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Mimics Enlight CMF is intended for use as a software interface and imaging segmentation system for the transfer of medical imaging information to an output file.

Mimics Enlight CMF is also intended to support the diagnostic and treatment planning process of maxillofacial procedures. For this purpose, Mimics Enlight CMF provides visualization, measurement and design tools.

The Mimics Enlight CMF output file can be used for the fabrication of the output file using traditional or additive manufacturing methods. The fabricated output can be used for diagnostic purposes in the field of maxillofacial applications.

Mimics Enlight CMF should be used in conjunction with other diagnostic tools and expert clinical judgement.

Mimics Enlight CMF is an image processing software for the diagnosis and treatment planning of maxillofacial procedures. Mimics Enlight CMF allows the user to import, visualize and segment medical images. Mimics Enlight CMF also allows the user to perform measurements, treatment planning and occlusal splint design. Mimics Enlight CMF allows the user to output digital 3D models of the anatomy to be used for fabrication of physical anatomical models. Mimics Enlight CMF is structured as a modular package consisting of separate workflows for the diagnosis and treatment planning of various indications within the maxillofacial field. The workflows in Mimics Enlight CMF are built on the Mimics Enlight platform. The workflows in Mimics Enlight CMF cover following steps and functionality in the diagnostic and treatment planning process of maxillofacial procedures:

Digital 3D model creation

• . Importing medical images in DICOM format and other formats
• Viewing images and DICOM data
• Selecting a region of interest using generic segmentation tools
• . Verifying and editing a region of interest
• . Calculating a digital 3D model and editing the model
• . Creating composite models by combining medical image information and dental information using registration tools
• Exporting digital 3D models for additive manufacturing (3D printing) of physical replicas (anatomical models)

Planning

• Indicating nerves and cephalometric landmarks
• . Setting the natural head position
• Planning the treatment by cutting the models and repositioning the parts
• Setting the occlusion digitally or by importing an occlusion model ●
• Measuring on images and digital 3D models
• Simulating the soft tissue of the face after the planned treatment

Design

• Designing personalized digital occlusal splints using generic design and finishing tools ●
  User fabrication using additive manufacturing (3D printing) of physical replicas includes only fabrication of anatomical models and does not include additive manufacturing of occlusal splints.

The provided text describes the device, Mimics Enlight CMF, and its substantial equivalence to predicate devices, but it does not contain the specific acceptance criteria or detailed study results (like sample sizes, expert qualifications, or MRMC study results) that would typically be found in a detailed performance study section of a 510(k) submission.

The document mainly focuses on:

• Indications for Use
• Comparison of Technological Characteristics with Predicate Device
• Statements about Software Verification and Validation
• Geometrical Accuracy Testing for Virtual Models and Physical Replicas (by reference to the predicate device)
• Soft Tissue Simulation Equivalence

Based on the available text, here's what can be extracted and what information is not present:

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

The document does not provide a specific table with numerical acceptance criteria and reported performance values. It mentions:

• "The results revealed no deviations in the virtual models, demonstrating substantial equivalency between the two devices."
• "The deviations were found to be within the acceptance criteria, indicating that the virtual models can be printed accurately using one of the compatible 3D printers." (This refers to predicate device testing, with the conclusion that it applies to the subject device due to no significant deviations in virtual models).
• "The test demonstrated that the soft tissue simulation in Mimics Enlight CMF is equivalent to the soft tissue simulation in the reference device Proplan CMF (K111641)."

This implies acceptance criteria related to "no deviations" or "deviations within acceptance criteria" and "equivalence," but the specific numerical thresholds are not detailed.

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

• Sample Size for Test Set: Not specified. The document mentions "virtual models were compared" and "soft tissue simulation in the subject device Mimics Enlight CMF" was tested, but no specific number of cases or models used for these comparisons is provided.
• Data Provenance: Not specified. There is no information regarding the country of origin of the data or whether it was retrospective or prospective.

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

Not specified. The document mentions testing for geometrical accuracy and soft tissue simulation equivalence, but it does not describe any expert-based ground truth establishment process involving specific numbers of experts or their qualifications.

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

Not specified. Given that the described tests involve comparisons of virtual models and simulations rather than human interpretation of cases to establish ground truth, an adjudication method in the traditional sense (for clinical interpretation) is not mentioned or implied.

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. The document does not mention an MRMC study. The device, Mimics Enlight CMF, is described as an image processing software for segmentation, visualization, measurement, and design tools, supporting diagnostic and treatment planning. It's not an AI-assisted diagnostic tool in the sense that medical images are interpreted by human readers with or without AI assistance. Therefore, an MRMC study to show human reader improvement with AI assistance is not relevant to the described performance evaluation.

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

Yes, in a sense. The described tests on "geometrical accuracy" of virtual models and "soft tissue simulation" are evaluations of the algorithm's output directly, without a human in the loop for the performance measurement itself. The device is an "image processing software," so its performance is inherently about the quality and accuracy of its processing capabilities. The statement "Mimics Enlight CMF should be used in conjunction with other diagnostic tools and expert clinical judgement" implies that it is not intended for standalone clinical decision-making but rather as a tool within a broader clinical workflow, where the algorithm's output is then used by a human expert.

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

The ground truth for the "geometrical accuracy" appears to be based on:

• Comparison with the predicate device's virtual models ("no deviations in the virtual models").
• Optical scans of physical models (for the predicate device, implying this accuracy carries over to the subject device).

For "soft tissue simulation," the ground truth was equivalence to the reference device Proplan CMF (K111641).

This is a technical ground truth based on direct comparison to a known state (predicate/reference device's output or physical measurements via optical scans), rather than a clinical ground truth like pathology or expert consensus on a diagnosis.

8. The sample size for the training set

Not specified. The document does not provide details about a training set, as it emphasizes verification and validation against requirements and comparison to predicate devices, rather than a machine learning model's training process.

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

Not applicable/Not specified. Since no training set or machine learning model training is described for this device in the provided text, the establishment of ground truth for a training set is not pertinent to the information given. The device appears to be a rule-based or algorithmic image processing software, not a deep learning AI model that requires a labeled training dataset in the traditional sense.

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Mimics Cardiac Planner is intended to be used as a pre-procedural planning software to screen and plan structural heart and vascular procedures based on DICOM compliant medical images. Mimics Cardiac Planner allows the clinician to visualize, measure, annotate and edit pre-procedural plan data. The software can be used to evaluate the sizing, positioning and delivery pathway of structural heart and vascular devices.

Mimics Cardiac Planner should be used in conjunction with other diagnostic tools and expert clinical judgement.

The Mimics Cardiac Planner is an online planning software, which allows the clinician to review and adjust a plan for a structural heart or vascular procedures based on DICOM images, 3D models and landmarks. The software guides the clinician through different steps of the cardio-vascular workflow, where the relevant information, like 3D models of anatomy and devices, images, landmarks and measurements are presented.

The software enables the clinician to review the anatomy and relevant workflow specific predefined set of measurements, evaluate the device size and position, assess the delivery pathway and prepare the fluoroscopy angles for the procedure.

The software provides the tools to adjust all predefined measurements, and to perform custom measurements.

The software is integrated with a Medical Device Data System, which is responsible for the case management and user management.

The provided text does not contain detailed acceptance criteria and corresponding study results for the device's performance. It primarily focuses on the regulatory aspects of the 510(k) submission, including:

• Device Name: Mimics Cardiac Planner
• Intended Use: Pre-procedural planning software for structural heart and vascular procedures based on DICOM images, allowing visualization, measurement, annotation, and editing of pre-procedural plan data. It can evaluate sizing, positioning, and delivery pathway of devices.
• Regulatory Classification: Class II, Product Code LLZ (Medical Image Management And Processing System)
• Predicate Device: Mimics Enlight (K190874)
• Reference Device: SurgiCase Shoulder Planner (K220452)
• Technological Characteristics Comparison: Discusses similarities and differences with predicate and reference devices, particularly concerning functionality (e.g., input requirements, cloud vs. desktop software) and underlying technology.
• Performance Data Summary: States that "Software verification and validation were performed, and documentation was provided following the FDA guidance 'Content of Premarket Submissions for Device Software Functions.' This includes verification against defined requirements, and validation against user needs."

However, it does not provide any specific acceptance criteria (e.g., numerical thresholds for accuracy, precision, sensitivity, specificity) or the quantitative results of any performance studies (e.g., clinical trials, reader studies, phantom studies).

Therefore, I cannot create the requested table of acceptance criteria and reported device performance or answer the detailed questions about sample size, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, or training set details. The document only broadly states that verification and validation were performed to demonstrate substantial equivalence, but the specifics of those evaluations are not included here.

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Materialise Mimics Enlight is intended for use as a software interface and image segmentation system for the transfer of DICOM imaging information from a medical scanner to an output file.

It is also intended as a software to aid interpreting DICOM compliant images for structural heart and vascular treatment options. For this purpose. Materialise Mimics Enlight provides additional visualisation and measurement tools to enable the user to screen and plan the procedure.

The Materialise Mimics Enlight output file can be used for the fabrication of physical replicas of the using traditional additive manufacturing methods. The physical replica can be used for diagnostic purposes in the field of cardiovascular applications.

Materialise Mimics Enlight should be used in conjunction with other diagnostic tools and expert clinical judgement.

Materialise Mimics Enlight for structural heart and vascular planning is a software interface that is organized in a workflow approach. High level, each workflow in the field of structural heart and vascular will follow the same kind of structure of 4 steps which will enable the user to plan the procedure:

• 1. Analyse anatomy
• 2. Plan device
• 3. Plan delivery
• 4. Output

To perform these steps the software provides different methods and tools to visualize and measure based on the medical images.

The user is a medical professional, like cardiologists or clinical specialists. To start the workflow DICOM compliant medical images will need to be imported. The software will read the images and convert them into a project file. The user can now start the workflow and follow the steps visualized in the software. The base of the workflow is to create a 3D reconstruction of the anatomy based on the medical images to use this further together with the 2D medical images in the workflow to plan the procedure.

The provided text describes the Materialise Mimics Enlight device and its 510(k) submission for FDA clearance. However, it does not contain specific details about acceptance criteria, numerical performance data, details of the study (sample sizes, ground truth provenance, number/qualifications of experts, adjudication methods, MRMC studies, or standalone performance), or training set information.

The document mainly focuses on:

• Defining Materialise Mimics Enlight's intended use and indications.
• Establishing substantial equivalence to predicate devices (Mimics Medical, 3mensio Workstation, Mimics inPrint).
• Describing general technological similarities and differences between the subject device and predicates.
• Stating that software verification and validation were performed according to FDA guidance, including bench testing and end-user validation.
• Mentioning "geometric accuracy" assessments for virtual models and physical replicas, and interrater consistency for the semi-automatic neo-LVOT tool, with the conclusion that "deviations were within the acceptance criteria."

Therefore, based only on the provided text, I cannot complete the requested tables and descriptions with specific numerical values for acceptance criteria or study results.

Here's a summary of what can be extracted and what is missing:

1. Table of acceptance criteria and reported device performance

Missing Information: Specific numerical values for the acceptance criteria for geometric accuracy (e.g., tolerance in mm) and for interrater consistency of the neo-LVOT tool.

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

• Sample size for test set: Not specified. The document mentions "Bench testing" and "a set of 3D printers" for physical replicas, but no case numbers.
• Data provenance (country of origin, retrospective/prospective): Not specified.

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

• Number of experts: Not specified.
• Qualifications of experts: Not specified. The document mentions "medical professional, like cardiologists or clinical specialists" as intended users, but not specifically for ground truth establishment in a test set.

4. Adjudication method for the test set

• Adjudication method: Not specified.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, and its effect size

• The document implies general "end-user validation" and mentions the neo-LVOT tool showing "higher interrater consistency/repeatability," which suggests some form of human reader involvement. However, it does not explicitly state that a multi-reader, multi-case (MRMC) comparative effectiveness study was performed in the context of human readers improving with AI vs. without AI assistance.
• Effect size: Not specified.

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

• "Software verification and validation were performed... This includes verification against defined requirements, and validation against user needs. Both end-user validation and bench testing were performed." This implies that the device's performance was evaluated, potentially including standalone aspects, but it doesn't separate out a clear standalone performance study result. The "semi-automatic" nature of the Neo-LVOT tool means it's not purely algorithmic.

7. The type of ground truth used

• While not explicitly stated, the context of "geometric accuracy of virtual models" and "physical replicas" suggests ground truth would be based on:
  • Geometric measurements: Reference measurements from the original DICOM data or CAD models for virtual models, and precise measurements of the physical replicas for comparison.
  • For the neo-LVOT tool, ground truth for "interrater consistency/repeatability" would likely be derived from expert measurements.

8. The sample size for the training set

• Sample size for training set: Not specified. The document focuses on verification and validation, not development or training data.

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

• Ground truth for training set: Not specified. As above, the document does not detail the training set.

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Mimics Medical is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. Mimics Medical is also intended for measuring and treatment planning. The Mimics Medical output can be used for the fabrication of the output file using traditional or additive manufacturing methods.

The physical replica can be used for diagnostic purposes in the field of orthopaedic, maxillofacial and cardiovascular applications.

Mimics Medical should be used in conjunction with expert clinical judgement.

Mimics Medical is image processing software that allows the user to import, visualize and segment medical images, check and correct the segmentations, and create digital 3D models can be used in Mimics Medical for measuring, treatment planning and producing an output file to be used for additive manufacturing (3D printing). Mimics Medical also has functionality for linking to third party software packages. Mimics Medical is structured as a modular package. This includes the following functionality:

• Importing medical images in DICOM format and other formats (such as BMP, TIFF, JPG and raw images)
• Viewing images and DICOM data
• Selecting a region of interest using generic segmentation tools
• Segmenting specific anatomy using dedicated semi-automatic tools or fully automatic algorithms
• Verifying and editing a region of interest
• Calculating a digital 3D model and editing the model
• Measuring on images and 3D models
• Exporting images, measurements and 3D models to third-party packages
• Planning treatments (surgical cuts etc.) on the 3D models
• Interfacing with packages for Finite Element Analysis
• Creating Python scripts to automate workflows

Here's an analysis of the provided text to fulfill your request, focusing on the acceptance criteria and study proving device performance:

Unfortunately, the provided text (K183105 510(k) Summary for Mimics Medical) does not contain specific acceptance criteria, detailed study results, or information about expert involvement (number, qualifications, adjudication method), MRMC studies, or standalone performance of the algorithm. The document primarily focuses on demonstrating substantial equivalence to a predicate device based on similar technological characteristics and general performance statements.

The document mentions that "Deviations were within the acceptance criteria" but does not define what those acceptance criteria are. It also states that "all performance testing conducted device performance and substantial equivalence to the predicate device" but doesn't elaborate on the specifics of this testing.

Therefore, many of your specific questions cannot be answered from the provided text. I will, however, extract all relevant information from the document to construct as much of the table and detailed answers as possible, noting where information is missing.

Device Description and Intended Use

Device Name: Mimics Medical
Regulation Number: 21 CFR 892.2050
Regulation Name: Picture archiving and communications system
Regulatory Class: Class II
Product Code: LLZ

Intended Use Statement (from page 2 & 4):
Mimics Medical is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. Mimics Medical is also intended for measuring and treatment planning. The Mimics Medical output can be used for the fabrication of physical replicas of the output file using traditional or additive manufacturing methods. The physical replica can be used for diagnostic purposes in the field of orthopedic, maxillofacial and cardiovascular applications. Mimics Medical should be used in conjunction with expert clinical judgement.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document mentions "acceptance criteria" but does not define them. The "Reported Device Performance" is also very summarized, lacking specific metrics or quantitative results.

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

The document does not state the sample size used for the test set.
The document does not state the data provenance (e.g., country of origin of the data, retrospective or prospective).

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

The document does not provide any information on the number of experts used or their qualifications for establishing ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not provide any information on the adjudication method used for the test set (e.g., 2+1, 3+1, none).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No. The document does not indicate that an MRMC comparative effectiveness study was done. The performance evaluation focuses on "geometric accuracy" of models created by the software compared to a predicate device, not on human reader improvement with AI assistance. The device is a "software interface and image segmentation system," implying it's a tool for a user, rather than a standalone AI for diagnostic interpretation. The text also states, "Mimics Medical should be used in conjunction with expert clinical judgement," further suggesting it's a tool, not a replacement or direct assistant in the AI-for-diagnosis sense that would typically warrant an MRMC study.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The study described is not a "standalone" algorithm performance study in the sense of an AI model making diagnostic interpretations. Mimics Medical is described as an "image processing software" and "software interface and image segmentation system" that allows users to segment, view, measure, and export data. While it contains "semi-automatic tools or fully automatic algorithms" for segmentation, the performance evaluation discussed (geometric accuracy of virtual models and physical replicas) assesses the output of the software as used, rather than a standalone diagnostic performance metric like sensitivity/specificity for a disease detection task. The product is a tool for creating models, not an algorithm that outputs a diagnostic decision without human input.

7. The Type of Ground Truth Used

The type of "ground truth" implied by the geometric accuracy testing would likely be:

• For virtual models: Comparison against either a known phantom or a reference standard measurement/model established with high precision (e.g., by the predicate device or a gold-standard metrology method). The document states "Accuracy of the virtual models was compared for the subject and predicate device," suggesting the predicate device's output might have served as a reference, or a common reference was used for both.
• For physical replicas: Comparison against the virtual models created by Mimics Medical. The document states, "The physical replicas were compared to the virtual models."

This is a technical ground truth based on geometric measurements, not a clinical ground truth like pathology or patient outcomes.

8. The Sample Size for the Training Set

The document does not provide any information on the sample size for the training set. The descriptions of "semi-automatic tools or fully automatic algorithms" hint at underlying algorithmic components that might require training, but no details are given.

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

The document does not provide any information on how the ground truth for the training set was established.

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Mimics inPrint is intended for use as a software interface and image segmentation system for the transfer of DICOM imaging information from a medical scanner to an output file. It is also used as pre-operative software for treatment planning. For this purpose, the Mimics inPrint output file can be used for the fabrication of the output file using traditional or additive manufacturing methods.

The physical replica can be used for diagnostic purposes in the field of orthopedic, maxillofacial and cardiovascular applications. Mimics inPrint should be used in conjunction with other diagnostic tools and expert clinical judgement.

Mimics inPrint is an image processing and segmentation software that was built on top of the Mimics application framework, an image processing and segmentation framework for the transfer of imaging information to an output file.

The provided text describes a 510(k) submission for the device "Mimics inPrint." However, it does not contain any information about acceptance criteria, device performance, data provenance, expert adjudication, MRMC studies, standalone performance, ground truth, or training set details. The "Performance Data" section briefly mentions "Non-clinical tests" and "measurement accuracy and calculate 3D study" but provides no specific results or methodologies for these tests.

Therefore, I cannot fulfill your request to describe the acceptance criteria and the study that proves the device meets the acceptance criteria based on the provided text. The text focuses on establishing substantial equivalence to a predicate device, as is common in 510(k) submissions, rather than providing detailed performance study results against specific acceptance criteria.

The 510(k) summary only states:

• "Measurement accuracy and calculate 3D study were performed and confirmed to be within specification." (No specific "specification" or acceptance criteria are provided, nor are the results of these studies.)
• "Validation of printing of physical replicas was performed and demonstrated that anatomical models for cardiovascular, orthopedic and maxillofacial cases can be printed accurately when using any of the compatible 3D printers." (Again, no specific "accuracy" metric or acceptance criteria are provided.)

To answer your questions, the provided text would need to contain a detailed performance study section with quantitative results against predefined acceptance criteria.

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Mimics is intended for use as a software interface and image segmentation system for the transfer of imaging information from a medical scanner such as a CT scanner or a Magnetic Resonance Imaging scanner to an output file. It is also used as pre-operative software for simulating /evaluating surgical treatment options. Mimics is not intended to be used for mammography imaging.

The Materialise Mimics software is intended for use as a software interface and image segmentation system for the transfer of imaging information from a medical scanner such as a CT scanner or a Magnetic Resonance Imaging scanner to an output file. It is also intended as pre-operative software for simulating / evaluating surgical treatment options. Mimics is not intended to be used for mammography imaging.

This 510(k) summary for the Mimics software does not provide any specific acceptance criteria or a study demonstrating that the device meets those criteria.

Instead, the submission focuses on establishing substantial equivalence to a previously cleared predicate device (SimPlant product from Materialise Dental, K033849). This means that the FDA determined Mimics is as safe and effective as the predicate device based on its similar design, materials, function, and indications for use.

Therefore, I cannot populate the table or answer the specific questions requested, as the information is not present in the provided document.

Here's why and what can be inferred:

• Acceptance Criteria & Reported Performance: No specific performance metrics (e.g., accuracy, precision, sensitivity, specificity for image segmentation or surgical simulation) are mentioned. The concept of "acceptance criteria" is not explicitly discussed in the context of device performance.
• Study Details: There's no description of a study conducted to prove the device meets performance criteria. The clearance is based on substantial equivalence, not a new clinical performance study.
• Sample Size, Data Provenance, Experts, Adjudication, MRMC, Standalone Performance, Ground Truth: None of these details are provided because a direct performance study, as typically described by these parameters, was not the basis for this 510(k) clearance.
• Training Set: There is no mention of a training set as the clearance is not based on an AI/ML model for diagnostic or predictive purposes that would typically require a training set. The device is described as an "image processing system" and "preoperative software for simulating surgical treatment options," implying functionality rather than a learned algorithm.

Conclusion based on the provided document:

The Mimics software received 510(k) clearance by demonstrating substantial equivalence to a predicate device (SimPlant System, K033849). This type of clearance typically relies on showing that the new device has similar technological characteristics and performance (or provides equivalent performance) to a device already on the market, rather than requiring new, independently established acceptance criteria and a performance study against those criteria. The provided document details the regulatory classification, indications for use, and the basis for substantial equivalence, but not a specific performance study.

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