(118 days)
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:
| Category | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Biocompatibility | Compliance with ISO 10993 for external communicating devices with limited contact. | Met applicable ISO 10993 requirements. |
| Dimensional Accuracy | Specific tolerances for accuracy based on patient imaging data. | Met acceptance criteria (details not provided). |
| Dimensional Precision | Consistency in dimensions before and after sterilization. | Met acceptance criteria (details not provided). |
| Mechanical Testing | Sufficient strength and durability for intended surgical use. | Met acceptance criteria (details not provided). |
| Cleanliness | Adherence to defined cleanliness standards after factory cleaning. | Met acceptance criteria (details not provided). |
| Shipping Test | Integrity of packaged device after shipping. | Met acceptance criteria (details not provided). |
| Process Reproducibility | Consistent manufacturing process yielding equivalent products. | Assessed and met acceptance criteria (details not provided). |
| Software Validation | Software tools used for manufacturing function as intended. | Validated for intended use. |
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).
§ 888.3560 Knee joint patellofemorotibial polymer/metal/polymer semi-constrained cemented prosthesis.
(a)
Identification. A knee joint patellofemorotibial polymer/metal/polymer semi-constrained cemented prosthesis is a device intended to be implanted to replace a knee joint. The device limits translation and rotation in one or more planes via the geometry of its articulating surfaces. It has no linkage across-the-joint. This generic type of device includes prostheses that have a femoral component made of alloys, such as cobalt-chromium-molybdenum, and a tibial component or components and a retropatellar resurfacing component made of ultra-high molecular weight polyethylene. This generic type of device is limited to those prostheses intended for use with bone cement (§ 888.3027).(b)
Classification. Class II.