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The Scorpio® NRG™ Knee System components are for use in total knee arthroplasty for painful, disabling joint disease of the knee resulting from degenerative arthritis, rheumatoid arthritis or post-traumatic arthritis; post-traumatic loss of knee joint configuration and function; moderate varus, valgus or flexion deformity in with the ligamentous structures can be returned to adequate function and stability; revision of previous unsuccessful knee replacement or other procedure. Additional indications for posterior stabilized components include: ligamentous instability requiring implant bearing surface geometries with increased constraint; and/or an absent or non-functioning posterior cruciate ligament.

The device includes femoral components and tibial insert components of a total knee system. These components are used for the replacement of the bearing and/or articulating surfaces of the distal femur, proximal tibia to relieve pain, instability and the restriction of motion due to degenerative bone disease, including osteoarthritis, rheumatoid arthritis, failure of other devices or trauma.

The provided document describes the Scorpio® NRG™ Knee System, but it is a premarket notification (510(k)) for a medical device and does not contain the information requested in the prompt regarding acceptance criteria and a study proving those criteria were met for an AI/ML device.

The document states that the proposed modification is to "Redesign the femoral and tibial insert component dimensions to provide for increased range of motion." The "Summary of Data" section (labeled as {1} in the input) briefly details the testing conducted for this knee system.

Here's an analysis of what is available and why it doesn't fit the request for AI/ML device study information:

Analysis of Provided Information:

1. Acceptance Criteria and Reported Device Performance:

   • The document mentions "risk analysis and Research and Development testing have been performed to demonstrate equivalence of the proposed products to the predicate devices."
   • Specific tests included "range of constraint testing, finite element analysis of contact stress/area and finite element analysis of femoral fatigue."
   • The reported performance is simply: "The results demonstrate equivalence."
   • This is not specific acceptance criteria in the format requested (e.g., sensitivity, specificity, AUC, or other quantitative metrics typically used for AI/ML performance), nor does it provide the detailed quantitative results of these tests.

2. Sample Size and Data Provenance:

   • Not applicable for AI/ML testing. The testing mentioned (range of constraint, finite element analysis) are mechanical engineering tests, not data-driven performance evaluations. No "test set" in the AI/ML sense is described.

3. Number of Experts and Qualifications:

   • Not applicable. No expert review for ground truth establishment is mentioned as this is not an AI/ML diagnostic or prognostic device.

4. Adjudication Method:

   • Not applicable. No adjudication process is relevant to the mechanical tests described.

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

   • No, this was not done. This type of study is relevant for diagnostic imaging AI/ML devices where human readers' performance is compared with and without AI assistance. The Scorpio® NRG™ Knee System is a physical joint replacement, not a diagnostic AI/ML tool.

6. Standalone (Algorithm Only) Performance:

   • No, this was not done. The device itself is a physical implant. The "finite element analysis" mentioned is a computational simulation (an "algorithm" in a very broad sense) used to design and verify the mechanical properties, not to perform a diagnostic or prognostic task in a standalone manner like an AI/ML algorithm.

7. Type of Ground Truth:

   • Not applicable. The "ground truth" for mechanical equivalence would be the physical properties and performance of the predicate device under specific mechanical loads and conditions. It's not "expert consensus," "pathology," or "outcomes data" in the context of an AI/ML evaluation.

8. Sample Size for Training Set:

   • Not applicable. There is no AI model or "training set" for physical knee implants.

9. How Ground Truth for Training Set was Established:

   • Not applicable.

In conclusion, the provided document K042343 describes a traditional medical device (a knee implant) and its regulatory submission. It does not contain any information relevant to the acceptance criteria or studies typically performed for AI/ML-driven medical devices.

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The Scorpio® NRG™ Knee System components are for use in total knee arthroplasty for painful, disabling joint disease of the knee resulting from degenerative arthritis, rheumatoid arthritis or post-traumatic arthritis; post-traumatic loss of knee joint configuration and function; moderate varus, valgus or flexion deformity in with the ligamentous structures can be returned to adequate function and stability; and/or revision of previous unsuccessful knee replacement or other procedure. Additional indications for the Posterior stabilized components include: Ligamentous instability requiring implant bearing surface geometries with increased constraint; and/or an absent or non-functioning posterior cruciate ligament.

The device includes femoral components and tibial insert components of a total knee system. These components are used for the replacement of the bearing and/or articulating surfaces of the distal femur, proximal tibia to relieve pain, instability and the restriction of motion due to degenerative bone disease, including osteoarthritis, rheumatoid arthritis, failure of other devices or trauma.

Acceptance Criteria and Study for Scorpio® NRG™ Knee

This document describes the acceptance criteria and the study performed for the Scorpio® NRG™ Knee.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

The provided document does not specify a distinct "test set" in the context of an AI/ML device or a clinical trial with a defined patient cohort. The "study" described is a series of engineering and risk analyses.

• Sample Size: Not applicable in the context of typical clinical study sample sizes. The tests involved specific component designs and their mechanical properties.
• Data Provenance: The data originates from internal "Research and Development testing" and "risk analysis" conducted by Howmedica Osteonics Corp. This means the data is internal to the manufacturer and is likely from laboratory settings and simulations rather than patient data. The provenance is therefore considered retrospective/simulation-based rather than from a specific country of origin in a clinical sense.

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

This is not applicable as the study described is a mechanical engineering evaluation and risk analysis, not a study requiring expert clinical adjudication of a test set in the traditional medical device sense. The "ground truth" for the mechanical tests would be the established engineering principles, material properties, and performance benchmarks for orthopedic implants.

4. Adjudication Method (Test Set)

Not applicable. There was no clinical test set requiring adjudication in the context of human interpretation or outcome assessment. The "adjudication" for the engineering tests would be adherence to pre-defined engineering standards and equivalence metrics.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The device is a knee implant, and the assessment was based on engineering and mechanical equivalence to predicate devices, not on the interpretation of medical images or data by multiple human readers, with or without AI assistance. Therefore, an effect size of human readers improving with AI vs. without AI assistance is not relevant or reported.

6. Standalone Performance Study (Algorithm Only)

No, a standalone (algorithm only) performance study was not done. This device is not an AI/ML algorithm; it is a physical knee implant.

7. Type of Ground Truth Used

The ground truth used for this study was engineering principles, material science properties, and performance data from predicate (already approved) devices. The goal was to establish "equivalence" to these existing, legally marketed devices. This involved:

• Benchmarking against known performance characteristics of the Scorpio® Total Knee and Scorpio® Scorpio-flex™ Tibial Inserts.
• Adherence to established mechanical testing protocols for orthopedic implants (e.g., range of constrained testing, stress analysis).

8. Sample Size for the Training Set

Not applicable. As this is a physical medical device (knee implant) and not an AI/ML algorithm, there is no "training set" in the sense of data used to train a model. The "training" in an engineering context would refer to material characterization, design iterations, and finite element analysis, which are not typically quantified by a "sample size" in this context.

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

Not applicable, as there is no "training set" for an AI/ML algorithm. For the engineering design and iteration process relevant to physical devices, the "ground truth" for development would be established through:

• Biomechanical research: Understanding the physiological loads and movements of the knee joint.
• Material science: Characterizing the properties of the materials used.
• Clinical experience and outcomes: Data from existing knee implants (predicates) informing design decisions to improve upon or match their performance.
• Regulatory standards: Ensuring the design meets established safety and performance requirements for knee implants.

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