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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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