Search Results

The Newclip Patient-matched instrumentation non sterile PSI is indicated to be used as a surgical instrument to assist in pre-operative planning and/or in guiding surgical instruments in High Tibial Medial Opening or Closing Wedge Osteotomy, Distal Femoral Medial or Lateral Closing Wedge Osteotomy. Distal Femoral Lateral Opening Wedge Osteotomy and Distal Femoral Medial or Lateral Derotational Osteotomy, when the anatomic landmarks necessary for pre-operative planning can be clearly identified on the patient's radiographic images (i.e., computed tomography (CT)). Each PSI is designed to be compatible with implants from the Newclip High Tibial Osteotomy System or the Newclip Activmotion Range.

The Newclip Patient-matched instrumentation non sterile PSI are patient-matched devices (PSI Guides). The PSI Guides are surgical drilling/cutting quides that are additively manufactured (3D printed) and are designed to match the patient's anatomy. They are intended to assist in pre-operative planning and/or in guiding the marking of bone and/or guiding surgical instruments in non-acute, non-joint replacing osteotomies around the knee. They are single-use devices provided non sterilization by health care professionals before use. The Newclip Patient-matched instrumentation non sterile PSI can be used to facilitate implantation of the Newclip Activmotion Range devices. The primary purpose of this 510(k) notification is to add PSI Guides for closingwedge and derotation osteotomies.

The provided text describes the Newclip Patient-matched instrumentation non sterile PSI, a device used in osteotomy procedures around the knee. The 510(k) summary focuses on demonstrating substantial equivalence to predicate devices, but it does not include explicit acceptance criteria, detailed study designs, or specific performance metrics typically expected for AI/Machine Learning-enabled devices.

The device is described as "patient-matched instrumentation," which implies that the design process involves processing patient imaging data (CT scans) to create custom surgical guides. However, the document does not elaborate on the algorithmic aspects of this patient-matching process or how its accuracy is evaluated in a standalone or comparative study setting.

Given the information provided, here's an attempt to answer the questions, highlighting the limitations due to the lack of specific details concerning AI/ML evaluation as per your request:

1. Table of acceptance criteria and the reported device performance

Based on the provided document, explicit quantitative acceptance criteria for the "Newclip Patient-matched instrumentation non sterile PSI" are not stated. The performance is broadly described in terms of "precision and accuracy" in simulated-use cadaver surgeries. No specific numerical thresholds or target values are provided.

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

The document mentions "simulated-use cadaver surgeries" for performance verification. However:

• Sample Size for Test Set: Not specified. The number of cadavers or surgical approaches evaluated is not provided.
• Data Provenance: Not specified, but given the nature of cadaver studies, it would be laboratory-based rather than from real-world patient data. The study is described as "simulated-use," suggesting a prospective evaluation within a controlled environment.

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

The document states "Surgeon evaluation of precision and accuracy."

• Number of Experts: Not specified.
• Qualifications of Experts: Identified as "Surgeon," but no further details such as specialty (e.g., orthopedic surgeon), years of experience, or specific board certifications are provided.

4. Adjudication method for the test set

The document only states "Surgeon evaluation." There is no mention of an adjudication method (such as 2+1, 3+1 consensus, or independent review) for establishing ground truth or assessing outcomes in the simulated-use cadaver surgeries.

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

• MRMC Study: Not mentioned. The study described is a "Surgeon evaluation of precision and accuracy based on simulated-use cadaver surgeries." This focuses on the performance of the device itself in guiding surgical actions, rather than an MRMC study comparing human reader performance with and without AI assistance for tasks like diagnosis or planning.
• Effect Size of AI/Human Improvement: Not applicable, as no such MRMC study is described. The device's role is to guide surgical instruments, not to assist human readers in interpreting images or making diagnostic decisions.

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

The document describes the "patient-matched instrumentation" as derived from CT images and used to guide surgical instruments. This implicitly means there is computational work done to generate these custom guides. However, no specific standalone algorithm-only performance study results are provided. The evaluation mentioned is "Surgeon evaluation of precision and accuracy based on simulated-use cadaver surgeries," which represents a human-in-the-loop scenario where the surgeon uses the device. A standalone validation of the algorithm's ability to precisely match anatomy or generate optimal guide designs is not explicitly detailed.

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

For the "precision and accuracy" evaluation in cadaver surgeries, the ground truth or reference standard against which the device's performance was measured is not explicitly stated. It's likely an assessment by the evaluating surgeon(s) based on intra-operative observation or post-operative measurements of the osteotomy characteristics (e.g., angles, cuts) against the pre-operative plan, but the method for establishing this ground truth is not detailed.

8. The sample size for the training set

The document does not provide any information regarding a training set sample size. This suggests that if an algorithm is used for patient-matching guide design, it was either not developed using a distinct "training set" in the machine learning sense, or the details of its development and validation outside of the simulated-use cadaver study are not included in this summary. The process likely involves computational design based on individual patient CT data rather than a generalizable AI model trained on a large dataset.

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

Not applicable, as no information about a training set is provided.

Ask a specific question about this device