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Axial3DInsight is intended for use as a cloud-based service and image segmentation framework for the transfer of DICOM imaging information from a medical scanner to an output file.

The Axial3DInsight output file can be used for the fabrication of the output file using additive manufacturing methods.

The output file or physical replica can be used for treatment planning.

The output file or physical replica can be used for diagnostic purposes in the field of orthopedic trauma, orthopedic, maxillofacial, and cardiovascular applications.

Axial3DInsight should be used with other diagnostic tools and expert clinical judgment.

Axial3D Insight is a secure, highly available cloud-based image processing, segmentation and 3D modelling framework for the transfer of imaging information either as a 3D printed physical model.

The acceptance criteria and the study proving the device meets them are described below, based on the provided text.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state a table of acceptance criteria with specific quantitative metrics. However, it describes two validation studies and their outcomes, implying that meeting these outcomes constituted the acceptance.

Inferred Acceptance Criteria & Reported Performance:

2. Sample Sizes and Data Provenance

• Test Set Sample Sizes:

  • Clinical Segmentation Performance Study: 12 cases
  • Intended Use Validation Study: 12 cases
  • Machine Learning Model Validation:
    • Cardiac CT/CTa: 4,838 images
    • Neuro CT/CTa: 4,041 images
    • Ortho CT: 10,857 images
    • Trauma CT: 19,134 images

• Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It only mentions the imaging scanner manufacturers and models used in the validation datasets: GE Medical Systems, Siemens, Phillips, and Toshiba.

3. Number of Experts and Qualifications

• Clinical Segmentation Performance Study: 3 radiologists. No specific years of experience are mentioned, but they are described as "radiologists."
• Intended Use Validation Study: 9 physicians. No specific qualifications (e.g., orthopedic surgeon, maxillofacial surgeon, cardiologist) or years of experience are mentioned, only "physicians."

4. Adjudication Method

• For the Clinical Segmentation Performance Study, the "RADPEER" framework was adopted. All cases were scored within the acceptance criteria of 1 or 2a. While RADPEER is a peer review system, the specific adjudication
  method for discrepancies among the 3 radiologists (e.g., majority vote, consensus meeting, 2+1, 3+1) is not explicitly detailed. It only states that all cases met the acceptance criteria, suggesting agreement or successful resolution.
• For the Intended Use Validation Study, no adjudication method is explicitly described beyond "9 physicians reviewing 12 cases" and concluding "successful validation."

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, a MRMC comparative effectiveness study was not explicitly mentioned as being done to evaluate how much human readers improve with AI vs. without AI assistance. The studies described focus on validation of the device's output and the AI models, rather than human-in-the-loop performance improvement. The text mentions that the Axial™ machine learning models are used to generate an initial segmentation, but the final segmentation and validation are done by "Axial3D trained staff," implying a human-in-the-loop process, but no comparative study to measure effect size is presented in this document.

6. Standalone (Algorithm Only) Performance

• Yes, standalone performance of the machine learning models was conducted. The document states: "Axial™- machine learning models were independently verified and validated before inclusion in the Axial3D Insight device." Table 5-4 provides the number of images used for validation for different clinical areas (Cardiac, Neuro, Ortho, Trauma CT), indicating a quantitative assessment of the models themselves. However, the specific metrics (e.g., Dice score, sensitivity, specificity) for this standalone performance are not provided in the text.

7. Type of Ground Truth Used

• For the Clinical Segmentation Performance Study: The ground truth was established by the consensus or review of the 3 radiologists, consistent with a form of expert consensus.
• For the Intended Use Validation Study: The ground truth was based on the expert clinical judgment of the 9 physicians, who reviewed the 3D models and concluded their utility for intended use.
• For the Machine Learning Model Validation: The document states that "The Axial™- machine learning model training data used during the algorithm development was explicitly kept separate and independent from the validation data used." While it doesn't explicitly state the type of ground truth for this segment, it can be inferred that the ground truth for the validation of the machine learning models was also based on expert-derived segmentations used to compare against the model's output.

8. Sample Size for the Training Set

• The document states: "The Axial™- machine learning model training data used during the algorithm development was explicitly kept separate and independent from the validation data used." However, the sample size for the training set is not provided. Only the sample sizes for the validation data are listed (Table 5-4).

9. How Ground Truth for Training Set was Established

• The document does not explicitly describe how the ground truth for the training set was established. It only implies that training data was distinct from validation data. Given the nature of medical image segmentation, it is highly probable that the ground truth for the training set was established through manual segmentation by human experts (e.g., radiologists, clinical experts), but this is an inference and not explicitly stated in the provided text.

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The AVIEW Modeler is intended for use as an image review and segmentation system that operates on DICOM imaging information obtained from a medical scanner. It is also used as a pre-operative software for surgical planning. 3D printed models generated from the output file are for visualization and educational purposes only and not for diagnostic use.

The AVIEW Modeler is a software product which can be installed on a separate PC, it displays patient medical images on the screen by acquiring it from image Acquisition Device. The image on the screen can be checked edited, saved and received.

• -Various displaying functions
  • Thickness MPR., oblique MPR, shaded volume rendering and shaded surface rendering.
  • . Hybrid rendering of simultaneous volume-rendering and surface-rendering.
• -Provides easy to-use manual and semi-automatic segmentation methods
  • Brush, paint-bucket, sculpting, thresholding and region growing. "
  • . Magic cut (based on Randomwalk algorithm)
• -Morphological and Boolean operations for mask generation.
• Mesh generation and manipulation algorithms. -
  • Mesh smoothing, cutting, fixing and Boolean operations.
• -Exports 3d-printable models in open formats, such as STL.
• DICOM 3.0 compliant (C-STORE, C-FIND) -

The provided text describes the 510(k) Summary for AVIEW Modeler, focusing on its substantial equivalence to predicate devices, rather than a detailed performance study directly addressing specific acceptance criteria. The document emphasizes software verification and validation activities.

Therefore, I cannot fully complete all sections of your request concerning acceptance criteria and device performance based solely on the provided text. However, I can extract information related to software testing and general conclusions.

Here's an attempt to answer your questions based on the available information:

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

The document does not provide a quantitative table of acceptance criteria with corresponding performance metrics like accuracy, sensitivity, or specificity for the segmentation features. Instead, it discusses the successful completion of various software tests.

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

The document does not specify the sample size (number of images or patients) used for any of the tests (Unit, System, Performance, Compatibility). It also does not explicitly state the country of origin of the data or whether the data was retrospective or prospective.

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

The document does not provide any information about the number or qualifications of experts used to establish ground truth for a test set. The focus is on internal software validation and comparison to a predicate device.

4. Adjudication method for the test set

The document does not mention any adjudication method for a test set, as it does not describe a clinical performance study involving human readers.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance

No, the provided text does not describe an MRMC comparative effectiveness study involving human readers with or without AI assistance. The study described is a software verification and validation, concluding substantial equivalence to a predicate device.

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

The document describes various software tests (Unit, System, Performance, Compatibility) which could be considered forms of standalone testing for the algorithm's functionality and performance. However, it does not present quantitative standalone performance metrics typical of an algorithm-only study (e.g., precision, recall, Dice score for segmentation). It focuses on internal software quality and compatibility.

7. The type of ground truth used

The type of "ground truth" used is not explicitly defined in terms of clinical outcomes or pathology. For the software validation, the "ground truth" would likely refer to pre-defined correct outputs or expected behavior of the software components, established by the software development and test teams. For example, for segmentation, it would be the expected segmented regions based on the algorithm's design and previous validation efforts (likely through comparison to expert manual segmentations or another validated method, though not detailed here).

8. The sample size for the training set

The document does not mention a training set or its sample size. This is a 510(k) summary for a medical image processing software (AVIEW Modeler), and while it mentions a "Magic cut (based on Randomwalk algorithm)," it does not describe an AI model that underwent a separate training phase with a specific dataset, nor does it classify the device as having "machine learning" capabilities in the context of FDA regulation. The focus is on traditional software validation.

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

As no training set is mentioned (see point 8), there is no information on how its ground truth would have been established.

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The D2P software 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 pre-operative software for surgical planning. For this purpose, the output file may be used to produce a physical replica. The physical replica is intended for adjunctive use along with other diagnostic tools and expert clinical judgement for diagnosis, patient management, and/or treatment selection of cardiovascular, craniofacial, genitourinary, neurological, and/or musculoskeletal applications.

The D2P software is a stand-alone modular software package that provides advanced visualization of DICOM imaging data. This modular package includes, but is not limited to the following functions:

• DICOM viewer and analysis
• Automated segmentation
• Editing and pre-printing
• Seamless integration with 3D Systems printers
• Seamless integration with 3D Systems software packages
• Seamless integration with Virtual Reality visualization for non-diagnostic use.

The provided text does not contain detailed information regarding acceptance criteria, specific study designs, or performance metrics in a structured format that directly addresses all the requested points. The document summarizes the device, its intended use, and its equivalence to a predicate device for FDA 510(k) clearance.

However, based on the limited information available, here's what can be extracted and inferred:

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

The document states: "All performance testing... showed conformity to pre-established specifications and acceptance criteria." and "A measurement accuracy and calculation 3D study, usability study, and decimation study were performed and confirmed to be within specification." It also mentions "Validation of printing of physical replicas was performed and demonstrated that anatomic models... can be printed accurately when using any of the compatible 3D printers and materials."

Without specific numerical thresholds or target values, a detailed table cannot be created. However, the categories of acceptance criteria and the qualitative reported performance are:

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

This information is not provided in the text. There is no mention of sample size for any test set or the origin (country, retrospective/prospective) of the data used for validation.

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

This information is not provided in the text. The document does not detail how ground truth was established for any validation studies.

4. Adjudication method for the test set:

This information is not provided in the text.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance:

The document describes the D2P software as an "image segmentation system," "pre-operative software for surgical planning," and a tool for "transfer of DICOM imaging information." It also mentions the "Incorporation of a deep learning neural network used to create the prediction of the segmentation."

However, there is no mention of an MRMC comparative effectiveness study involving human readers with and without AI assistance, nor any effect size related to human reader improvement. The focus appears to be on the performance of the software itself and the accuracy of physical replicas.

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

Yes, the testing described appears to be primarily standalone performance testing of the D2P software and its ability to produce accurate segmented models and physical replicas. The statement "All performance testing... showed conformity to pre-established specifications and acceptance criteria" without mention of human interaction suggests standalone evaluation.

7. The type of ground truth used:

This information is not explicitly stated in the text. While it mentions "measurement accuracy," "usability," and "accuracy of physical replicas," it does not specify the method used to establish the gold standard or ground truth for these measurements (e.g., expert consensus, pathology, outcomes data, etc.). It can be inferred that for "measurement accuracy" and "accuracy of physical replicas," there would be established objective standards or measurements used as ground truth.

8. The sample size for the training set:

This information is not provided in the text. The document mentions the "Incorporation of a deep learning neural network," which implies a training set was used, but its size is not disclosed.

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

This information is not provided in the text. While a deep learning network was used, the method for establishing the ground truth for its training data is not discussed.

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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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