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MyKnee® R Pin Positioners are intended for use as anatomical pin positioners specific for a single patient anatomy in case of revision total knee replacement. They are designed based on CT images of a patient's knee and the primary TKA implant in situ to assist in positioning the total knee replacement components intraoperatively and in guiding the marking of bone prior to cutting. MyKnee® R Pin Positioners are intended for single use only. Resections are performed through the standard or revision cutting guides. These are positioned on the holes drilled through the MyKnee® R Pin Positioner blocks after removal of the primary total knee implant components and according to the surgeon's preoperative planning. MyKnee® R Pin Positioners are intended for use with GMK® Primary, GMK® SpheriKA, GMK® Revision, GMK® Hinge and their cleared indications for use.

MyKnee R Pin Positioners are a line extension to the currently marketed MyKnee Cutting Blocks (K093806) and MyKnee PPS-Pin Positioners (K170106). The MyKnee R blocks are single use, patient-specific pin positioner blocks designed based on CT images of a patient's knee and the primary TKA implant in situ. They are intended to position the pins for placement of the standard instruments according to the surgeon's preoperative surgical planning. The MyKnee R Pin Positioners are manufactured from medical grade nylon for sintering which is identical to the predicate devices. They are available in left and right configuration with sizes 1-6 for the both the femurs and the tibia and, as the predicate devices, they can be provided in both non-sterile version.

The provided FDA 510(k) summary for Medacta International S.A.'s MyKnee R Pin Positioners does not describe the specific acceptance criteria and detailed study that proves the device meets those criteria in the way typically expected for an AI/CADe device.

The MyKnee R Pin Positioners are patient-specific surgical guides, not an AI or CADe device in the sense of image analysis or diagnostic assistance. The performance data section explicitly states "No clinical studies were conducted," and the non-clinical studies primarily focus on software validation and cadaver testing validating the intended use, functional characteristics, and features of the physical guides themselves.

Therefore, many of the requested items related to AI/CADe performance (e.g., AUC, sensitivity/specificity, MRMC studies, ground truth establishment for training and test sets, number of experts, adjudication methods) are not applicable to this submission.

However, based on the information provided, we can infer and present what is available, noting the limitations of this document regarding the specific questions.

Here's an attempt to address the request based on the provided document:

Device: MyKnee R Pin Positioners

Device Type: Patient-specific anatomical pin positioners (surgical guides) for revision total knee replacement. This is NOT an AI/CADe device for image analysis or diagnosis.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of quantitative acceptance criteria and corresponding reported device performance values in the context of accuracy metrics (e.g., for an AI algorithm's diagnostic performance). Instead, the "performance data" section focuses on validation activities for the physical device's functionality and intended use.

Note: The document states that "testing activities were conducted to written protocols" and that the "slight differences between the subject and predicate devices do not raise new questions of safety and effectiveness." This implies that the acceptance criteria for these functional aspects were met, but the specific numerical targets or results are not detailed in this public summary.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: Not explicitly stated. The "Cadaver testing" refers to testing the physical device's function. The number of cadavers or specific test cases is not provided.
• Data Provenance: The document does not specify the country of origin for any data related to the cadaver testing. It is implied that the software processes CT images, but the origin of these CT images (e.g., patient demographics, retrospective/prospective collection) is not detailed as this is not an image analysis AI device.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Not Applicable in the AI/CADe sense. This submission is for a physical surgical guide. Ground truth for the geometric accuracy or fit of the guides would likely be established through engineering measurements and potentially surgeon feedback during cadaver trials, rather than expert consensus on image interpretation. The document does not specify the number or qualifications of any experts involved in the cadaver testing or validation of the guide's fit.

4. Adjudication Method for the Test Set

• Not Applicable. As there's no clinical imaging data interpretation or diagnostic task, there is no need for an adjudication method for establishing ground truth from image readers.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No MRMC study was done. The document explicitly states "No clinical studies were conducted." The device is a surgical guide, not an AI assistance for human readers in diagnostic tasks.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• No standalone algorithm performance study (in the AI sense) was done. The "software validation" performed is likely related to the accuracy of the software in converting CT images into the physical design of the pin positioners and ensuring the correct output for manufacturing, not for diagnostic performance.

7. Type of Ground Truth Used

• For the cadaver testing, the ground truth would inherently be physical measurements and functional assessment of the pin positioners in situ, confirming that they achieve their intended purpose of guiding pin placement accurately according to preoperative plans. No diagnostic "ground truth" (e.g., pathology, outcomes data, or expert consensus on image findings) is mentioned or relevant to the device's function.

8. Sample Size for the Training Set

• Not Applicable. This is not an AI/ML model that requires a training set for learning. The device's design is based on established principles of patient-specific instrumentation derived from CT images, not on training a machine learning model.

9. How the Ground Truth for the Training Set Was Established

• Not Applicable. Since there is no training set for an AI/ML model, this question is not relevant. The "ground truth" for the device's development lies in anatomical principles, engineering specifications, and the accuracy of the CT data used for individual patient planning.

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