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

The PathLoc-L 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

PathLoc-L MIS Spinal System consists of 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 made of titanium alloy (ASTM F136).

The provided text is a 510(k) Summary for the PathLoc-L MIS Spinal System. It describes a medical device, which is a pedicle screw spinal system used to stabilize spinal segments. The submission is for adding new components and changing part numbers to an already cleared device (K161766).

Based on the document, this is not a study that proves a device meets acceptance criteria for an AI/CADe system. This is a submission for a physical medical device (spinal implant), and the "performance testing" section refers to mechanical tests conducted on the physical components of the spinal system, not the performance of an AI algorithm.

Therefore, most of the requested information regarding AI/CADe systems (e.g., sample sizes for test/training sets, ground truth establishment, MRMC studies, standalone algorithm performance, expert qualifications, adjudication methods) is not applicable to this document.

However, I can extract the relevant "acceptance criteria" and "device performance" in the context of this physical device's mechanical testing:

1. Table of Acceptance Criteria and Reported Device Performance

Note on "Acceptance Criteria" and "Reported Performance": The document states that the performance testing was conducted according to specific ASTM standards. For a 510(k) submission, meeting these recognized standards for mechanical properties means the device is considered as safe and effective as a predicate device that also meets these standards. The document doesn't provide specific numerical results of these tests, but rather states that the tests were performed and the device (including the new components) performs as well as the predicate.

Given this is a physical implant device, the following points are not directly extractable or applicable as they pertain to AI/CADe systems:

• 2. Sample size used for the test set and the data provenance: Not applicable. This is not an AI/CADe system.
• 3. Number of experts used to establish the ground truth... and the qualifications of those experts: Not applicable. Ground truth for an AI system is not relevant here.
• 4. Adjudication method: Not applicable.
• 5. If a multi reader multi case (MRMC) comparative effectiveness study was done: Not applicable.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
• 7. The type of ground truth used: Not applicable.
• 8. The sample size for the training set: Not applicable.
• 9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document details the regulatory submission for a physical spinal implant system, focusing on its mechanical properties and equivalence to a predicate device, rather than the performance of a diagnostic AI algorithm.

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