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The MedCAD® AccuPlate® 3DTi Patient-Specific Plating System is intended for use in the stabilization, fixation, and reconstruction of the maxillofacial / midface and mandibular skeletal regions in adolescents (greater than 12 to 21 years of age) and adults.

The MedCAD AccuPlate® 3DTi Patient-Specific Plating System is a metal bone plate used in conjunction with commercially available, non-locking metal bone screws for the fixation to bone in orbital, midface / maxillofacial, and non-continuity mandibular operations. The design and dimensions of each plate within the envelope specification is based upon the patient's anatomical data (CT scan, CBCT scan, or MRI), and the intended anatomy to be fixated as determined from input provided by the surgeon. The subject device is not intended to be bent or modified in surgery. If for any reason the surgeon chooses not to use the subject device in surgery, they may use any of the commercially available plates to complete the surgery. The subject device is additively manufactured from Ti-6AL-4V Extra Low Interstitial (ELI) titanium alloy, provided non-sterile, must be sterilized prior to use, and is intended for single use only. Plates are fastened to bone using commercially available non-locking bone screws with diameters ranging from 1.5mm to 2.7mm and lengths ranging from 3.5mm to 22mm.

Here's a summary of the acceptance criteria and study information for the MedCAD® AccuPlate® 3DTi Patient-Specific Plating System, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA clearance letter mentions the following non-clinical performance tests. The acceptance criteria are implied to be "equivalent" to identified reference devices.

2. Sample Sizes Used for the Test Set and Data Provenance

The document does not explicitly state the sample sizes (number of devices or tests) for the non-clinical performance tests (bending, pushout, fit and form validation). It also does not specify the country of origin of the data or whether the studies were retrospective or prospective, as these are non-clinical (mechanical and material) performance tests rather than clinical studies with patient data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not explicitly provided in the document. For non-clinical performance tests, "ground truth" is typically established by engineering standards and validated testing procedures, not by human experts in the same way as clinical image interpretation. The "Fit and Form Validation" mentions alignment with an "approved surgical plan," which would involve surgical expertise in its creation, but the number and qualifications of individuals involved in approving these plans or assessing the fit are not detailed.

4. Adjudication Method for the Test Set

Not applicable for these types of non-clinical, objective performance tests. Adjudication methods like 2+1 or 3+1 are typically used for subjective clinical interpretations by multiple readers (e.g., radiologists) in diagnostic accuracy studies.

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 such study was mentioned. The MedCAD® AccuPlate® 3DTi Patient-Specific Plating System is a patient-specific surgical implant, not an AI-assisted diagnostic tool.

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

This concept is not directly applicable. The device itself is an implant. However, the "patient specific design process" for creating the implant is an "algorithm only" type of process in that it uses patient anatomical data to generate the device design. The "Fit and Form Validation" likely assessed the output of this design process against the intended surgical plan. The document states that the design process "was shown to produce devices that aligned with the approved surgical plan," indicating successful standalone performance of the design software.

7. The Type of Ground Truth Used

• Biocompatibility: Established by adherence to ISO 10993 standards and FDA guidance.
• Sterilization Validation: Established by adherence to ISO 17665 standards and FDA guidance, with a specific SAL target.
• Static and Dynamic Bending: Established by ASTM F382 standard, with comparison to a reference device's known performance.
• Axial Screw Pushout: Established by ASTM F543 standard, with comparison to a reference device's known performance.
• Fit and Form Validation: Established by alignment with an "approved surgical plan." This likely involves a comparison of the 3D-printed plate geometry with the virtual surgical plan derived from patient imaging data, which can be considered "design ground truth."

8. The Sample Size for the Training Set

The document does not describe the development or training of an AI algorithm in the traditional sense, so there is no mention of a training set sample size. The "patient-specific design software" is mentioned as being the same as a reference device (K192282), implying it's a previously validated system rather than a newly trained AI model.

9. How the Ground Truth for the Training Set Was Established

As no training set for an AI algorithm is described, this information is not applicable.

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MedCAD® AccuPlan® Orthopedics System is intended for use as a software system and image segmentation system for the transfer of imaging information from a medical scanner such as a CT based system. The input data file is processed by the system and the result is an output data file. This file may then be provided as digital models or used as input to a rapid prototyping portion of the system that produces physical outputs including anatomical models of the fibula and ilium. surgical guides for harvesting bone grafts from the fibula or ilium, and surgical planning case reports for use in maxillofacial reconstructive surgeries. MedCAD® AccuPlan® Orthopedics System is also intended as a pre-operative software tool for simulating / evaluating surgical treatment options. The MedCAD® AccuPlan® Orthopedics System is indicated for use in adolescents (greater than 12 to 21 years of age) and adults.

The MedCAD® AccuPlan® Orthopedics System is a collection of software and associated additive manufacturing equipment intended to provide a variety of outputs to support harvesting of bone to support maxillofacial reconstructive surgeries. The system uses electronic medical images of the patient's anatomy with input from the physician to manipulate original patient images for planning and executing surgery. The patient specific outputs from the system include anatomical models, surgical guides, and patient-specific case reports.

Following the MedCAD® Quality System and specific Work Instructions, trained employees utilize Commercial Off-The-Shelf (COTS) software to manipulate 3-D medical Computed Tomography (CT) images to create patient-specific physical and digital outputs. The process requires clinical input and review from the physician during and prior to delivery of the final outputs. While the process and dataflow vary somewhat based on the requirements of a given patient and physician, the following description outlines the functions of key sub-components of the system, and how they interact to produce the defined system outputs. It should be noted that the system is operated only by trained MedCAD employees, and the physician does not directly input information. The physician provides input for model manipulation and interactive feedback through viewing of digital models of system outputs that are modified by the engineer during the planning session.

The MedCAD® AccuPlan® Orthopedics System is made up of two individual pieces of software for the design and various manufacturing equipment integrated to provide a range of anatomical models (physical and digital), surgical guides, and patient-specific planning reports for harvesting of bone from the fibula and ilium for use in maxillofacial reconstructive surgeries.

The MedCAD® AccuPlan® Orthopedics System requires an input 3-D image file from medical imaging systems (i.e., CT). This input is then used, with support from the prescribing physician to provide the following potential outputs to support maxillofacial reconstructive surgery. Each system output is designed with physician inout and reviewed by the physician prior to finalization. All outputs are used only with direct physician involvement to reduce the criticality of the outputs.

System outputs include:

• Anatomical Models
• Surgical Guides
• Patient-Specific Case Reports

Here's a breakdown of the acceptance criteria and study information for the MedCAD® AccuPlan® Orthopedics System, based on the provided FDA 510(k) summary:

This device is primarily a software system for surgical planning and the creation of physical outputs (anatomical models and surgical guides) based on CT scan data. The performance testing detailed here focuses on the physical outputs rather than an AI-driven diagnostic or assistive algorithm's accuracy.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Wear Debris Testing: Not explicitly stated, but "worst-case titanium surgical guide" indicates at least one such guide was tested.
• Fit and Form Validation: "All samples" were tested. The exact number of samples is not explicitly stated.
• Data Provenance: Not specified in the provided text (e.g., country of origin, retrospective/prospective). However, since the system processes patient-specific CT imaging, the "data" would be the CT scans themselves.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• The document states that "The process requires clinical input and review from the physician during and prior to delivery of the final outputs" and "All outputs are used only with direct physician involvement to reduce the criticality of the outputs."
• For the performance testing itself, the identity and number of "experts" (e.g., those determining if a fit was "PASS") are not specified. The ground truth for fit and form seems to be established by physical/optical measurement against a 3D model and representative anatomical model.
• For the broader system design and physician input, "the prescribing physician" provides input and review, but their qualifications and number are not detailed beyond "physician."

4. Adjudication Method for the Test Set

• No specific adjudication method (e.g., 2+1, 3+1 consensus) for the performance tests is described. The "PASS" result suggests a clear acceptance/rejection criteria was applied.
• For the general operation of the system, it notes that the "physician provides input for model manipulation and interactive feedback through viewing of digital models of system outputs that are modified by the engineer during the planning session." This implies an iterative, interactive process rather than a formal adjudication of a test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not performed or described. The device is a surgical planning and manufacturing system, not an AI diagnostic aid evaluated for human reader improvement. The performance testing focuses on the physical properties and accuracy of the manufactured outputs.

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

• The performance testing performed (wear debris, fit and form, sterilization, biocompatibility) relates to the outputs of the system (surgical guides, anatomical models) rather than the standalone algorithmic accuracy of image segmentation or planning.
• The system itself relies on "trained MedCAD employees" and "clinical input and review from the physician." Therefore, it's not a purely standalone AI algorithm without human involvement in its operational workflow.

7. The Type of Ground Truth Used

• For Fit and Form Validation: The ground truth appears to be the digital 3D model ("alignment with the 3D model") and a representative anatomical model ("fitting the guide over the corresponding defect"). These are objective measurements and physical fit evaluations.
• For Wear Debris Testing: The ground truth for safety is published literature values of safe wear debris.
• For Sterilization and Biocompatibility: Ground truth is established by international standards (ISO 17665, ISO 10993-1) and FDA guidance documents, which define accepted levels and methodologies.

8. The Sample Size for the Training Set

• Not applicable / Not provided. This device is described as using "Commercial Off-The-Shelf (COTS) software to manipulate 3-D medical Computed Tomography (CT) images." There is no mention of a machine learning model that requires a dedicated training set. The "software" component appears to be tools for engineers and physicians to interact with and refine 3D models.

9. How the Ground Truth for the Training Set was Established

• Not applicable. As no training set for a machine learning model is mentioned, there's no ground truth establishment process to describe for a training set.

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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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The MirrorMe3D Modeling System is intended for use as an image processing system for the transfer of 3D medical images. The MirrorMe3D Modeling System is also intended as a visualization system for measuring and treatment planning for aesthetic facial soft tissue. The input data is processed by the System using off-the-shelf modeling software and the result is an output data file that may then be provided as a digital model or used as input for the additive manufacturing of a physical anatomic model, which is not for diagnostic use. The MirrorMe3D Modeling System should only be used in conjunction with expert clinical judgment and is not intended for diagnostic use. MirrorMe3D trained personnel will use off-the-shelf software to assist users in creating the 3D virtual (or digital) model that depicts the surgeon's intended outcome. The anatomic models are not for diagnostic use.

The MirrorMe3D Modeling System is image processing software that enables the input and visualization of 3D medical imaging with output files that can be virtual or physical 3D anatomic models. The Modeling System software is used for visualization of preoperative treatment planning options, with measurement functionality, for surgery of the aesthetic facial soft tissue.

Here's a breakdown of the acceptance criteria and the study information you requested, based on the provided document:

Acceptance Criteria and Device Performance

The document describes that for each device produced by MirrorMe3D, there are checks to ensure the system conforms to specifications and is fit for its intended use. There isn't a single, aggregated table of numerical acceptance criteria for the entire device as one might find for a standalone AI algorithm. Instead, the "acceptance criteria" are tied to individual product validation and process auditing.

However, based on the text, we can infer some key performance aspects being tested.

1. Table of Acceptance Criteria (Inferred) and Reported Device Performance

Important Note: The document describes a workflow and quality control process for each individual product rather than a large-scale statistical study of the device as a whole against predefined performance metrics like sensitivity/specificity for a diagnostic device. The MirrorMe3D Modeling System is framed as an "image processing system" and "visualization system" that relies heavily on human expertise and approval.

Study Information

The document describes process validation and quality control measures for the manufacturing and utilization of the MirrorMe3D Modeling System for each specific case, rather than a single, large-scale clinical performance study with a test set in the traditional sense of an AI/diagnostic device submission.

Thus, many of your requested items for a "study" (like test set sample size, expert qualifications for ground truth establishment, MRMC studies) are not directly applicable or explicitly detailed in the provided text, as the "study" is more of an ongoing quality assurance and product-specific validation process.

Let's address the points as best as possible given the provided text:

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

• Sample Size: Not applicable in the context of a single retrospective/prospective test set. The document states their process: "For each device, MirrorMe3D checks..." indicating that every individual case processed by MirrorMe3D undergoes a series of checks and reviews.
• Data Provenance: Not specified. The input is "3D medical images," but the source (country, hospital) is not mentioned. Given it's a service, the data would come from individual clinicians/patients. The document also doesn't specify if the input images for the internal process validation were retrospective or prospective.

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

• Number of Experts: For each case, at least one surgeon (Doctor) is involved in establishing the "ground truth" (i.e., the intended treatment outcome). MirrorMe3D trained personnel also assist.
• Qualifications of Experts: The primary "expert" establishing the desired outcome (which acts as the ground truth for the model's design) is the "Doctor" (Surgeon). Specific qualifications like years of experience or board certification are not detailed in this document but are implicitly understood for a surgeon using such a system for treatment planning. MirrorMe3D also employs "trained personnel" to assist.

4. Adjudication method for the test set:

• Adjudication Method: "The approval of the design of the anatomic model depicting the intended treatment outcome by the Doctor is required and indicates design acceptance." This suggests a single-expert approval model for each case. There is no mention of a multi-reader consensus or 2+1/3+1 adjudication for a test set.

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:

• A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was NOT done according to the provided text. The device is described as a "visualization system for measuring and treatment planning" and "image processing software" where "MirrorMe3D trained personnel will use off-the-shelf software to assist users in creating the 3D virtual (or digital) model that depicts the surgeon's intended outcome." This process does not describe an AI-assisted diagnostic workflow with human readers.

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

• No, a standalone algorithm-only performance study was NOT done. The system explicitly requires "MirrorMe3D trained personnel" and "expert clinical judgment" (from the Doctor/Surgeon) to create the models and approve the design. It's a human-in-the-loop system. The document states, "MirrorMe3D trained personnel will use off-the-shelf software to assist users in creating the 3D virtual (or digital) model that depicts the surgeon's intended outcome."

7. The type of ground truth used:

• The primary "ground truth" for the treatment plan and subsequent model design is the "surgeon's intended outcome" or "expert clinical judgment" as approved by the Doctor/Surgeon. For the physical models, the ground truth is adherence to the approved digital design and established geometric accuracy tolerances.

8. The sample size for the training set:

• Not applicable as this document describes a quality control and process validation framework for a system that uses "off-the-shelf software" and relies on human intervention, rather than a deep learning AI model that requires a dedicated training set. The "off-the-shelf software" would have been developed and "trained" by its respective manufacturers, but that is not part of this submission for the MirrorMe3D system.

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

• Not applicable. As explained in point 8, the MirrorMe3D system itself doesn't refer to a "training set" in the context of an AI algorithm that learns from data.

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