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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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IPS CaseDesigner is indicated for use as a software and image segmentation system for the transfer of imaging information from a scanner such as a CT scanner. It is also indicated to support the diagnostic and treatment planning process of cranio-maxillofacial procedures. IPS CaseDesigner facilitates the service offering of individualized surgical aids.

The IPS CaseDesigner software is a tool for the virtual planning of orthognathic surgery. The user is a (oral and maxillofacial) surgeon who needs to plan an orthognathic surgery, and will use the software to plan the osteotomy (bone cutting). This allows the surgeon to re-position the digitally cut pieces in the desired alignment and allows the visualization of how the patient aspect will look like, through soft tissue simulation. The software supports various types of osteotomy procedures (Le Fort I, Sagittal Split, Ramus and Chin).The selection of the appropriate osteotomy procedure for each patient case is at the discretion of the clinician.

The IPS CaseDesigner is a 3D image-based environment that allows the clinician to visualize a patient's anatomy. The creation of the 3D augmented patient model is derived from CBCT (Cone-Beam CT) data (DICOM). This allows the clinician virtually plan and simulate a craniomaxillofacial surgery. The IPS CaseDesigner software has the capability to save a surgical splint file that the clinician can send to the manufacturing facility for surgical splint creation based on the CBCT Data. The software does not have any functionality for designing.

The provided text describes the 510(k) premarket notification for the "IPS CaseDesigner" device (K161634). However, it does not contain detailed information about specific acceptance criteria and the results of a study proving the device meets those criteria.

The document states:

• "Performance Data VII. The following performance data were provided or relied upon in support of the substantial equivalence determination."
• "Software Validation: Software verification and validation testing was conducted on the subject device and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices"."

This indicates that performance data and software validation were submitted to the FDA, but the details of these tests, including specific acceptance criteria, sample sizes, ground truth establishment, or clinical study results (like MRMC studies or standalone performance), are not present in the provided excerpt.

Therefore, I cannot provide a table of acceptance criteria and reported performance, nor can I answer questions about sample sizes, data provenance, expert involvement, adjudication methods, MRMC studies, standalone performance, or ground truth details. That information would typically be found in the detailed performance data or a separate study report, which is not included in this FDA 510(k) summary.

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