(149 days)
The Zimmer Patient Specific Instruments System is intended to be used as a surgical instrument to assist in the positioning of Unicompartmental Knee Replacement components intra-operatively and in guiding the marking of bone before cutting and to guide cutting of the bone provided that anatomic landmarks necessary for alignment and positioning of the implant are identifiable on patient imaging scans.
The Zimmer Patient Specific Instruments System is to be used with Zimmer Unicompartmental High Flex Knee System 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 unicompartmental knee replacement components for Unicompartmental High Flex Knee System prosthesis.
The provided text describes a 510(k) summary for the Zimmer Patient Specific Instruments System, outlining its intended use, technological characteristics, and performance data. However, it does not contain specific acceptance criteria with numerical targets, nor does it detail a study that explicitly proves the device meets such criteria in a quantitative manner as typically requested for AI/ML device evaluations.
Instead, the performance data section generally states that "Software validation and accuracy performance testing by means of saw bone models, cadaveric trials and guide deformation verification after sterilization were performed to assess the safety and effectiveness of the device." And "These tests verified that the accuracy and performance of the device is adequate to perform as intended."
This suggests a general validation rather than a study with defined acceptance criteria and corresponding quantitative results. The submission predates modern requirements that would typically necessitate detailed quantitative performance metrics for AI/ML components.
Given the information provided, I cannot populate the table or answer all requested questions with specific quantitative details. I will indicate where the information is not available in the provided text.
Here's an attempt to answer the questions based on the available text:
1. A table of acceptance criteria and the reported device performance
No specific numerical acceptance criteria or quantitative performance metrics are provided in the document. The performance data section broadly states: "These tests verified that the accuracy and performance of the device is adequate to perform as intended."
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified in detail (e.g., target accuracy, precision) | "Accuracy and performance of the device is adequate to perform as intended" based on software validation, saw bone models, cadaveric trials, and guide deformation verification after sterilization. (Specific numerical results are not provided). |
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
The text mentions "saw bone models" and "cadaveric trials" as part of non-clinical tests.
- Sample size: Not specified.
- Data provenance: "Saw bone models" and "cadaveric trials" imply laboratory-based testing, not clinical data from patients. The country of origin for this testing is not specified. It is inherently prospective in the context of the testing (i.e., conducted specifically for the submission).
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. The performance testing described (saw bone models, cadaveric trials, guide deformation) does not involve expert-established ground truth in the context of diagnostic interpretation or clinical decision-making. It's a physical or software accuracy validation.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
Not applicable. This device is not an AI/ML diagnostic interpretation system that would typically require expert adjudication for ground truth. The validation relies on measurements from physical models and cadavers.
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
No. An MRMC study is not mentioned. The device assists surgeons in positioning knee replacement components and guiding cuts, rather than providing interpretations for human readers to evaluate.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
The device explicitly includes a "human-in-the-loop" step: "The software is then used pre-operatively by a qualified surgeon to inspect, fine-tune and approve the pre-surgical plan." Therefore, a standalone (algorithm only) performance, separate from the surgeon's review and approval, is not described or indicated as being evaluated as such. The system is designed to be used with a surgeon's oversight.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
For the non-clinical tests:
- For software validation and accuracy testing: Likely engineering specifications, direct measurements on saw bone models or cadavers (e.g., comparing planned cuts/positions to actual cuts/positions, or anatomical landmarks), and measurements related to guide deformation.
- The text states "These tests verified that the accuracy and performance of the device is adequate to perform as intended," implying comparison against an established engineering or anatomical ground truth, even if not explicitly detailed.
8. The sample size for the training set
Not applicable. The description does not indicate an Artificial Intelligence/Machine Learning (AI/ML) component that would typically require a "training set" in the modern sense. The "Zimmer Patient Specific Instruments Planner" is described as a "software component" that "generates a pre-surgical plan based on MRI imaging data." This sounds like rule-based software or traditional image processing, not a machine learning model that undergoes a distinct training phase.
9. How the ground truth for the training set was established
Not applicable, as there is no indication of an AI/ML component requiring a training set with established ground truth in the context of machine learning.
§ 888.3520 Knee joint femorotibial metal/polymer non-constrained cemented prosthesis.
(a)
Identification. A knee joint femorotibial metal/polymer non-constrained cemented prosthesis is a device intended to be implanted to replace part of a knee joint. The device limits minimally (less than normal anatomic constraints) translation in one or more planes. It has no linkage across-the-joint. This generic type of device includes prostheses that have a femoral condylar resurfacing component or components made of alloys, such as cobalt-chromium-molybdenum, and a tibial component or components made of ultra-high molecular weight polyethylene and are intended for use with bone cement (§ 888.3027).(b)
Classification. Class II.