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The MedCAD® AccuShape® Titanium Patient-Specific Cranial Implant is designed individually for each patient and intended to correct defects / replace bony voids in the cranial skeleton.

The MedCAD® AccuShape® Titanium Patient-Specific Cranial Implant is a preformed non-alterable cranioplasty plate that cannot be altered or reshaped at the time of surgery and is designed to be implanted in a patient to repair a skull defect.
The subject device is composed of commercially pure (CP) Grade 2 titanium per ASTM F67. The manufacturing process is subtractive manufacturing (CNC milled) from models created and developed from patient specific CT Scan Data. The software used in this process is identical to the software used in the predicate device (K110684). The device is designed to have, as requested by the physician, drainage holes over the defect void area, fixation holes over an onlay area, and retractions and other features that fall within the approved design envelope. All designs must be approved by the physician prior to manufacture.

The provided document describes the MedCAD AccuShape Titanium Patient-Specific Cranial Implant and its substantial equivalence to a predicate device (MedCAD AccuShape PEEK Patient Specific Cranial Implant) based on non-clinical performance testing.

It is important to note that this document does not describe an AI/ML-driven device or study parameters typical for such devices (e.g., ground truth establishment for a training set, human reader studies, or expert consensus on clinical data). The device described is a physical cranial implant, and the study referenced in the document is a series of non-clinical performance tests designed to assess the physical and mechanical properties of the implant, not its diagnostic or predictive accuracy in an AI context.

Therefore, many of the requested bullet points, particularly those pertaining to AI/ML device evaluation (like sample size for test/training sets of data, number of experts for ground truth, MRMC studies, standalone performance), are not applicable to the information provided in this document.

However, I can extract information relevant to the device's acceptance criteria and the non-clinical performance testing performed for this physical device.

Here's an interpretation of the "acceptance criteria" and "study" as presented for a physical medical device, rather than an AI/ML diagnostic:

Device: MedCAD AccuShape Titanium Patient-Specific Cranial Implant (K220357)

Purpose of the "Study" (Non-Clinical Performance Testing): To demonstrate the substantial equivalence of the MedCAD AccuShape Titanium Patient-Specific Cranial Implant to its predicate device (MedCAD AccuShape PEEK Patient Specific Cranial Implant, K110684) by evaluating its physical and mechanical properties.

1. A table of acceptance criteria and the reported device performance

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

• Sample Size for Test Set:
  • Evaluation of Fit Testing: "3 large defect predicate historical cases (K110684)" were used to generate "worst case CT data". The number of manufactured implants tested is implied to be at least 3 (one for each case). The phrase "All samples" in the result suggests a specific number of manufactured implants were produced and tested, but the exact number isn't quantified beyond the 3 cases used for input data.
  • Comparative Strength: "Identical subject and predicate devices" were used, implying at least one (and likely more for statistical significance, though not stated) of each type (titanium and PEEK) for comparative testing.
  • Screw Fixation: Not explicitly stated, but implies multiple tests to determine "at least as strong as" criteria.
  • MR Compatibility: At least one device (or representative sample) would be tested.
• Data Provenance: The "worst case CT data" for the Evaluation of Fit testing came from "3 large defect predicate historical cases (K110684)". This suggests a retrospective use of previously acquired clinical data (CT scans) from actual patients. The country of origin is not specified but is implicitly USA, given this is an FDA submission.

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

• This concept is not directly applicable in the context of this device's non-clinical testing. The "ground truth" (or reference standard) is based on engineering specifications, material properties, and established test methodologies (e.g., ASTM standards, previous FDA-cleared device performance).
• For the "Evaluation of Fit Testing," "qualified inspectors" performed the evaluation. Their qualifications (e.g., years of experience, specific certifications) are not detailed beyond "qualified".

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

• Not applicable. Adjudication methods are typically used in clinical studies involving multiple human readers interpreting medical images, where discrepancies need to be resolved. This document describes physical, non-clinical tests.

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

• Not applicable. This device is a physical implant, not an AI/ML diagnostic tool, and no human reader study was performed.

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

• Not applicable. This is not an AI algorithm. The manufacturing software is mentioned (same as predicate device), but its performance in terms of design output is assessed through the physical device tests (e.g., Evaluation of Fit), not as a standalone AI model.

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

• The "ground truth" for these tests is based on:
  • Engineering Specifications/Standards: e.g., ASTM F2503-20 for MR compatibility.
  • Predicate Device Performance: For comparative strength, the performance of the legally marketed predicate device (K110684 PEEK implant) served as the benchmark.
  • Established Biomechanical Principles: For screw fixation, comparison to "axial pullout forces measured in prior testing of FDA-cleared neuro screws in an established cortical bone model" serves as the reference.
  • 3D Digital Models/Physical Prototypes: For "Evaluation of Fit," the 3D digital model of the implant and representative anatomical models served as the reference for fit.

8. The sample size for the training set

• Not applicable. This device is not an AI/ML system that requires a "training set" of data in the machine learning sense. The manufacturing process uses patient-specific CT scan data as input for design, but this is not a training set for an AI model.

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

• Not applicable, as there is no AI/ML training set in this context.

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