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MySpine S2-Alar/Alar-Iliac is intended for use with M.U.S.T. Pedicle Screw System and its cleared indications for use. MySpine S2-Alar/Alar-Iliac guides (referred to from this point on as, MySpine guides) are intended to be used as anatomical perforating guides, specific to a patient's anatomy, to assist intra-operatively in the preparation of the screw trajectory in S1, S2 and in the Ilium. The guides are created using a surgical planning software which pre-operatively plans the positions of the components based upon radiological images of the patients' anatomical landmarks and the surgical equipment selected. MySpine guides are intended for single use only.

The MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides are a line extension to Medacta's MySpine Pedicle Screw Placement Guides. Identical to the other Medacta MySpine products, the MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides are a patient matched, pedicle targeted, technology involving the production of patient specific guides for placement of the M.U.S.T. Pedicle Screw System (K12115, K132878, K141988, K153664, K162061, and K171170). Specifically, the subject MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides are intended to be used as anatomical perforating guides to assist intra-operatively in the preparation of the screw trajectory in S1, S2 and in the Ilium. The MySpine software platform allows the surgeon to complete 3D pre-operative planning based on the patient's spinal CT scans. CT images are used to create a 3D model of the vertebrae that will represent the template used to generate the corresponding MySpine Screw Placement Guides fitting the patient's vertebral anatomy. The MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides as well as their bone models are single-use and they can be provided in sterile or non-sterile version.

The provided text describes a medical device, the "MySpine S2-Alar/Alar-Iliac Pedicle Screw Placement Guides," and its substantial equivalence determination by the FDA. However, the document does NOT contain information about specific acceptance criteria for a device's performance (like sensitivity, specificity, accuracy, etc.) nor does it usually detail specific studies that prove the device meets such criteria in terms of quantitative metrics suitable for the requested table.

Instead, this document focuses on demonstrating substantial equivalence to a predicate device based on similar technological characteristics (manufacturing process, material, biocompatibility, device usage, sterility, shelf life, packaging) and performance data from non-clinical studies (software validation, cadaver testing, guide accuracy, stability assessment). The "guide accuracy" mentioned is a general category and not a specific set of acceptance criteria with reported performance.

Therefore, I cannot fill in the table of acceptance criteria and reported device performance, nor can I answer questions related to sample size, expert qualifications, or ground truth establishment for a standalone algorithm performance study, because this information is not present in the provided text. The document explicitly states: "No clinical studies were conducted." and it does not describe an AI algorithm with human-in-the-loop performance.

Here's an overview of what can be extracted or inferred based on the provided text, and where information is missing:

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

• Acceptance Criteria: Not explicitly stated with quantifiable metrics (e.g., "accuracy must be > 95%"). The document talks about "software validation," "cadaver testing," "guide accuracy," and "stability assessment" as general categories of testing performed, aiming to show safety and effectiveness comparable to the predicate.
• Reported Device Performance: No specific quantitative performance metrics (e.g., accuracy percentages, error margins) are reported in the document.

Therefore, this table cannot be filled based on the provided input.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: Not specified. The document mentions "Cadaver testing" and "Guide accuracy" testing, but no details on the number of cadavers or cases used for these tests are provided.
• Data Provenance: Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable as no detailed test set and ground truth establishment methodology is described in the provided text. The document refers to "surgical planning software" that "pre-operatively plans the positions of the components based upon radiological images," but this is part of the device's function, not a ground truth establishment for an AI algorithm's performance evaluation.

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

• Not applicable as no detailed test set and ground truth establishment methodology is described.

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

• MRMC Study: No. The document explicitly states: "No clinical studies were conducted." The device (surgical guides) assists surgeons, but the documentation does not describe a study involving human readers and AI assistance.

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

• The device is a physical surgical guide produced using software, not a standalone AI algorithm that provides diagnostic or prognostic outputs. The "MySpine software platform" is integral to creating the guides. "Software validation" was performed, but no standalone algorithm performance as typically understood in AI/ML context (e.g., measuring diagnostic accuracy against ground truth without human intervention) is described.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

• Not explicitly stated for any performance evaluation in terms of AI. For the device itself and its intended use, the anatomical landmarks from patient CT scans and selected surgical equipment (per planning software) would form the basis for the design of the patient-specific guides. The "guide accuracy" testing would presumably involve comparing the guide's output (screw trajectory) against the planned trajectory or anatomical reality, but the specific ground truth methodology for this testing is not detailed.

8. The sample size for the training set

• Not applicable. The document describes a medical device (surgical guides) and the software that creates them for individual patients. It does not refer to a machine learning model that requires a "training set" in the conventional sense for AI performance. The software uses patient-specific CT data for planning.

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

• Not applicable, as there is no mention of a "training set" for an AI model.

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