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The Surgical Planning Software assists healthcare professionals in planning lumbar spinal fusion surgeries. The device allows service providers to plan surgical procedures, including tools for assessing anatomical components for placement of surgical implants. Clinical judgment and experience are required to properly use the software.

The Ortho Kinematics Surgical Planning Software is a software only accessory to a picture archiving and communications system. It is designed to assess measurements from DICOM compliant images in a cleared PACS device and plan surgical spinal procedures. It offers the ability to plan certain surgical procedures, such as lumbar spine fusion. It offers tools for viewing data, and the ability to facilitate the estimation of a potential post-operative alignment and correction, based on pre-operative measurements, as well as the geometric parameters associated with specific lumbar interbody implant devices.

The provided document is a 510(k) premarket notification for the "Surgical Planning Software version 1.1". It states that the device is substantially equivalent to a predicate device and therefore can be marketed. However, this submission does not contain the detailed acceptance criteria or the study that proves the device meets specific acceptance criteria for a new AI/ML-driven medical device.

Instead, it states:
"Software verification and validation testing was completed for the subject device. The software functioned as intended and all results observed were as expected."
And, "Software verification and validation testing demonstrates that the device performs as intended."

This suggests that the performance review was primarily focused on software functionality and comparison to a predicate device rather than a comprehensive clinical or analytical study with defined acceptance criteria for AI/ML performance metrics.

Therefore, I cannot extract the information required in the prompt as it is not present in the provided text. The document focuses on regulatory approval based on equivalence to a predicate device rather than presenting new performance data against specific acceptance criteria for an AI/ML algorithm.

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