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.

Device Description

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.

AI/ML Overview

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.

Summary

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Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left, there is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

May 31, 2024

Materialise NV Victoria Becheva Regulatory Affairs Specialist, Medical Technologielaan 15 Leuven, 3001 Belgium

Re: K223427

Trade/Device Name: Mimics Enlight CMF Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: LLZ, DZJ Dated: Mav 24, 2024 Received: May 24, 2024

Dear Victoria Becheva:

We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"

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(https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review. the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely.

Bobak Shirmohammadi -S

For Michael E. Adjodha, M.ChE., RAC, CQIA Assistant Director DHT1B: Division of Dental and ENT Devices

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OHT1: Office of Ophthalmic, Anesthesia, Respiratory, ENT and Dental Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K223427

Device Name Materialise Mimics Enlight CMF

Indications for Use (Describe) Materialise Mimics Enlight CMF:

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

Type of Use (Select one or both, as applicable)

X Prescription Use (Part 21 CFR 801 Subpart D)

Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) Summary - K223427

510(k) Summary

The following section is included as required by the Safe Medical Devices Act (SMDA) of 1990 and 21CFR 807.92.

Company name	Materialise N.V.
Establishment registration number	3003998208
Street Address	Technologielaan 15
City	Leuven
Postal code	3001
Country	Belgium
Phone number	+32 16 744 571
Fax number	+32 16 39 66 06
Principal Contact person	Victoria Becheva
Contact title	Regulatory Affairs Specialist
Contact e-mail address	Regulatory. Affairs@materialise.be
Additional contact person	Polliane Carvalho
Contact title	Regulatory Affairs Specialist
Additional contact person	Jenny Jones
Contact title	Global Quality & Regulatory Manager, Medical
Contact e-mail address	Jenny.Jones@materialise.com

Submission date

The date of the Traditional 510(k) submission is November 09, 2022. The Summary was prepared on May 23, 2024.

Submission information

Trade Name	Mimics Enlight CMF
Common Name	Image processing system
Classification Name	System, Image processing, Radiological
Classification product code	LLZ (892.2050), DZJ (872.4120)

Predicate Devices

The primary predicate device to which substantial equivalence is claimed:

Trade or proprietary or model name	Materialise Mimics Medical, Mimics Medical, Mimics
510(k) number	K183105
Decision date	03/27/2019
Classification product code	LLZ (892.2050)
Manufacturer	Materialise N.V.

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Reference device

Trade or proprietary or model name	SurgiCase, SurgiCase CMF, ProPlan CMF
510(k) number	K111641
Decision date	09/26/2011
Classification product code	LLZ (892.2050)
Manufacturer	Materialise N.V.

Trade or proprietary or model name	OsteoPlan System
510(k) number	K212570
Decision date	02/12/2022
Classification product code	DZJ (872.4120)
Manufacturer	OsteoMed

Description and functioning of the device

Image /page/5/Figure/5 description: This image shows a diagram of the process for creating personalized splints using 3D printing. The process starts with a patient, who undergoes imaging. The images are then used for segmentation and 3D model creation, followed by planning and design of personalized splints using Mimics Enlight CMF. Finally, the splints are 3D printed and used for treatment.

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

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. 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.

Indications for Use

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.

The Mimics Enlight CMF output file can be used for the fabrication of physical replicas of the 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.

Comparison of Technological Characteristics with the Predicate Device

The subject device employs similar fundamental technologies as the predicate device.

Technological similarities include:

Device functionality: The image information, the measuring and planning functionality, the processing to output file are the same for the subject device as for the predicate device.

Software technology: The subject device has the same code base as the predicate device and uses the same methods for design and verification and validation as the predicate device.

Following technological differences exist between the subject device software and the predicate device software:

The main difference between the subject device and previously cleared device, K183105 is that the subject device's intended use is limited to -maxillofacial applications, whereas the primary predicate device covers a broader field of orthopedic, maxillofacial and cardiovascular applications.

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The subject software technology differences have been demonstrated that they do not affect the safety or effectiveness, or that they do not raise any new issues regarding to the safety and effectiveness compared to the predicate device.

Performance Data

Software verification and validation were performed, and documentation was provided following the "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices". This includes verification against defined requirements, and validation against user validation and bench testing were performed.

To evaluate the geometrical accuracy of the subject device, virtual models were compared with those of the predicate device. The results revealed no deviations in the virtual models, demonstrating substantial equivalency between the two devices.

For the predicate device, the geometrical accuracy of physical replicas was assessed by comparing virtual models with the optical scans of the physical models. 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.

As the virtual models of the predicate and subject devices showed no significant deviations, it can be concluded that compatible 3D printers can produce accurate results for the subject device as well.

Additional testing was done for the soft tissue simulation in the subject device Mimics Enlight CMF. 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).

In conclusion, all performance testing conducted device performance and substantial equivalence to the predicate device.

Summary

A comparison of intended use and technological characteristics combined with performance data demonstrates that Mimics Enlight CMF is substantially equivalent to the predicate device Mimics (K183105), the primary predicate device, and also substantially equivalent to its reference devices Proplan CMF (K111641) and OsteoPlan System (K212570). Minor differences in intended use and technological characteristics exist, but performance data demonstrates that Mimics Enlight CMF is as safe and effective and performs as well as the predicate device.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).