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The GALILEO CAS/NAV TKR System is intended for computer-assisted navigation of the GALILEO-CAS instruments with the aim to position the implants of the TC-PLUS™ Solution Knee (K000666 - S/E 10/13/00) optimally for the patient.

The GALILEO CAS/NAV TKR System is a system for computer-assisted navigation of the GALILEO-CAS instruments with the aim to position TC-PLUS™ Solution Knee prostheses optimally for the patient. The collection of patient data required for this occurs exclusively operatively. A preoperative CT scan is not necessary. The connection between patient and computer is made via two infrared transmitters (active locators), which are attached to the distal femur and to the proximal tibia. Two passive locators are used for the spatial arrangement of the instruments. A manual navigation key button is used for scanning the anatomical bone features (landmarks). An infrared camera locates the locators as well as the manual key button and is connected to the computer. The computer-assisted Galileo CAS Total Knee Replacement System supports the operating surgeon performing the total knee replacement procedure. The system takes the femur-cutting device to the required position and enables resections with high accuracy and flexibility.

The provided text describes the GALILEO CAS/NAV TKR System, a computer-assisted navigation system for total knee replacement procedures. However, the document does not contain sufficient information to populate the requested table regarding acceptance criteria and performance data. Specifically, it states that "Biomechanical tests have been performed. The test results were equivalent to other similar implants and are sufficient for in vivo loading," but it does not provide specific acceptance criteria or quantitative performance metrics from these tests.

Therefore, many of the requested fields cannot be answered from the provided text.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size: Not specified.
• Data provenance: Not specified (e.g., country of origin, retrospective/prospective). The tests were "Biomechanical tests," implying laboratory or bench testing rather than human clinical data.

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

• Not applicable/Not specified as the performance data appears to be from biomechanical tests, not a clinical study involving expert assessment of patient outcomes.

4. Adjudication method for the test set

• Not applicable/Not specified.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned. The device is a surgical navigation system, not an imaging interpretation AI.

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

• The performance data mentioned ("Biomechanical tests") would likely represent the standalone performance of the system as it assists in guiding instruments. However, the exact methodology is not detailed. The system supports the operating surgeon, implying human-in-the-loop during actual surgery, but the "biomechanical tests" likely assessed the automated or guided aspects.

7. The type of ground truth used

• For the "Biomechanical tests," the ground truth would likely be engineering specifications, physical measurements, or established biomechanical standards for implant loading and surgical accuracy. No specific type of ground truth (e.g., pathology, outcomes data) is mentioned, suggesting laboratory testing.

8. The sample size for the training set

• The document does not mention any "training set" for an AI or machine learning model. The system is described as computer-assisted navigation, which typically uses pre-programmed algorithms and patient-specific data acquired intraoperatively, rather than a learning algorithm that requires a separate training set.

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

• Not applicable, as no training set for an AI/ML model is mentioned.

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