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The Overture Resurfacing Uni Knee Resurfacing System is intended to be used in the partial replacement of the articulating surfaces of the knee in instances where, due to compartmental degenerative disease, post-traumatic degenerative disease, previous tibial condyle or plateau fractures, deformity, or previous arthroplasty, only the one side of the joint is affected. This device is intended to be used with bone cement.

The Uni Knee Resurfacing System is comprised of femoral implants, and a set of ancillary instruments. The femoral implants are titanium and feature a spherical polished articulating surface with a titanium nitride (TIN) coating. The tibial implants are comprised of a titanium tibial tray with an integrated ultra-high molecular weight polyethylene (UHMWPE) tibial insert. The femoral implants and tibial trays utilize cemented pegs and porous titanium bone-contacting surfaces to allow for fixation. The femoral implant articulates against the tibial insert in a non-constrained manner.

The implants are provided in a variety of configurations and sizes to accommodate various patient anatomy. Femoral implants are offered in oblong and round configurations. The oblong femoral implants are offered in lengths ranging 25-40mm and diameters ranging 17.5-27.5mm, and the round femoral implants are offered in diameters ranging 17.5-27.5mm. The tibial implants are offered in diameters ranging 17.5-22.5mm.

The implants are provided gamma sterilized and individually packaged. The ancillary instruments are provided non-sterile and are to be sterilized by the end user.

This document is a 510(k) summary for the Uni Knee Resurfacing System. It focuses on demonstrating substantial equivalence to a predicate device based on design, materials, and performance testing, rather than an AI/ML-based device requiring extensive clinical study for performance metrics like accuracy, sensitivity, or specificity.

Therefore, the requested information regarding acceptance criteria and a study proving the device meets these criteria in the context of AI/ML performance (e.g., sample size, data provenance, expert ground truth, MRMC studies) is not applicable to this submission.

The "Performance Testing" section (page 4) details bench testing conducted for the Uni Knee Resurfacing System, which typically involves mechanical and material evaluations, not clinical performance metrics for an AI algorithm.

To specifically address the prompts:

1. A table of acceptance criteria and the reported device performance: Not applicable. The document describes engineering and material-based performance tests, not AI-related performance metrics. The conclusion states that these tests "indicated no new risks and demonstrated substantial equivalence in performance compared to a legally marketed predicate." Specific numerical acceptance criteria for each bench test are not provided in this summary.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. This document refers to bench testing of physical implants, not an AI algorithm's performance on clinical data.

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): Not applicable. Ground truth as typically defined for AI validation (e.g., expert consensus on medical images) is not relevant to this device's testing.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

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: Not applicable. This is not an AI-assisted device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable in the context of AI. The "ground truth" for the performance testing would be established by objective measurements and scientific standards for material properties, mechanical strength, wear, etc., as per the listed ASTM and other relevant engineering standards.

8. The sample size for the training set: Not applicable. There is no AI algorithm being trained.

9. How the ground truth for the training set was established: Not applicable.

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The UNI KNEE SURGETICS Navigation System is intended for use during stereotaxic surgery to aid the surgeon in locating anatomical structures and aligning the endoprostheses with the anatomical structures. It is specifically indicated for : - UniKnee Arthroplasty .

As the equivalent Surgetics ORTHO KNEELOGICS Navigation System and TOTAL KNEE SURGETICS Navigation System, the UNI KNEE SURGETICS navigation System consists of the following major components and subsystems: - The Surgetics Station or the NanoStation, consisting of a mobile computer . system and an optical localizer - Ancillary instruments and reflective markers used for reference and . registration - UNI KNEE SURGETICS software . The main modifications to the predicate devices concern the internal architecture of the software, allowing more easily to integrate new implants and to better adapt the workflow to different surgical techniques. New instruments adapted to new implants are also incorporated.

The provided document, a 510(k) summary for the UNI KNEE SURGETICS Navigation System, describes performance testing. However, it does not detail specific quantitative acceptance criteria or a study designed to prove the device met such criteria in the manner requested.

Instead, the document states that the system was tested to assess that no new safety and efficiency issues were raised compared to its predicate devices. The conclusion is based on an assessment that the "accuracy and performance of the system was adequate for its intended use and not reduced in comparison to the predicate devices." This is a qualitative statement of equivalence rather than a report of specific metrics meeting predefined thresholds.

Therefore, many of the requested details, such as a table of acceptance criteria and reported performance, sample sizes for test sets, expert qualifications, adjudication methods, MRMC study results, and detailed ground truth establishment for training sets, are not present in 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 (Test Set): Not specified.
• Data Provenance: Not specified. The study was a "non clinical setting (bench testing, specimen)". This implies the use of anatomical specimens or phantoms, rather than human patient data, and was conducted by the manufacturer (Praxim).

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication method for the test set:

• Adjudication Method: 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:

• MRMC Study: No. This device is a surgical navigation system, not an AI-assisted diagnostic tool. No human "readers" are involved in the performance testing described.

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

• Standalone Performance: The described "non clinical setting (bench testing, specimen)" appears to be a standalone performance assessment of the system's accuracy and performance. However, specific metrics are not provided. The system is designed to aid the surgeon, implying a human-in-the-loop during actual clinical use, but the testing described focuses on the system's inherent capabilities.

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

• Type of Ground Truth: Not specified beyond "bench testing, specimen." For a navigation system, ground truth would typically refer to highly accurate measurements of actual anatomical positions or implant alignments, often established using precise measurement tools (e.g., CMM, optical trackers or highly accurate physical models) during bench testing.

8. The sample size for the training set:

• Training Set Sample Size: Not applicable/not specified. The document describes a "navigation system" and software modifications, but not a machine learning model that would typically require a training set in the conventional sense. The "training" here refers to software development and validation, not model training.

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

• Ground Truth for Training Set: Not applicable/not specified.

In summary, the provided 510(k) summary focuses on demonstrating substantial equivalence to predicate devices through qualitative assessment of "accuracy and performance," rather than presenting a detailed, quantitative study against explicit acceptance criteria as would be expected for a novel device or an AI/ML product.

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Intended for partial replacement of the articulating surfaces of the knee when only one side of the joint is affected due to compartmental primary degenerative or post-traumatic degenerative disease, previous tibial condyle or plateau fractures, deformity or revision of previous arthroplasty. The device is a single use implant intended for implantation with bone cement.

The additional components that are the subject of this Special 510(k) submission are the tibial inserts and baseplates. These components are to be used with the femoral components of the Uni Knee cleared in K020741.

The tibial inserts of this submission are the similar the tibial components in K020741 with the addition of a snap feature on the non-articulating side. This snap feature is meant to be used with the tibial baseplate. The tibial inserts are manufactured from UHMWPE that conforms to ASTM F648 and are available in 5 sizes (35-45 mm) and 3 thicknesses (6-8 mm).

The tibial baseplates are manufactured from CoCr alloy that conforms to ASTM F75 and are available in 5 sizes (35-45 mm) and 2 configurations (left medial/right lateral and right medial/left lateral).

The provided text describes a submission for "Uni Knee Additional Components" to the FDA, but it does not contain any information regarding acceptance criteria, device performance metrics, or the details of a study that proves the device meets specific criteria.

The 510(k) summary focuses on:

• Device identification: Submitter, device name (Uni Knee Additional Components), common name, classification, and predicate devices.
• Device description: Details the tibial inserts (UHMWPE, snap feature, 5 sizes, 3 thicknesses) and tibial baseplates (CoCr alloy, 5 sizes, 2 configurations).
• Intended use: Partial replacement of articulating knee surfaces for specific conditions, for single use with bone cement.
• Basis for Substantial Equivalence: States similarity in design, materials, and indications to a previously cleared device (K020741).

The FDA's response letter (K022437) confirms that the device is substantially equivalent to legally marketed predicate devices. This indicates that the FDA found the device to be as safe and effective as existing devices, based on the information provided in the 510(k) submission.

Therefore, I cannot provide the requested information about acceptance criteria or a study proving device performance because it is not present in the provided document. The document is a regulatory submission for substantial equivalence, not a detailed performance study report.

To address the specific points you requested, if such information were available, here's how it would typically be presented:

Hypothetical Response (if the information were available in the provided text):

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size (Test Set): [e.g., N=10 implants for fatigue testing, N=5 animal models for biocompatibility]
• Data Provenance: [e.g., In-vitro lab testing conducted in the USA, Retrospective clinical data from European clinics, Prospective animal study conducted in Canada]

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

• Number of Experts: [e.g., 3 orthopedic surgeons]
• Qualifications: [e.g., Each with over 15 years of experience in knee arthroplasty, Board-certified and affiliated with major academic medical centers]

4. Adjudication Method for the Test Set

• Adjudication Method: [e.g., 2+1 (two experts agree, or a third expert resolves disagreement), Consensus panel, Majority rule, None (if ground truth is objective, like pathology reports)]

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

• MRMC Study Conducted?: [Yes/No]
• Effect Size (Human Readers with AI vs. Without AI): [e.g., "Human readers demonstrated a 15% improvement in diagnostic accuracy with AI assistance (p < 0.01)," or "Mean reduction of 30 seconds per case in reading time," or "No statistically significant improvement observed."]

6. Standalone (Algorithm Only) Performance Study

• Standalone Study Conducted?: [Yes/No]
• Standalone Performance Metrics: [e.g., Sensitivity: 92%, Specificity: 88%, Area Under the Curve (AUC): 0.95 (for a diagnostic algorithm)]

7. Type of Ground Truth Used

• Type of Ground Truth: [e.g., Histopathology, Surgical confirmation, Long-term patient outcomes (e.g., 5-year re-operation rates), Expert consensus based on clinical imaging, Objective mechanical measurements]

8. Sample Size for the Training Set

• Training Set Sample Size: [e.g., 10,000 images, 500 patient records, N=100 implant designs used for finite element analysis model training]

9. How Ground Truth for the Training Set was Established

• Ground Truth Establishment (Training Set): [e.g., "Annotated by a team of 5 resident radiologists under the supervision of a fellowship-trained musculoskeletal radiologist using a standardized annotation protocol," "Derived from electronic health records using natural language processing and validated by manual chart review," "Determined by predefined engineering specifications and validated through mechanical testing."]

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