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The MIS Spinal Systems are intended to provide immobilization of spinal segments in skeletally mature patients as an adjunct to fusion in the treatment of acute and chronic instabilities or deformities of the thoracic, lumbar and sacral spine. The MIS Spinal System can be used in an open approach and a percutaneous approach.

The MIS Spinal System is intended for the following indications:

Degenerative disc disease (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies), Spondylolisthesis, Trauma (i.e., fracture or dislocation), Spinal stenosis, Curvatures (i.e., scoliosis, kyphosis, lordosis), Tumor, Pseudarthrosis, Failed previous fusion.

The MIS spinal system consists of cannulated poly screws, straight rods, curved rods and set screw components that can be used via percutaneous surgical approach. The components are available in a variety of diameters and lengths in order to accommodate patient anatomy and are fabricated from titanium alloy (ASTM F136) and CoCrMo alloy (Cobalt-28Chromium-6Molybdenum, per ASTM F1537).

The provided text is a 510(k) summary for a spinal system, not a summary of an AI/ML-driven medical device study. Therefore, the information required to answer the prompt regarding acceptance criteria and performance studies for an AI/ML device is not present in the input.

Specifically, the document focuses on demonstrating substantial equivalence of a physical medical device (spinal screws and rods) to previously cleared predicate devices based on design, materials, manufacturing process, and mechanical performance testing. It does not mention any AI/ML components, software, or algorithms.

Therefore, I cannot extract the following information from the provided text:

1. Table of acceptance criteria and reported device performance: This document reports mechanical testing data (substituted from predicate devices), not performance metrics for an AI/ML algorithm (e.g., accuracy, sensitivity, specificity).
2. Sample size used for the test set and data provenance: There is no mention of a test set for an AI/ML algorithm. The "testing" referred to is mechanical reliability testing of the physical hardware.
3. Number of experts used to establish ground truth and qualifications: Ground truth for AI/ML models is typically established by experts (e.g., radiologists, pathologists). This is not applicable here as there is no AI/ML component.
4. Adjudication method for the test set: Not applicable for mechanical hardware testing.
5. Multi-reader multi-case (MRMC) comparative effectiveness study: This type of study is for evaluating the impact of AI assistance on human readers, which is not relevant to a spinal implant.
6. Standalone (algorithm only) performance: There is no algorithm.
7. Type of ground truth used: No AI/ML ground truth is involved.
8. Sample size for the training set: Not applicable as there is no AI/ML model to train.
9. How ground truth for the training set was established: Not applicable.

In conclusion, the provided text does not contain the necessary information to describe the acceptance criteria and study proving an AI/ML device meets them, as it pertains to a physical medical device (spinal system hardware).

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