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Smith & Nephew's VISIONAIRE Patient Matched Cutting Blocks are intended to be used as patient-specific surgical instrumentation to assist in the positioning of total knee replacement components intra-operatively and in guiding the marking of bone before cutting provided that anatomic landmarks necessary for alignment and positioning of the implant are identifiable on patient imaging scans.

The Smith & Nephew VISIONAIRE Patient Matched Cutting Blocks are intended for use with the following existing Smith & Nephew, Inc. Knee Systems and their cleared indications for use:

• Genesis II Knee System
• Legion Knee System
• Journey BCS Knee System
• Journey II Knee System

The Smith & Nephew VISIONAIRE Patient Matched Cutting Blocks are intended for single use only.

The subject of this premarket notification is to seek FDA clearance of a software component to be used in the design and manufacture of the VISIONAIRE Patient Matched Cutting Blocks. Also within this notification is an extension of the subject device's shelf life. Patient Matched Cutting Blocks were previously cleared for market via premarket notifications- K082358, K130708 and K170282.

The provided document is a 510(k) premarket notification for Smith & Nephew Patient Matched Cutting Blocks. It primarily focuses on demonstrating substantial equivalence to previously cleared predicate devices, rather than presenting a study with specific acceptance criteria and performance data for a new device.

The document states:

• "No new mechanical testing was performed. No implants or new blocks are being introduced in this premarket notification. There are no changes to the block design, packaging, material composition or manufacturing of Smith & Nephew VISIONAIRE Patient Matched Cutting Blocks as a result of these changes described in the premarket notification."
• "Clinical data was not needed to support the safety and effectiveness of the subject device(s)."
• "Software verification and validation testing was completed in line with FDA's guidance document entitled, 'General Principles of Software Validation; Final Guidance for Industry and FDA Staff,' dated January 11, 2002. A review of this testing has demonstrated that there are no new issues related to the safety and effectiveness of the subject device and the software will perform as intended."
• "Based on the documentation within this premarket notification, there are no new issues related to the safety and effectiveness of the subject device. Clinical data was not needed to support the safety and effectiveness of the subject device."

Therefore, the document does not contain the information requested regarding acceptance criteria and a study proving device performance in the way typically associated with a new clinical or AI performance study. Instead, it relies on demonstrating that there are no significant changes to the previously cleared device and that software verification meets internal standards.

Based on the provided text, I cannot complete the requested tables and sections because the document does not describe a new study with explicit acceptance criteria and corresponding device performance metrics for the reasons stated above.

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The patient-specific Bodycad Unicompartmental Knee System (Bodycad UKS) is indicated for unicompartmental knee arthroplasty (UKA) in patients with advanced knee osteoarthritis (OA) of the medial compartment with evidence of adequate healthy bone to support the implanted components. Candidates for unicompartmental knee replacement include those with:

· joint impairment due to osteoarthritis or traumatic arthritis of the knee,

· varus deformity of the knee, and

· as an alternative to tibial osteotomy in patients with unicompartmental OA.

The patient-specific Bodycad UKS components fit within an envelope of dimensions that are specific to each patient. The Bodycad UKS femoral component and tibial baseplate are intended for cemented fixation. The Bodycad screws must be used for fixation of the femoral and tibial components.

The Bodycad Unicompartmental Knee System (Bodycad UKS) is a patient-specific prosthesis that consists of femoral and tibial implants for replacement of the medial tibiofemoral compartment of the knee. The patient-specific femoral and tibial components and single-use cutting guides are manufactured from CAD and CAM files generated from validated Bodycad software, which are based on MRI images of the patient's knee and input from the surgeon. The femoral component is fabricated from wrought Cobalt-Chrome alloy (CoCr) with a centrally located peg and provision for ancillary fixation with a CoCr bone screw. The tibial implant consists of a Titanium (Ti) alloy baseplate with one to three posterior spikes and provision for ancillary fixation with a Tit alloy bone screw. The polyethylene insert is fabricated from Ultra-High-Molecular-Weight- Polyethylene (UHMWPE), compression molded and machined GUR 1020. The tibial-femoral articulation is an unconstrained design. This device is for cemented use.

Materials: CoCrMo (ASTM F1537-11) for the femoral component. Ti6Al4V ELI (ASTM F136-13) for the tibial component, UHMWPE (F648-14) for the tibial insert

The provided text is a 510(k) Summary for the Bodycad Unicompartmental Knee System. It describes the device, its indications for use, and a comparison to predicate devices, as well as a list of performance tests conducted.

However, the document does not contain the information requested in the prompt regarding acceptance criteria and a study proving the device meets those criteria, specifically concerning data provenance, expert adjudication, MRMC studies, or training/test set details for an AI/algorithm-driven device performance study.

The "Performance Data" section lists various mechanical and material tests performed on the knee system components (fatigue strength, range of motion, constraint testing, screw strength, contact pressure, etc.), as well as cadaver laboratory testing for surgical technique and implant fit, and evaluation of single-use cutting guides. This section focuses on the physical and functional characteristics of the medical device itself, not on the performance of a diagnostic algorithm or AI system for which the requested details about ground truth, expert readers, and sample sizes would be relevant.

The closest mention of software is that the patient-specific implant components and cutting guides are manufactured from CAD-CAM models created from the patient's MRIs and input from the surgeon, using proprietary software. It states: "This software, and off-the-shelf software, were validated per the FDA guidance document 'Guidance for the content of premarket submissions for software contained in medical devices'." However, this refers to validation of the manufacturing software workflow, not a diagnostic AI system predicting disease state or lesion characteristics.

Therefore, based on the provided text, I cannot answer the specific questions about acceptance criteria and a study proving the device meets them in the context of an AI/algorithm-driven diagnostic performance study.

The document details the regulatory clearance of a medical device (a knee implant system), not an AI software as a medical device (SaMD). The acceptance criteria mentioned are related to the physical performance, material integrity, and surgical fit of the implant, not the diagnostic performance of an AI.

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MyKnee Cutting Blocks are intended to be used as anatomical cutting blocks specific for a single patient anatomy to assist in the positioning of total knee replacement components intraoperatively and in guiding the marking of hone before cutting.

MyKnee Cutting Blocks are intended for use with GMK Total Knee System and its cleared indications for use.

MyKnee Cutting Blocks are intended for single use only.

MyKnee Cutting Blocks are designed and manufactured from patient imaging data so that the cutting blocks match the patient's anatomy. The MvKnee Cutting blocks for the patient are used with Medacta's existing GMK Total Knee System.

This 510(k) summary for the MyKnee Cutting Blocks describes performance testing; however, it does not provide detailed acceptance criteria or a specific study designed to "prove" the device meets acceptance criteria in the way one might expect for a software or AI/ML device.

Instead, the documentation focuses on demonstrating substantial equivalence to predicate devices through a combination of non-clinical testing and design validation. Given the nature of the device (patient-matched cutting blocks for total knee replacement), the "acceptance criteria" discussed are largely related to manufacturing quality, material properties, and dimensional accuracy, rather than clinical efficacy as might be assessed with AI.

Here's an analysis based on the provided text, addressing your points where information is available:

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions that "MyKnee Cutting Blocks were tested as part of design verification to written protocols with pre-defined acceptance criteria. The testing met all acceptance criteria..." However, the specific acceptance criteria and their corresponding reported performance values are not detailed in this summary.

Based on the text, the following types of performance were evaluated, implying associated acceptance criteria existed:

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

The document mentions "a cadaver laboratory" for design validation. This implies the use of human cadavers as a test set. However:

• Sample Size: The exact number of cadavers used is not specified.
• Data Provenance: This would be from cadaveric studies, likely performed in a laboratory setting. It is retrospective in the sense that the cadavers were not living patients undergoing surgery, but rather preserved specimens. The country of origin is not specified but would typically be the country where the manufacturing or validation studies were performed (likely Switzerland where Medacta is based, or potentially the US if outsourced).

3. Number of Experts and Qualifications

This information is not provided in the summary. While the study involved surgical tools, there is no mention of experts establishing a ground truth for a test set in the context of diagnostic performance.

4. Adjudication Method

This information is not provided in the summary. This type of adjudication is typically relevant for studies involving human interpretation (e.g., radiologists reviewing images), which doesn't appear to be the primary focus of the performance testing described here.

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

No, an MRMC comparative effectiveness study was not done with human readers and AI assistance. This device is a physical cutting block, not an AI diagnostic or assistive tool in that traditional sense. The "patient-matched" aspect refers to the manufacturing process creating a custom physical tool from patient imaging data, not an AI providing clinical interpretations.

6. Standalone (Algorithm Only) Performance

The closest analog to "standalone performance" for this device would be the accuracy and precision of the MyKnee Cutting Blocks themselves in matching the patient's anatomy and guiding cuts. This was part of the "dimensional accuracy and precision" testing, and "software tools used to manufacture the MyKnee Cutting Blocks were validated for their intended use." However, it's not an algorithm only in the sense of a standalone AI model; it's the accuracy of the manufactured physical product derived from digital data. No specific performance metrics are given.

7. Type of Ground Truth Used

For the cadaver laboratory design validation, the ground truth would likely involve:

• Physical measurements: Direct measurements of the bone cuts and component positioning on the cadaveric knees after using the MyKnee Cutting Blocks, compared against surgical plans derived from the patient imaging data.
• Expert surgical assessment: Evaluation by surgeons to confirm if the blocks facilitate accurate and appropriate resections as intended.

8. Sample Size for the Training Set

The phrase "training set" is typically used for machine learning models. For this device, the "training" for the manufacturing process comes from the engineering design, material science, and manufacturing protocols. There isn't a "training set" of data in the AI/ML sense. The "patient imaging data" for each individual patient is used to design their specific cutting blocks, not to train a general model.

9. How Ground Truth for Training Set Was Established

Given that there is no "training set" in the AI/ML sense, this question is not applicable to the MyKnee Cutting Blocks device as described. The "ground truth" for the overall design and manufacturing process would be established through engineering specifications, material properties testing, and verification of the manufacturing process (as implied by process reproducibility and software validation).

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The Zimmer Patient Specific Instruments System is intended to be used as a surgical instrument to assist in the positioning of Total Knee Replacement components intra-operatively and in guiding the marking of bone before cutting. The Zimmer Patient Specific Instruments System is to be used with Zimmer NEXGEN CR-FLEX fixed bearing, Zimmer NEXGEN LPS-FLEX fixed bearing and Zimmer Gender Solutions Natural – Knee Flex fixed bearing prostheses families only. The Zimmer Patient Specific Instruments are intended for single use only.

The Zimmer Patient Specific Instruments System consists of a software component, Zimmer Patient Specific Instruments Planner and a hardware component, Zimmer Patient Specific Instruments and is designed to assist the surgeon in the placement of total knee replacement components for Zimmer Gender Solutions Natural – Knee Flex fixed bearing prostheses families.

The provided text describes the Zimmer Patient Specific Instruments System, which includes a software component (Zimmer Patient Specific Instruments Planner) and a hardware component (Zimmer Patient Specific Instruments). The system is designed to assist surgeons in the placement of total knee replacement components.

However, the 510(k) summary does not contain detailed information about specific acceptance criteria or a comprehensive study report proving the device meets particular benchmarks. The performance data section is very brief, stating:

"Non-clinical tests were performed to assess the safety and effectiveness of the device. Testing verified that the accuracy and performance of the system is adequate to perform as intended."

And for clinical data: "Not applicable."

Therefore, based solely on the provided text, a full table of acceptance criteria and a detailed study description cannot be generated. The document primarily focuses on regulatory submission details, device description, and substantial equivalence to a predicate device, rather than a detailed performance study.

Here's an attempt to answer the questions based on the limited information available in the provided document, acknowledging significant gaps:

Acceptance Criteria and Device Performance Study (Based on Limited Information)

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

Based on the provided text, specific quantitative acceptance criteria or detailed performance metrics are not explicitly stated. The document only provides a high-level qualitative statement regarding performance.

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

The document does not specify any sample sizes for test sets or data provenance. The "non-clinical tests" are mentioned without further detail regarding the nature of data used.

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)

The document does not provide any information about experts used for establishing ground truth or their qualifications. The "non-clinical tests" are not described with this level of detail.

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

The document does not provide any information about adjudication methods.

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

The document does not describe an MRMC study, nor does it mention any comparison of human readers with or without AI assistance. The system is described as assisting the surgeon, but no comparative effectiveness data is presented. The device is a planning and guiding system, not an AI for interpretation that would involve "human readers."

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

The software component (Zimmer Patient Specific Instruments Planner) is used pre-operatively by a qualified surgeon to "inspect, fine-tune and approve the pre-surgical plan." This indicates a human-in-the-loop process. The document does not describe a standalone algorithm-only performance evaluation for the planning software. The hardware component (Zimmer Patient Specific Instruments) is a patient-specific template, which is by nature a standalone manufactured item based on the approved plan.

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

The document does not specify the type of ground truth used for its "non-clinical tests." For a device that assists in positioning, ground truth would typically relate to anatomical accuracy of the generated plan or the delivered template's guidance, but no details are provided.

8. The sample size for the training set

The document does not mention a training set sample size. Given the submission date (2009) and the description, it's unclear if "AI" in the modern sense of machine learning was heavily involved in a way that would necessitate a large training set as understood today. The software generates a pre-surgical plan based on MRI data, which could imply rule-based algorithms or image processing, rather than deep learning requiring extensive training data.

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

The document does not provide any information about a training set or how its ground truth might have been established.

Conclusion:

The provided 510(k) summary for K093533 is very high-level regarding performance data. It explicitly states "Clinical data: Not applicable" and provides only a generic statement about non-clinical tests verifying "adequacy" of accuracy and performance. It lacks the detailed information typically found in study reports for specific acceptance criteria, sample sizes, ground truth establishment, or comparative effectiveness studies.

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