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510(k) Data Aggregation
(84 days)
NANOVIS SPINAL SYSTEM
The Nanovis Spinal System is intended for posterior, non-cervical (i.e., T1-S1) pedicle and non-pedicle fixation to provide immobilization and stabilization in skeletally mature patients as an adjunct to fusion for the treatment of the following acute and chronic instabilities or deformities: degenerative disc disease (DDD, defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies), spondylolisthesis, fracture, dislocation, spinal stenosis, scoliosis, kyphosis, lordosis, spinal tumor, pseudarthrosis and failed previous fusion.
The Nanovis Spinal System consists of rods, hooks, monoaxial and polyaxial screws with set screws, and crosslinks with fastening set screws. Rods are available either straight or pre-contoured in a variety of lengths. Hooks are offered in a variety shapes and sizes. Solid and cannulated monoaxial and polyaxial screws are available in standard and reduction versions in a variety of diameter-length combinations to accommodate differing patient anatomy.
The Nanovis Spinal System is a medical device and its approval is based on a demonstration of substantial equivalence to predicate devices rather than the typical performance study structure found in AI/ML device submissions. Therefore, many of the requested categories related to human-AI performance, sample sizes for training/test sets, and ground truth establishment are not applicable.
Here's the information extracted from the provided text regarding the Nanovis Spinal System:
Acceptance Criteria and Device Performance
The acceptance criteria for the Nanovis Spinal System are based on demonstrating mechanical performance that is "as well as or better than the predicate device" through ASTM F1717 testing.
Acceptance Criteria | Reported Device Performance |
---|---|
Mechanical performance "as well as or better than" the predicate device for: | "The mechanical test results demonstrated that the Nanovis Spinal System performs as well as or better than the predicate device." |
- Static compression bending | (Met) |
- Static torsion | (Met) |
- Dynamic compression bending of the worst-case construct | (Met) |
Study Proving Device Meets Acceptance Criteria
The study conducted was a mechanical testing study performed according to ASTM F1717.
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Sample size used for the test set and data provenance:
- Sample Size: Not explicitly stated as a number of samples in the classic sense (e.g., cases, images). The testing refers to "the worst case Nanovis construct." This implies that specific configurations of the device were chosen for testing, likely representative samples of the manufactured components.
- Data Provenance: Not applicable in the context of clinical data. This refers to mechanical testing performed on physical prototypes/products.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable. Mechanical testing uses engineering standards (ASTM F1717) and physical measurements as the 'ground truth,' not expert clinical opinion.
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Adjudication method for the test set:
- Not applicable. Adjudication is relevant for subjective assessments, typically in clinical studies or image review. Mechanical tests yield objective, quantifiable results.
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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:
- No. This is a mechanical device, not an AI/ML diagnostic or assistive tool.
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If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Not applicable. This refers to a physical medical device (spinal implant system).
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The type of ground truth used:
- The "ground truth" for this device's performance evaluation was established by the specified mechanical testing protocols and acceptance criteria of ASTM F1717. This standard defines the methods for assessing the static and fatigue properties of metallic spinal implant constructs.
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The sample size for the training set:
- Not applicable. This device does not involve a "training set" in the context of AI/ML or statistical modeling.
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How the ground truth for the training set was established:
- Not applicable. As above, there is no "training set" in this context.
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