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The Stryker Facial iD Plating System is intended for osteotomy, stabilization of maxillofacial fractures and reconstruction in adults and adolescents (age 12 and higher).

Specific Indications for Use:
-Orthognathic surgery
-Reconstructive maxillofacial surgery
-Mandible and maxillofacial trauma surgery.

The Stryker Facial iD Plating System is intended for osteotomy, stabilization and rigid fixation of maxillofacial fractures and reconstruction in adults and adolescents (age 12 and higher), with the specific Indications for Use in orthognathic surgery, reconstructive maxillofacial surgery, and mandible and maxillofacial trauma surgery.

The Subject Device plate(s) are additively manufactured patient-specific plates, and the patientspecific design of the plates allows certain features to be configured to meet the individual needs of each patient. The Subject Device plate(s) are provided with a Design Proposal, an electronic Instruction for Use (IFU) and an optional Anatomical Model.

Here's an analysis of the acceptance criteria and study information based on the provided text, focusing on what is present and what is not:

Acceptance Criteria and Device Performance (Based on provided text)

The document primarily states that the subject device (Stryker Facial iD Plating System, K193143) is substantially equivalent to its primary predicate device (Stryker Facial iD Plating System, K182305). The core of the acceptance criteria lies in demonstrating that the subject device performs as intended and is comparable to the predicate device, especially after software updates.

Detailed Study Information (Based on provided text)

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

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

   • Test Set Sample Size:
     • For "Performance Bench Testing," "Cleaning and Sterilization," and "Shipping and Handling," the document states that data from the Primary Predicate Device (K182305) are "valid for the Subject Device." This implies that the sample sizes from the predicate device's testing were utilized, but the specific numbers are not provided in this document.
     • For the "end-user test validation in a cadaver lab," no specific sample size (number of cadavers or test cases) is mentioned.
   • Data Provenance: Not specified for any of the inherited or new testing. The manufacturing facility is in Freiburg, Germany, which might imply the origin of some data, but it's not explicitly stated for any of the studies. The studies are essentially "retrospective" for the predicate device's data being applied to the new device. The cadaver lab testing would be "prospective" for the specific evaluation.

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):

   • The document mentions "end-user test validation of the Subject Device in a cadaver lab." This implies evaluation by medical professionals. However, the number of experts, their qualifications, or how a "ground truth" was established from their feedback is not provided.

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

   • Not specified. The document vaguely refers to "end-user test validation" but offers no details on assessment or adjudication methods for this or any other performance evaluation.

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 comparative effectiveness study was not done. The device is a "Facial iD Plating System" (physical plates and associated software for design), not an AI-assisted diagnostic or interpretative system in the context of human "readers." The "software updates" mentioned are for "automated bone thickness measurements and visualization" and "plate design process improvements," aiding in the design of the physical plates, not directly assisting human "readers" in interpreting medical images or data for diagnosis.

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

   • The document states "Software Verification and Validation testing were performed for the software updates" according to internal procedures and IEC 62304. This implies standalone testing of the software components. The new features like "automated bone thickness measurements and visualization" and "plate design process improvements" would have undergone testing in a standalone capacity to ensure they met their specified requirements. However, the specific metrics and results of this standalone testing are not detailed beyond the statement that they "met all predefined acceptance criteria."

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

   • For the inherited bench testing from the predicate device, the ground truth would have been based on engineering specifications and established mechanical test standards.
   • For the "end-user test validation in a cadaver lab," the ground truth likely involved expert assessment of surgical handling, fit, and intended performance by the medical professionals using the system, although how this "ground truth" was formally established (e.g., expert consensus) is not specified.
   • For the software verification and validation, the ground truth would be against defined software requirements and expected outputs/behaviors.

8. The sample size for the training set:

   • This device is a physical plating system with associated design software, not a machine learning or AI algorithm that requires a "training set" in the conventional sense of supervised learning. Therefore, a "training set" as understood in AI/ML model development is not applicable here and is not mentioned in the document. The software updates are described as process improvements and automation of measurements, suggesting a rule-based or calculative approach rather than a learning algorithm trained on data.

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

   • As a "training set" is not applicable, this question is not relevant to the information provided.

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The Stryker Facial iD Plating System is intented for osteotomy, stabilization of maxillofacial fractures and reconstruction in adults and adolescents (age 12 and higher).

Specific Indications for Use:

-Orthognathic surgery

-Reconstructive maxillofacial surgery

-Mandible and maxillofacial trauma surgery.

The Stryker Facial iD Plating System (Subject Device) is intended for osteotomy, stabilization and rigid fixation of maxillofacial fractures and reconstruction in adults and adolescents (age 12 and higher), with the specific Indications for Use in orthognathic surgery, reconstructive maxillofacial surgery, and mandible and maxillofacial trauma surgery.

The Subject Device plate(s) are additively manufactured patient-specific plates, and the patientspecific design of the plates allows certain features to be configured to meet the individual needs of each patient. The Subject Device plate(s) are provided with a printed version of the surgeonapproved Design Proposal, an electronic Instruction for Use (IFU) and an optional Anatomical Model. Additionally, the Subject Device is compatible with a separately provided Marking / Cutting Guide accessory.

The provided document describes the Stryker Facial iD Plating System, a custom-designed maxillofacial plating system. The following information is extracted regarding its acceptance criteria and the studies performed:

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

2. Sample size used for the test set and the data provenance:
   The document focuses on bench testing and validation studies, not clinical studies involving patient data. Therefore, the concepts of "test set sample size" and "data provenance" (country of origin, retrospective/prospective) as typically applied to AI/diagnostic device studies using patient data are not directly applicable here. The "test sets" refer to physical samples (e.g., plates, accessory models) used in engineering and biocompatibility tests. The number of samples for each specific test is not detailed, but it is implied that sufficient samples were tested to meet the respective standards.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   This information is not applicable as the studies described are bench tests and validation studies for a medical device (bone plate system), not a diagnostic AI device requiring expert-established ground truth from patient data. The "ground truth" for these tests are the established standards and specifications (e.g., ISO, ASTM).

4. Adjudication method for the test set:
   Not applicable, as no human expert adjudication of patient cases or images is described. The results of the tests were evaluated against predefined standards and criteria.

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 comparative effectiveness study was not performed. This document describes the clearance of a physical medical device (bone plating system), not an AI/diagnostic software.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
   While "Software output validation" was conducted for the design and manufacturing workflow, this is not a standalone performance evaluation of a diagnostic or assistive AI algorithm in the typical sense. It validates the output of the design software, ensuring it accurately translates digital models to manufacturing data. There's no performance metric comparing the algorithm's output to a ground truth established by humans for diagnostic purposes.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   For bench testing and validation, the ground truth is based on:

   • Established engineering standards: e.g., ASTM F382 for metallic bone plates, ASTM STP 731 for fatigue limits, ASTM D642 for compressive resistance, ASTM F1886M for seal integrity, ASTM E2314 for cleaning effectiveness.
   • Biocompatibility standards: e.g., ISO 10993 series and FDA guidance.
   • Sterilization standards: e.g., ISO 17665-1, ISO 14937.
   • Internal design specifications and user requirements: For software output validation and compatibility testing.

8. The sample size for the training set:
   Not applicable. This device is a physical bone plating system, not an AI model that requires a training set of data. The "patient-specific design" does involve software to design the plates for individual patients, but this is a design workflow, not a machine learning model that learns from a training set in the conventional sense.

9. How the ground truth for the training set was established:
   Not applicable, as there is no mention of a "training set" for an AI model in this 510(k) submission.

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