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The Aesculap Ennovate Cervical Spinal and Occiput System is intended to provide immobilization of spinal segments as an adjunct to following acute and chronic instabilities of the craniocervical junction, the cervical spine (Cl to C7) and the thoracic spine (Tl-T3): traumatic spinal fractures and/or traumatic dislocations; instability or deformity; failed previous (e.g. pseudoarthrosis); tumors involving the cervical/thoracic spine; and degenerative disease, including intractable radior myelopathy, neck and/or arm pain of discogenic origin as confirmed by radiographic studies, and degenerative disease of the facets with instability. The Aesculap Ennovate Cervical Spinal and Occiput 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 Aesculap Ennovate Lumbar System may be connected to the Aesculap Ennovate Cervical Spinal and Occiput System using connectors and rods.

The Aesculap ENNOVATE Cervical Spinal and Occiput System is implant systems used to facilitate the biological process of spinal fusion. This system is intended to promote fusion of the cervical and thoracic spine (C1-T3) and occipito-cervico-thoracic junction (occiput-T3). The Aesculap ENNOVATE Cervical Spinal and Occiput System consist of various plates, screws, rods, hooks, and connectors.

The provided text is a 510(k) Summary for the Aesculap ENNOVATE Cervical Spinal and Occiput System, which is a medical device. This document focuses on demonstrating substantial equivalence to a predicate device, primarily through bench testing (performance data).

The request asks for information about the acceptance criteria and the study that proves the device meets the acceptance criteria, specifically in the context of an AI/algorithm-based device. The original document does not describe an AI/algorithm-based device. It describes a physical implant system (screws, rods, plates, etc.) for spinal stabilization. Therefore, the questions related to AI/algorithm performance studies (such as MRMC studies, standalone algorithm performance, ground truth establishment for training/test sets, training set sample size, expert consensus, etc.) are not applicable to this submission.

The "Performance Data" section solely lists ASTM standards that the physical device was tested against. These standards evaluate the mechanical properties of the implant system (e.g., strength, fatigue, static properties) to ensure it performs as safely and effectively as the predicate device.

Given that the request is for an AI/algorithm device study and the provided document is for a physical orthopedic implant, I can only address the "acceptance criteria" and "device performance" in the context of the mechanical and material testing described for this specific physical device.

Acceptance Criteria and Device Performance (Based on the provided document for a physical implant):

The acceptance criteria for this physical device are implicitly tied to the successful completion of the listed ASTM standard tests, demonstrating equivalence in mechanical performance to the predicate device. The performance is stated to be "as safely and effectively" as the predicate device based on these tests.

1. A table of acceptance criteria and the reported device performance:

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

• Sample Size: Not specified in the document. For physical device testing, this typically refers to the number of implants or components tested according to the ASTM standards.
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). However, since it's bench testing, the "data" originates from the physical tests performed on the device samples in a laboratory setting.

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

• Not Applicable. This is a physical device, and its performance is evaluated against engineering standards, not by expert interpretation of images or data. Ground truth here is based on the objective measurements from the mechanical testing.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not Applicable. Adjudication methods are relevant for human interpretation or complex data analysis, not for direct physical property measurements against engineering standards.

5. 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:

• Not Applicable. This is not an AI/algorithm-based device. An MRMC study would not be performed for a physical implant.

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

• Not Applicable. This is not an AI/algorithm-based device.

7. The type of ground truth used:

• Mechanical Performance Standards: The ground truth for this physical device is the set of mechanical and material properties defined by the ASTM standards (e.g., tensile strength, fatigue life, torque limits) which are universally accepted engineering benchmarks for such implants. Compliance with these quantitative standards serves as the "ground truth."

8. The sample size for the training set:

• Not Applicable. This is a physical device, not an algorithm that requires a training set.

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

• Not Applicable. As above, no training set for an algorithm is involved.

In summary, the provided document describes a 510(k) submission for a physical medical device. The "acceptance criteria" and "study proving device meets criteria" are based on a series of bench tests against pre-defined ASTM engineering standards to demonstrate substantial equivalence to an existing predicate device, primarily in terms of mechanical and material performance. The questions related to AI/algorithm studies are not relevant to this specific premarket notification.

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