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The EndoMap package is an x-ray imaging system software option, which allows the planning of orthopedic surgeries on a workstation. The software is intended to read in diagnostic images (e.g. digitized x-rays) for use with a database of orthopedic implant geometries and dimensions. This provides a constructed image of this data, to use in conjunction with the EndoMap software to overlay the constructed images to aid surgeons in their planning of orthopedic surgeries.

The EndoMap System is a x-ray imaging software option, which allows the planning of orthopedic surgeries on several Siemens workstations such as the LEONARDO, the syngo® Multimodality Workstation and the Magic View 300. The Leonardo Workstation was described in the 510(k) premarket notification, K992073, which received FDA clearance on Sept. 09, 1999. The syngo® Multimodality Workstation K010938 received FDA clearance on June 26, 2001. The EndoMap program enables exact adaptation of the prosthesis to the existing anatomic conditions based on digital images of the pelvis and the leg skeletal structure. This is accomplished using various help functions such as determination of the rotation center and the rotation of the pelvis, medialization, leg length correction, reflection via the symmetric axis of the body, biomechanical evaluation of the hip geometry and coxometry (analysis of the hip values). The planning data can be saved to the database of the LEONARDO or other workstations and output to a laser or paper printer.

The provided text is a 510(k) summary for the EndoMap system, an X-ray imaging system software option for planning orthopedic surgeries. However, it does not contain information about acceptance criteria or a study proving the device meets those criteria, nor does it provide details on performance metrics or study methodologies.

The document primarily focuses on:

• Identification of the device: EndoMap, its classification, and intended use.
• Substantial equivalence: Comparing EndoMap to predicate devices (BrainLabs' VectorVision Hip and Knee) based on functional specifications.
• Device description: General features and functionalities.
• Regulatory approval: The FDA clearance letter stating substantial equivalence.

Therefore, I cannot fulfill your request for the specific details regarding acceptance criteria and performance studies based on the provided text. The document explicitly states that "Information that substantiates this claim of equivalence is provided throughout this 510(k) submission and specific equivalence information is provided in Attachment 5," and that "the functional specifications and the substantial equivalence comparison matrix proves that the EndoMap Software is substantially equivalent." This suggests that the detailed equivalence information, which would typically contain performance data if such a study were deemed necessary for equivalence, is located in an attachment not provided here.

Based on the provided text, the following information is missing:

1. A table of acceptance criteria and the reported device performance: This information is not present. The document focuses on substantial equivalence to predicate devices rather than independent performance metrics against predefined criteria.
2. Sample size used for the test set and the data provenance: Not mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not mentioned.
4. Adjudication method for the test set: Not mentioned.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not mentioned. The device is described as an aid for surgeons in planning, but no studies on reader performance or improvement are detailed.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not explicitly mentioned or detailed. The nature of the device as a planning aid implies human interaction.
7. The type of ground truth used: Not mentioned.
8. The sample size for the training set: Not mentioned.
9. How the ground truth for the training set was established: Not mentioned.

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