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The MyKnee UNI-ST blocks are based on CT or MRI scans of the knee and they are intended for use as anatomical cutting blocks or pin positioners specifically designed for each patient to assist in the intraoperative positioning of the tibial unicompartmental component and to guide the marking of the bone before cutting.

The MyKnee UNI-ST blocks are intended for use with MOTO Partial Knee systems when the clinical evaluation complies with clear indications for use. MyKnee UNI-ST blocks are intended for single use only.

MyKnee UNI-ST are a line extension to the currently marketed MyKnee Cutting Blocks (K093806), MyKnee PPS-Pin Positioners (K170106) and MyKnee R Pin Positioners (K220705).

The MyKnee UNI-ST are single use, patient-specific tibial cutting blocks which allows the surgeon to carry out the tibial resection according to the preoperative 3D plan based on CT or MRI images of the patient's knee.

The MyKnee UNI-ST cutting blocks are available in left and right medial configuration with a range of sizes covering the whole MOTO tibial implant portfolio for which they are intended for use with.

Identically to the predicate devices, the MyKnee UNI-ST are manufactured from medical grade nylon for sintering and they can be provided in both non-sterile and sterile version.

This is a 510(k) premarket notification for a medical device called "MyKnee UNI-ST," which are patient-specific tibial cutting blocks used in knee replacement surgery. The submission claims substantial equivalence to a legally marketed predicate device.

The provided text does not contain acceptance criteria or a study that rigorously proves the device meets specific performance criteria in terms of accuracy or clinical outcomes. Instead, it focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing and leveraging existing data.

Here's an analysis based on the information provided, highlighting the absence of some requested details:

1. Table of acceptance criteria and the reported device performance:

   • Acceptance Criteria: Not explicitly stated as quantifiable metrics. The "performance data" section describes the types of tests performed rather than specific pass/fail criteria with numerical thresholds.
   • Reported Device Performance: The document only states that "performance testing was conducted to written protocols" and that "the comparison of technological characteristics and performance data provided within the submission supports the substantial equivalence of the subject devices respect to the predicate devices." No specific numerical results (e.g., accuracy in mm or degrees) from these tests are provided in this summary.

   Acceptance Criterion (Implied/General)	Reported Device Performance (Summary)
   Functionality for intended use	Verified in sawbone workshop.
   Mechanical Resistance	Dimensional analysis showed no new worst-case compared to predicate.
   Accuracy of placement & resection	Validated in cadaver testing.
   Biocompatibility	Leveraged data from predicate devices.
   Shelf-life	Leveraged data from predicate devices.
   Sterilization	Leveraged data from predicate devices.

2. Sample size used for the test set and the data provenance:

   • Sawbone workshop: Sample size not specified.
   • Dimensional analysis: Sample size not specified.
   • Cadaver testing: Sample size not specified.
   • Data provenance: Not specified (e.g., country of origin). The testing seems to be internal ("conducted to written protocols"). It is not explicitly stated if it was retrospective or prospective, though cadaver and sawbone testing are typically prospective experiments.

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

   • Not specified. The document does not describe a process for establishing ground truth by external experts for the performance tests mentioned (sawbone, dimensional analysis, cadaver). The validation was likely performed by internal R&D or clinical engineers.

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

   • Not applicable/Not specified. This typically applies to studies involving human interpretation or clinical endpoints adjudicated by experts. The performance tests described (sawbone, dimensional analysis, cadaver) are objective measurements without an adjudication process for consensus on observed outcomes.

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. This device is a physical cutting block for surgery, not an AI-assisted diagnostic or interpretive tool. Therefore, an MRMC study is not relevant or mentioned.

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

   • Not applicable. This is a physical device, and its function involves direct interaction with a surgeon (human-in-the-loop for its application). The "accuracy of placement & resection" validated in cadaver testing is a form of performance evaluation for the device itself, but it's not an "algorithm-only" performance in the AI sense.

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

   • For the sawbone and cadaver testing, the "ground truth" would likely be derived from objective measurements taken post-procedure against the pre-operative plan generated by the MyKnee system (which the cutting blocks implement). This involves comparing the actual bone cut/pin placement to the planned bone cut/pin placement, using precise measurement tools. It's an engineering ground truth based on design specifications and pre-operative planning, rather than a clinical ground truth like pathology or patient outcomes.

8. The sample size for the training set:

   • Not applicable. This device is a patient-specific physical cutting block. It does not use machine learning or AI that requires a "training set" in the computational sense. Each block is custom-designed based on an individual patient's CT or MRI scan.

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

   • Not applicable, as there is no "training set" for an AI or machine learning model. The "ground truth" for the design of each individual MyKnee UNI-ST block is the patient's anatomical data from their CT or MRI scan, which is used to generate the 3D model for the custom block.

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