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The ConforMIS Unicondylar Knee System is intended for use in patients with:

• joint impairment due to osteoarthritis or . traumatic arthritis of the knee
• . Previous tibial condyle or plateau fracture, creating loss of function
• valgus or varus deformity of the knee .

The ConforMIS Unicondylar Knee System is intended for use with cement.

The ConforMIS Unicondylar Knee System ("iUni") is a device developed from patient CT scans or MR images to replace one compartment of the knee condyles. It is unconstrained in the anteroposterior and mediolateral directions and allows internal/external rotation between the femoral and tibial components. Movement is limited by the ligaments and other soft tissues surrounding the device. The device is designed to match the patient's unique normal anatomy.

The provided document describes the ConforMIS Unicondylar Knee System ("iUni"), a device developed from patient CT scans or MR images to replace one compartment of the knee condyles. The submission focuses on demonstrating substantial equivalence to predicate devices, particularly a version of the implant designed based on CT scan data.

Here's an analysis of the acceptance criteria and study findings based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a distinct "test set" in the context of an AI/ML device. Instead, it refers to a "design verification procedure" where implants designed using MRI data were compared to those designed from CT scan data for "the same patient." The exact number of patients or imaging sets (sample size) used in this comparison is not provided.

The provenance of this data (e.g., country of origin, retrospective or prospective) is also not specified.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not describe the use of human experts to establish "ground truth" for evaluating the performance of the device in the way it would for an AI/ML imaging device. The comparison is based on the physical parameters of the implants themselves.

4. Adjudication Method for the Test Set

No adjudication method (e.g., 2+1, 3+1) is mentioned, as the evaluation did not involve expert interpretation or subjective assessment of the device's output in the typical sense of an AI/ML study.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

No, a multi-reader, multi-case (MRMC) comparative effectiveness study was not done. The study described is a design verification comparing physical parameters of implants derived from different imaging modalities, not a study of human reader performance with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The core of the described study (design verification comparing implants derived from MRI vs. CT) is effectively a standalone (algorithm-only) performance assessment in that it evaluates the output of the design process, which is driven by algorithms interpreting imaging data to create the implant. However, it's not a "standalone" performance evaluation in the context of an AI-driven diagnostic system producing a specific output (like disease detection) for human review. It assesses the consistency of the physical implant parameters based on different input imaging.

7. The Type of Ground Truth Used

The ground truth used in this context is the physical parameters of the produced implants. The acceptance criteria are that implants designed from MRI data must have the "same physical parameters" as those designed from CT scan data for the same patient. This implies a comparison of quantitative measurements of the implant's design.

8. The Sample Size for the Training Set

The document does not specify a training set sample size. The device is described as being "developed from patient CT scans or MR images," suggesting that these images are used in the design process, but it does not detail a separate "training set" in the machine learning sense. The focus is on the development of the device from these images, rather than the training of an AI model.

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

Since a "training set" in the AI/ML context is not explicitly described, neither is the method for establishing its ground truth. The design process presumably involves established engineering principles and anatomical measurements derived from imaging.

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The ConforMIS metal backed tibial component is intended for use with the ConforMIS Unicondylar Knee Repair System and the ConforMIS BiCompartmental Knee repair Systems, in patients with:

• joint impairment due to osteoarthritis or traumatic arthritis of the knee .
• previous tibial condyle or plateau fracture, creating loss of function .
• . valgus or varus deformity of the knee

The ConforMIS metal backed tibial component is intended only for use with bone cement.

The ConforMIS Metal Backed Tibial Component is to be used with either the ConforMIS Unicondylar Knee Repair System or the BiCompartmental Knee Repair ConforMIS System to provide the surgeon with an alternative tibial component in the event that prefers to use a metal backed he/she component rather than an all polyethylene component. It consists of a cobalt-chrome alloy (CoCrMo) tray and a polyethylene insert. The X/Y dimensions of the device (i.e. the twodimensional shape or "footprint"). are designed to conform to the patient's anatomy as closely as possible based on images (MRI or CT scan) of the patient's knee. It is available with UHMEPE inserts with minimal thickness from 6 to 14 mm.

The provided text is a 510(k) summary for the ConforMIS Metal Backed Tibial Component ("mBT"). It describes a device intended for knee repair and seeks to establish substantial equivalence to predicate devices, rather than proving specific performance against acceptance criteria through a clinical study.

Therefore, many of the requested sections about acceptance criteria, study details, human reader performance, and ground truth establishment cannot be found in this document. This document focuses on demonstrating substantial equivalence through non-clinical testing and comparison to existing devices.

Here's a breakdown of what can and cannot be answered based on the provided text:

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

• Cannot be provided. This document does not specify quantitative acceptance criteria or report device performance against such criteria. It states that "Testing completed as part of the design verification procedure for the ConforMIS Metal Backed Tibial Component found this device to be as safe and effective as the predicate devices," implying that it met internal design verification standards, but no specific metrics are given.

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

• Cannot be provided. The document states, "Non-clinical Performance and Conclusions: Testing completed as part of the design verification procedure for the ConforMIS Metal Backed Tibial Component found this device to be as safe and effective as the predicate devices. further confirming substantial equivalence." This refers to non-clinical (likely bench or mechanical) testing, not a clinical test set with patient data. No details on sample size or data provenance for any "test set" are present.
• The document explicitly states, "Clinical Performance: Clinical data and conclusions are not necessary to demonstrate substantial equivalence."

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)

• Cannot be provided. No clinical test set or ground truth establishment by experts is mentioned, as the submission relies on non-clinical performance and equivalence to predicates.

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

• Cannot be provided. No clinical test set or adjudication method is mentioned.

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

• Cannot be provided. This document describes a medical device (a knee implant component), not an AI algorithm or a diagnostic tool that would typically involve human readers or MRMC studies.

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

• Cannot be provided. This document is for a medical device (implant component), not an algorithm.

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

• Cannot be provided. No clinical ground truth is mentioned, as the submission focuses on non-clinical performance and substantial equivalence.

8. The sample size for the training set

• Cannot be provided. This document is for a physical medical device. It does not mention any "training set" in the context of an algorithm. The device design is customized "based on images (MRI or CT scan) of the patient's knee," but this refers to patient-specific design, not an algorithmic training process.

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

• Cannot be provided. No training set or ground truth establishment is mentioned.

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