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The ExactechGPS is intended for use during preoperative planning and during orthopedic surgeon in locating anatomical structures and aligning the endoprosthesis with the anatomical structures provided that the required anatomical landmarks can be identified on the patient's preoperative CT scan.

The ExactechGPS Total Shoulder Planning Application is specifically indicated for pre-operative planning of Total Shoulder Arthroplasty using the Equinoxe system. The ExactechGPS Total Shoulder Planning Application permits to visualize, measure and reconstruct anatomical structures in order to select and place the glenoid and humeral components.

The Exacted GPS Total Shoulder Navigation is specifically indicated for Total Shoulder Arthroplasty using the Equinoxe system to aid the surgeon in locating anatomical structures and aligning the glenoid component with the anatomical structures.

The ExactechGPS Total Shoulder Application proposed in this submission is a modification of the ExactechGPS Total Shoulder Application cleared per 510(k) #K173372.

The ExactechGPS Total Shoulder Application is an Image Guided Surgery, or Navigation, system designed to quide surgeons during the preparation of the glenoid as part of a total shoulder arthroplasty procedure. The ExactechGPS Total Shoulder Application also offers a preoperative planning feature that enables surgeons to plan a surgical intervention by evaluating implant size, type, and positioning using reconstructed patient bone models in a virtual environment. The ExactechGPS Total Shoulder Application requires patient CT-scan data to undergo segmentation prior to being imported into the software, as part of reconstructing the bone model, for both navigation and planning.

In the predicate device, the planning step was only indicated for pre-operative planning of the dlenoid part of the Equinoxe system. This submission proposes the addition of the humeral component. Therefore, the proposed ExactechGPS Total Shoulder Planning Application permits to visualize, measure and reconstruct anatomical structures in order to select and place the glenoid and humeral components.

The proposed ExactechGPS Total Shoulder Planning and Navigation Applications integrate some other modifications compared to the predicate device. These modifications are documented in Letter to Files.

The proposed modifications do not change the ExactechGPS System general intended use, general design features, or basic fundamental scientific technology. No changes to the hardware platform or system accessories are proposed by this submission.

The provided text is a 510(k) summary for the ExactechGPS Total Shoulder Application. It describes a medical device and its proposed modifications, claiming substantial equivalence to a predicate device. However, it does not include detailed performance data, acceptance criteria, or study methodologies that would allow for a comprehensive description as requested. It only broadly states that "testing information demonstrating safety and effectiveness... is supported by testing that was conducted in-house" and lists general types of software testing (verification and validation).

Therefore, I cannot provide a complete answer to all parts of your request based solely on the provided document. I will focus on what can be inferred or directly stated from the text.

Here's an attempt to answer your questions based on the provided document, highlighting where information is missing:

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

The document states: "Software verification testing to ensure all design outputs meet all specified requirements" and "Software validation to ensure software specifications conform to user needs and intended uses."
However, the specific acceptance criteria and the reported device performance metrics (e.g., accuracy, precision, sensitivity, specificity, etc.) are not provided in this 510(k) summary.

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

This information is not provided in the document. The text only mentions "testing that was conducted in-house."

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

This information is not provided in the document.

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

This information is not provided in the document.

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

The document describes a "surgical navigation system" and "preoperative planning feature," but does not indicate that an MRMC comparative effectiveness study was performed or that human readers' improvement with AI assistance was measured. The focus is on the device's functionality in aiding surgical planning and navigation. Therefore, this information is not applicable or not provided in the context of this 510(k) summary.

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

The document mentions "software verification testing" and "software validation" which would typically involve testing the algorithm's performance in a standalone manner (e.g., accuracy of bone model reconstruction, implant placement) before integration with the surgeon's input. However, the specific results or detailed methodology for these standalone tests are not provided.

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

This information is not explicitly provided. Given the nature of a surgical planning and navigation system, ground truth would likely involve highly accurate imaging (e.g., high-resolution CT scans), anatomical measurements, and potentially expert anatomical review for validation of the reconstructed bone models and implant placements. However, the specific method for establishing this ground truth (e.g., expert consensus based on imaging, cadaveric studies with known anatomical landmarks) is not detailed in this summary.

8. The sample size for the training set

The document describes modifications to an existing device and its functionalities (e.g., extending planning to the humeral component, extending segmentation capabilities). It does not explicitly mention a deep learning or machine learning system that would require a distinct "training set" in the common AI sense. If the "software" relies on rule-based programming or image processing algorithms, a traditional "training set" might not be applicable in the same way as for a neural network. If there was any machine learning involved, the training set size is not provided.

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

As the existence or nature of a training set is unclear, the method for establishing its ground truth is also not provided.

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