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The Kyocera TRIBRID® Unicompartmental Knee System is indicated for use in patients with the following:

• Painful and disabled knee joint resulting from osteoarthritis, or idiopathic osteonecrosis, of . either the medial or lateral compartments.
• Correction of varus, valgus, or posttraumatic deformity. .
• As an alternative to tibial osteotomy in patients with unicompartmental NIDJD disease. .
• Revision procedures where other treatments or devices have failed. ●
  This device is intended for cemented use only.

KMTI's TRIBRID® Unicompartmental Knee System is a partial knee replacement prostheses intended for application with bone cement. Partial knee replacement components include femoral and tibial components. Femoral baseplates are CoCrMo Alloy while the Tibial implants are Ti6Al4V Alloy. The Tibial bearing inserts are made from Ultra-High Molecular Weight Polyethylene (UHMWPE) variations. Components are available in a variety of designs and size ranges intended for both primary and revision applications.

This document is a 510(k) summary for the Kyocera TRIBRID® Unicompartmental Knee System, a medical device. It focuses on mechanical, non-clinical performance data rather than AI/software performance. Therefore, many of the requested criteria related to AI device performance are not applicable.

Here's an analysis based on the provided document:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device are based on demonstrating that its mechanical performance is sufficient for its intended use and is substantially equivalent to legally marketed predicate devices. The document does not provide specific quantitative pass/fail values as acceptance criteria; rather, it lists the types of tests performed and concludes that the results support substantial equivalence.

Table of Acceptance Criteria and Reported Device Performance:

Study Details (based on the provided document)

Since the document describes a mechanical device and its non-clinical performance testing rather than an AI/software device, many of the typical questions for AI acceptance criteria and studies (e.g., sample size for test set, data provenance, number of experts, MRMC studies, standalone performance) are not applicable.

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not explicitly stated in this summary. Mechanical tests typically use a small number of samples (e.g., 3-6 or more, depending on the standard) to demonstrate compliance with a standard or to compare to predicate devices.
   • Data Provenance: Not applicable. This is in vitro mechanical testing, not human data.

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

   • Not applicable. Ground truth for mechanical testing is established by engineering specifications, material properties, and industry standards (e.g., ASTM, ISO), not by expert consensus on clinical data.

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

   • Not applicable. This is for mechanical testing, not interpretation of clinical imagery or data by experts. Test results are typically compared directly to standard requirements or predicate device performance.

4. 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:

   • Not applicable. This is a knee implant, not an AI-powered diagnostic or assistive tool.

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

   • Not applicable. This is a physical medical device, not an algorithm.

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

   • Mechanical Testing Standards: The "ground truth" for this type of device is compliance with industrial standards (e.g., ASTM, ISO) for mechanical properties, durability, and wear, and demonstrating substantial equivalence to predicate devices that have established safety and effectiveness through their historical use.

7. The sample size for the training set:

   • Not applicable. This is non-clinical mechanical testing, not a machine learning model.

8. How the ground truth for the training set was established:

   • Not applicable. As above, this is mechanical engineering testing.

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The MOTO PFJ is designed for cemented use in partial knee arthroplasty, if there is evidence of enough sound bone to seat and support the components. Patellofemoral replacement is indicated in the following cases:

Osteoarthritis, post-traumatic arthritis, polyarthritis, severe chondrocalcinosis of the patellofemoral joint.

Previously failed surgical attempts (i.e. arthroscopy, lateral release, tibial tubercle elevation, cartilage transplantation). History of patellar dislocation or fracture, resulting in cartilage degeneration of the patellofemoral joint.

Degeneration induced by dysplasia.

If the surgeon evaluates an unequivocal indication for replacement of the patellofemoral joint, with or without a patella resurfacing, which outweighs the risks associated with the surgery, PFJ replacement may be considered, particularly for young patients.

The MOTO PFJ System, subject of this submission, consists of:

• o Patello Femoral Joint, made of Cobalt-Chromium-Molybdenum alloy
• MOTO Patella, made of UHMWPE ●

The MOTO PFJ System is intended for replacement of the femoral trochlea of the patellafemoral joint affected by injury and/or disease process.

The MOTO PFJ System is intended for cemented use only.

The MOTO PFJ System may be used alone or in combination with the MOTO Partial Knee System Unicompartmental Prosthesis (Medial K161741 and Lateral K183029) and GMK UNI Prosthesis (K162084), to treat multiple conditions of patellofemoral and tibiofemoral regions of the natural knee. The Patello Femoral Joint component is designed to articulate with natural patella or with the dedicated MOTO Patella.

The provided document is a 510(k) summary for the Medacta International SA MOTO PFJ System. It describes the device, its indications for use, and a comparison to predicate devices, along with performance data. However, this document does not describe an AI/ML medical device. It details a knee joint patellofemoral polymer/metal semi-constrained cemented prosthesis.

Therefore, most of the requested information regarding acceptance criteria and studies for an AI/ML medical device (such as sample size for test sets, ground truth establishment, expert adjudication, MRMC studies, and standalone performance) is not applicable or present in this document.

The document focuses on non-clinical performance data to demonstrate substantial equivalence to predicate devices, which is typical for implantable medical devices of this type.

Here's an analysis of what is available and what is not for an AI/ML context:

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

• For an AI/ML device: This would typically involve metrics like sensitivity, specificity, accuracy, F1-score, AUC, etc., with pre-defined thresholds for acceptance.

• For this device: The acceptance criteria are based on mechanical and material performance, and biological safety. The "performance" is demonstrated through various non-clinical tests.

  • Acceptance Criteria (Implied from tests): The device must demonstrate sufficient fatigue endurance under walking and squatting scenarios, proper articular surface congruence, acceptable range of motion, comparable constraints to predicate, comparable contact pressure and areas to predicate, and acceptable wear behavior. Biocompatibility (pyrogenicity, LAL test) is also an acceptance criterion.
  • Reported Device Performance:
    • Non-Clinical Studies (Validation & Characterization):
      • Shape and Dimension Validation
      • Cadaveric workshop validation
      • Fatigue Endurance Test (Walking Scenario) - Test Reports A3
      • Fatigue Endurance Test (Squatting Scenario) - Test Reports A4
      • Articular Surface Fully Congruent - Test Report B1
      • Range of Motion - Test Report B2
      • Comparison within Medacta MOTO Patella and Zimmer NexGen Patella Constraints - Test Report B3
      • Comparison within Medacta MOTO Patella and Zimmer NexGen Patella in relation to Contact Pressure and Areas - Test Report B4
      • Wear Behaviour - Test Report B5
      • Bacterial Endotoxin Test (LAL test) - passed (equivalent to USP chapter )
      • Pyrogen test - passed (according to USP )

  Table (Reinterpreting for this hardware device):

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

• For an AI/ML device: This would refer to the number of cases/images in the independent test set.
• For this device: The "test set" here refers to the number of physical devices or components subjected to mechanical and biological testing. The document does not specify the exact number of samples for each test (e.g., how many femoral components were tested for fatigue), nor the specific origin of these physical samples beyond being manufactured by Medacta International SA (Switzerland). The testing is "prospective" in the sense that the tests were performed on newly manufactured devices.

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

• For an AI/ML device: This refers to human expert annotations.
• For this device: "Ground truth" is established by engineering specifications, biomechanical principles, and established international standards (e.g., ISO, ASTM, Pharmacopoeia) for material properties and mechanical performance. Experts would be engineers, material scientists, and toxicologists interpreting these results. The document does not list the number or qualifications of these experts analyzing the test results, as it's typically part of the company's internal quality system.

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

• For an AI/ML device: This refers to resolving disagreements between human annotators.
• For this device: Not applicable. Performance is measured against physical and chemical standards, not subjective human interpretations.

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

• For an AI/ML device: Refers to studies evaluating AI's impact on human performance.
• For this device: Not applicable, as this is a physical implant, not a diagnostic AI. The document explicitly states: "No clinical studies were conducted."

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

• For an AI/ML device: Refers to algorithm performance metrics without human interaction.
• For this device: Not applicable. There is no algorithm. The performance is the inherent mechanical and biological performance of the device itself.

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

• For an AI/ML device: Refers to the definitive determination of the condition being detected/diagnosed.
• For this device: The 'ground truth' is based on engineering and material science standards and protocols. For example, fatigue endurance is measured against a specific load cycle standard, wear is measured according to a wear test standard, and biocompatibility is measured against pharmacopoeia standards. There is no "disease state" ground truth for this type of device.

8. The sample size for the training set

• For an AI/ML device: Refers to the data used to train the algorithm.
• For this device: Not applicable, as there is no AI/ML algorithm requiring a training set. The "training" for such devices would be the iterative design and manufacturing process, and knowledge gained from previous designs, but not in the context of data used to train a machine learning model.

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

• For an AI/ML device: Refers to how training data was annotated or labeled.
• For this device: Not applicable.

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