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The Lineum OCT Spine System is intended to provide immobilization of spinal segments as an adjunct to fusion for the following acute and chronic instabilities of the craniocervical junction, the cervical spine (C1 to C7) and the thoracic spine (T1-T3): traumatic spinal fractures and/or traumatic dislocations; instability or deformity; failed previous fusions (e.g. pseudarthrosis); tumors involving the cervical/thoracic spine; and degenerative disease, including intractable radiculopathy and/or myelopathy, neck and/or arm pain of discogenic origin as confirmed by radiographic studies, and degenerative disease of the facets with instability. The Lineum OCT Spine System is also intended to restore the integrity of the spinal column even in the absence of fusion for a limited time period in patients with advanced stage tumors involving the cervical spine in whom life expectancy is of insufficient duration to permit achievement of fusion.

In order to achieve additional levels of fixation, the Lineum OCT Spine System can also be connected to the Biomet Polaris System via transitional rods or using the Altius Rod Connectors or Polaris Dominoes. Please refer to the individual system's package insert for a list of indications for use for each system.

The Lineum OCT Spine System consists of various screws, hooks, plates, rods, connectors, etc. that are used to build a construct to provide supplemental stabilization of spinal segments to support fusions. The system can be assembled in a variety of configurations, allowing the surgeon to tailor the construct to the particular needs of the patient. The implants are designed for the occiput, cervical, and/or upper thoracic spine (Occiput – T3) and consist of medical grade titanium alloy and CoCrMo alloy, similar to the predicate device.

The provided document is a 510(k) premarket notification summary for a medical device called the "Lineum OCT Spine System." This type of document is for demonstrating substantial equivalence to a legally marketed predicate device, primarily through performance data focusing on mechanical and material properties, rather than clinical efficacy studies often seen with AI/ML devices. As such, the information required for a detailed AI/ML device study description (acceptance criteria, sample sizes, ground truth establishment, expert qualifications, etc.) is not present in this document.

The document discusses mechanical performance data, not AI/ML algorithm performance. Therefore, I cannot provide the requested information about acceptance criteria for an AI/ML device, the study proving it meets them, sample sizes for AI/ML tests, ground truth details, expert qualifications, or MRMC studies, as these concepts are not applicable to the content of this regulatory submission.

Here's what the document does provide regarding performance data:

Performance Data (Mechanical)

Study Details (Mechanical Testing):

• Study Type: Non-clinical mechanical construct and component testing.
• Standards Used: Based on ASTM F2706 (Standard Test Method for Static and Fatigue Testing of a Spinal Orthosis for the Occiput-Cervical-Thoracic Spine) and ASTM F1798 (Standard Guide for Evaluation of Static and Fatigue Properties of Interconnection Mechanisms and Subassemblies Used in Anterolateral Cervical Spine Fusion Constructs).
• Purpose: To support the substantial equivalence of the modified Lineum OCT Spine System to its predicate device.
• Conclusion: The mechanical testing provided demonstrates the substantial equivalence of the subject Lineum OCT Spine System.

Missing Information (Not applicable to this type of device submission):

• Sample size used for the test set and data provenance (e.g., country of origin, retrospective or prospective) for an AI/ML algorithm.
• Number of experts used to establish ground truth for a test set and their qualifications.
• Adjudication method for the test set.
• Multi-reader multi-case (MRMC) comparative effectiveness study or effect size of human readers with/without AI assistance.
• Standalone (algorithm only) performance study.
• Type of ground truth used (expert consensus, pathology, outcomes data, etc.) for an AI/ML algorithm.
• Sample size for the training set (for an AI/ML algorithm).
• How the ground truth for the training set was established (for an AI/ML algorithm).

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