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The OPTIMUS Anterior Lumbar Interbody Fusion (ALIF) System is indicated for intervertebral body fusion procedures at one or two contiguous levels (L2-Sl) in skeletally mature patients with degenerative disc disease (DDD). DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by the history and radiographic studies. DDD patients may also have up to Grade I Spondylolisthesis or retrolisthesis at the involved levels. The patient should be skeletally mature and have had six months of non-operative treatment.

The OPTIMUS Anterior Lumbar Interbody Fusion (ALIF) System is a stand-alone system intended to be used with bone screws, autogenous bone graft and requires no additional supplementary fixations. One device is used per intervertebral space.

The Optimus Anterior Lumbar Interbody Fusion (ALIF) system is a zero profile stand-alone interbody that combines the benefits of an anterior fixation plate and fusion device. Optimus is a modular design that consists of a center fixation plate, lordotic end caps and three bone screws for anterior fixation and stability. The bone screws thread into the fixation plate and pass through the superior and inferior openings of the end caps to fixate the implant with the adjacent vertebral bodies. The device is available in two footprints, multiple heights, lordotic angles and screw lengths.

The provided text describes a medical device, the OPTIMUS Anterior Lumbar Interbody Fusion (ALIF) System, and its performance testing to demonstrate substantial equivalence to predicate devices. It does not contain information about an AI/ML powered device, nor does it detail a clinical study with human readers or ground truth established by experts in the context of diagnostic accuracy.

Therefore, many of the requested categories are not applicable to the provided document.

Here's an analysis based on the information provided, with "N/A" for sections not covered by the text:

Acceptance Criteria and Device Performance

The device's performance was evaluated through mechanical testing to demonstrate substantial equivalence to previously cleared predicate devices. The acceptance criteria were implicit in establishing mechanical superiority or equivalence to these predicates.

Study Details:

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not specified. Mechanical testing typically involves a number of samples to ensure statistical validity, but the exact number isn't mentioned in this summary.
   • Data Provenance: The data comes from mechanical testing performed on the physical device components and predicate devices, so it's laboratory-generated data, not patient data from a specific country or retrospective/prospective study.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • N/A. The performance data is based on mechanical testing according to established ASTM standards, not on expert-adjudicated ground truth as would be used for AI/ML diagnostic devices. The "ground truth" here is the objective measurement of mechanical properties.

3. Adjudication method for the test set:

   • N/A. Not applicable as this is mechanical testing, not a diagnostic study requiring expert adjudication.

4. 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 not an AI-powered device, nor is it a diagnostic imaging device that would typically involve human readers. Therefore, an MRMC study and effects on human reader performance are not applicable.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • N/A. This is a physical medical implant, not an algorithm. Therefore, standalone algorithm performance is not applicable.

6. The type of ground truth used:

   • Physical Measurement/Standardized Testing Results: The "ground truth" for this device's performance is derived from objective physical measurements obtained through standardized mechanical testing (e.g., force, displacement, torque) performed in accordance with ASTM standards. The comparison is against previously cleared predicate devices' mechanical properties.

7. The sample size for the training set:

   • N/A. This is a physical medical device, not an AI/ML algorithm requiring a training set.

8. How the ground truth for the training set was established:

   • N/A. Not applicable, as there is no training set for this type of device.

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