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Wright's PROPHECY® Pre-Operative Navigation Alignment Guides 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. The PROPHECY® Pre-Operative Navigation Alignment Guides are intended for use with Wright's ADVANCE® and EVOLUTION™ Total Knee Systems and their cleared indications for use, provided that anatomic landmarks necessary for alignment and positioning of the implant are identifiable on patient imaging scans. The PROPHECY® Pre-Operative Navigation Alignment Guides are intended for single use only.

PROPHECY® Pre-Operative Navigation Alignment Guides are patient-specific guides created to fit the contours of the patient's distal femoral and tibial plateau anatomy. The guides are designed and manufactured from patient imaging data (MRI, CT), and are available in two versions: alignment and alignment and resection. The guides are made from biocompatible nylon, and the resection slots are biocompatible stainless steel. The PROPHECY® Guides serve as a alternative to traditional alignment instrumentation used with Wright's ADVANCE® and EVOLUTION™ Total Knee Systems, and thereby reduce the overall number of surgical steps required during total knee arthroplasty. The guides serve to position and align the ADVANCE® or EVOLUTION™ implants in a comparable position to traditional instrumentation.

Here's an analysis of the acceptance criteria and study information for the PROPHECY® Pre-Operative Navigation Alignment Guides, based on the provided text:

Important Note: The provided 510(k) summary (K10359B) is from 2011 and describes a Class II device. For such devices, the regulatory requirements for "performance data" are often less stringent than for higher-risk devices or more recent AI/Machine Learning devices, which typically require extensive validation studies with specific endpoints and statistical power. The information provided heavily emphasizes equivalence to a predicate device rather than de novo performance validation.

Acceptance Criteria and Reported Device Performance

Study Details

The provided document describes performance data rather than a single, comprehensive "study" in the typical clinical trial sense. The data presented focuses on establishing substantial equivalence to a predicate device (K093405) and supporting the expansion of indications for use to include the EVOLUTION™ knee system.

1. Sample Size used for the test set and the data provenance:

   • Test Set (for performance data): The document specifically mentions "Cadaver testing by end users analyzing placement location and orientation."
     • Sample Size: Not specified. The number of cadavers used is not detailed.
     • Data Provenance: Retrospective (cadaver-based testing). The country of origin is not specified but is presumably the US, given the submission to the FDA.

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

   • Number of Experts: Not specified. The phrase "Cadaver testing by end users" implies that surgeons or other relevant medical professionals were involved.
   • Qualifications of Experts: Not explicitly stated. The term "end users" suggests surgeons or surgical technicians experienced in knee arthroplasty, but their specific qualifications (e.g., years of experience, board certification) are not provided.

3. Adjudication method for the test set:

   • Adjudication Method: Not specified. With "cadaver testing by end users," it's unclear if there was any formal adjudication process or if a single assessment per cadaver was performed by the "end user."

4. 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, an MRMC comparative effectiveness study was not done. This device is a physical surgical guide for total knee replacement, not an AI-based diagnostic tool requiring human-in-the-loop performance measurement. Therefore, the concept of "human readers improve with AI vs without AI assistance" is not applicable here.

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

   • Standalone Performance: The core function of the device (patient-specific surgical guides designed from imaging data) is fundamentally a "standalone algorithm" in terms of its design and manufacturing process. The "Detailed software descriptions and documentation" and "Repeatability testing across design engineers" relate to the internal consistency and accuracy of this design process.
   • However, the performance data listed explicitly includes "Cadaver testing by end users," indicating that human interaction (surgical use) was part of the overall assessment of the final product's effectiveness. So, while the guide design is a standalone process, its validation involved human use in a simulated environment.

6. The type of ground truth used:

   • For the cadaver testing: The "ground truth" would be the measured achieved alignment and component placement in the cadaver after using the guides, compared to pre-defined surgical plans or ideal anatomical alignments. This is a form of empirical measurement against an intended outcome.
   • For the design process (software/repeatability): The ground truth would be the consistency of the design outputs (guide geometry) against repeated inputs or design specifications.

7. The sample size for the training set:

   • Training Set Sample Size: Not applicable/not specified. This device is not an AI/Machine Learning model in the modern sense that requires a "training set" for learning patterns from data. The guides are designed using patient-specific imaging data (MRI, CT) and pre-defined algorithms/rules embedded in the software, not learned from a training set.

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

   • Ground Truth for Training Set: Not applicable. As explained above, there isn't a "training set" and associated ground truth in the context of an AI/ML model for this device. The software relies on established biomechanical principles and anatomical landmarks identifiable on patient imaging scans as its "knowledge base" for generating the guides.

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