specificity, or AUC.
The ACPii Anterior Cervical Plate System is a physical medical device (implant). Its approval process focuses on mechanical performance and material safety, as well as demonstrated substantial equivalence to existing predicate devices.
Therefore, the information you're asking for is not applicable to this type of device and its 510(k) summary. There is no mention of AI, diagnostic imaging, human readers, or any of the study design elements typically found in AI/ML device evaluations.
Here's why each point isn't relevant to the provided text:
- A table of acceptance criteria and the reported device performance: For a spinal implant, "acceptance criteria" and "device performance" would relate to mechanical strength (e.g., fatigue strength, pull-out strength of screws), biocompatibility, and perhaps surgical success rates if clinical data were submitted. The summary only states "Mechanical test data were provided in support of this notification." It does not provide specific acceptance criteria or detailed performance results in the summary itself.
- Sample size used for the test set and the data provenance: This usually refers to patient data for AI/ML models. For a physical implant, "test set" would refer to the number of implants tested mechanically. This information is not provided in the summary.
- Number of experts used to establish the ground truth... and qualifications: Ground truth establishment by experts is relevant for diagnostic accuracy or image interpretation. It's not applicable to a spinal implant's mechanical properties.
- Adjudication method: Again, relevant for diagnostic interpretations, not for mechanical testing of an implant.
- Multi reader multi case (MRMC) comparative effectiveness study: This is for evaluating diagnostic performance with and without AI assistance. Not applicable.
- Standalone (i.e. algorithm only without human-in-the loop performance): This refers to AI algorithm performance. Not applicable.
- The type of ground truth used: For an implant, "ground truth" would be the actual mechanical performance measurements or clinical outcomes, not expert consensus on an image.
- The sample size for the training set: Training set for AI/ML models. Not applicable.
- How the ground truth for the training set was established: Ground truth for AI/ML models. Not applicable.
In summary, the provided 510(k) summary is for a physical orthopedic implant. The questions you've asked are designed for AI/ML-driven diagnostic devices. As such, the information you're looking for is not present in this document because it pertains to a different category of medical device evaluation.
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