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The Episealer Patellofemoral System is intended to be used in patients with osteoarthritis limited to the distal patellofemoral joint, patients with a history of patellar dislocation or patellar fracture, and those patients with failed previous surgery (arthroscopy, tibial tubercle elevation, lateral release, etc.) where pain, deformity or dysfunction persists.

The device is intended for cemented fixation.

The Episealer Patellofemoral System is a patient-individualized arthroplasty device which replaces a damaged patellofemoral joint. The subject device consists of two components:

• . Episealer PF
• Patellar Component ●

The Episealer PF component is implanted centrally in the trochlear area of the distal femur. The Patellar Component is implanted on the backside of the patella and articulates with the Episealer PF.

The provided text describes a 510(k) premarket notification for the Episealer® Patellofemoral System, an orthopedic device. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving a new device's safety and effectiveness through extensive clinical trials with pre-defined acceptance criteria.

As such, detailed information regarding acceptance criteria for a specific performance metric, and a study proving those criteria were met in the same way one would assess an AI/ML algorithm, is not available in this document.

However, I can extract the information that is present regarding performance testing, even though it's not structured around explicit acceptance criteria and corresponding performance metrics in the way you've requested for an AI model.

Here's an attempt to answer your request based on the provided text, highlighting what is included and what is explicitly not mentioned:

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

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics like "sensitivity", "specificity", "AUC", or "agreement" as would be seen for an AI/ML device. Instead, it describes general performance testing and analyses.

Summary of Performance Testing:

The document states: "Performance Testing Summary: Wear testing of the Episealer Patellofemoral System was performed. In addition, analyses of contact area and cantilever bending strength were conducted. A cadaver study was conducted to demonstrate that the components of the patient-matched Episealer Patellofemoral System can be accurately placed relative to the pre-planned position, with proper recession and engagement of the Episealer PF and Patellar components. The testing, engineering analyses, and cadaver study demonstrate the ability of the Episealer Patellofemoral System to perform as intended in the target population."

This section indicates that the device underwent standard engineering and mechanical testing relevant to orthopedic implants, as well as a cadaver study to verify surgical placement. However, specific acceptance criteria (e.g., "wear rate must be less than X," "contact area must be within Y range") and the quantitative results against those criteria are not provided in this 510(k) summary. The conclusion is a qualitative statement: "demonstrate the ability... to perform as intended."

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

• Sample Size for Test Set: Not specified in the provided summary. The text only mentions "wear testing," "analyses of contact area and cantilever bending strength," and "A cadaver study." The number of cadavers or individual test repetitions is not given.
• 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)

This is not applicable in the context of this device. A medical device like the Episealer Patellofemoral System undergoes engineering, mechanical, and cadaveric testing, not evaluation against expert-defined "ground truth" labels in the way an AI/ML diagnostic tool would.

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

This is not applicable for the type of testing described (mechanical testing, cadaver study).

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

This is not applicable. This is an orthopedic implant, not an AI-assisted diagnostic tool.

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

This is not applicable. This is an orthopedic implant, not an algorithm. The device itself is a standalone implant that is surgically placed.

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

The concept of "ground truth" as used for AI/ML evaluation does not directly apply here. Instead, performance is assessed through:

• Engineering measurements (wear, contact area, bending strength) against industry standards or internal specifications.
• Cadaveric surgical validation, where the "truth" would be successful implantation according to surgical protocols and demonstration of intended function.

8. The sample size for the training set

This is not applicable. This is not an AI/ML device that requires a training set.

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

This is not applicable. This is not an AI/ML device that requires a training set.

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