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SimPlant 2011 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 scanner. It is also intended as pre-planning software for dental implant placement and surgical treatment.

SimPlant 2011 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 scanner. It is also intended as pro-planning software for dental implant placement and surgical treatment.

The provided text does not contain detailed acceptance criteria and a study proving the device meets those criteria in the typical format of a medical device performance study. Instead, it details a 510(k) premarket notification for SimPlant 2011, focusing on demonstrating substantial equivalence to a predicate device (SimPlant Dr James, K053592).

This type of submission primarily relies on showing that the new device has the same intended use and similar technological characteristics, and that any differences do not raise new questions of safety or effectiveness. As such, the "acceptance criteria" discussed are largely related to software validation and regulatory compliance, rather than specific clinical performance metrics.

However, based on the information provided, here's an attempt to answer your questions, interpreting "acceptance criteria" in the context of this 510(k) submission:

1. Table of Acceptance Criteria and Reported Device Performance

• Software functionality as described in its design.
• Robustness to usual, unexpected, and invalid inputs.
• Adherence to medical device software development lifecycle standards (e.g., ISO 13485:2003, IEC 62304:2006, EN ISO 14971:2007). | - The SimPlant 2011 software was thoroughly tested and originates from the same medical software platform as the cleared predicate (K033849).
• Testing performed included Unit, Integration, IR, Smoke, Formal (General, Reference, Usage), Acceptance, Alpha, and Beta testing.
• Both static (inspections, walkthroughs) and dynamic analysis were used to find and prevent problems and demonstrate run-time behavior.
• "All controls and procedures are functioning properly" as per documented test plan derived from final specifications. Results are on file at Materialise Dental. |
  | Substantial Equivalence:
• Same intended use as the predicate device.
• Similar technological characteristics to the predicate device.
• Any differences in technological characteristics do not raise new questions of safety or effectiveness. | - Intended Use: Identified as "substantially equivalent" for use as a software interface and image segmentation system, and as pre-planning software for dental implant placement and surgical treatment. (Matches predicate's general intended use).
• Technological Comparison: SimPlant 2011 has more features (e.g., ISO Surface, X-Ray Rendering, Segmentation Wizard, advanced virtual teeth, dual scan registration, optical scanner support, occlusion tool, virtual occludator) than the predicate, SimPlant System.
• Conclusion: The submitter states, "considered to be substantially equivalent in design, material and function... It is believed to perform as well as the predicate device." FDA concurrence on substantial equivalence was granted. |
  | Safety & Effectiveness:
• Device does not contact the patient.
• Device does not deliver medication or therapeutic treatment.
• Application of risk management to devices. | - The product "does not contact the patient and does not deliver medication or therapeutic treatment."
• Risk management was applied in accordance with EN ISO standard 14971:2007. |

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

The document does not specify a "test set" in the context of clinical data or patient images for performance evaluation. The "tests" mentioned are primarily related to software engineering and validation (Unit testing, Integration testing, etc.) to ensure the software itself functions as designed. There is no mention of a specific dataset of patient images used to evaluate the clinical performance or accuracy of the segmentation or planning features in a statistically quantifiable manner.

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

Not applicable. As noted above, there's no mention of a clinical "test set" requiring expert-established ground truth for performance evaluation. The validation described is focused on software quality and functionality.

4. Adjudication Method for the Test Set

Not applicable for the same reasons as above.

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, an MRMC comparative effectiveness study is not mentioned or described in this 510(k) submission. The document focuses on demonstrating substantial equivalence of the software's functionality and safety, not on its impact on human reader performance.

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

The document describes "thorough testing" of the software, including various types of software testing (Unit, Integration, Formal, Acceptance, etc.). This would inherently involve evaluating the algorithm's standalone performance in terms of its intended software functions (e.g., segmentation, rendering, planning tools). However, it does not describe a clinical standalone performance study in the sense of an algorithm making a diagnostic or treatment decision without human involvement and comparing its output to a clinical ground truth. The device is explicitly "pre-planning software," implying human-in-the-loop usage.

7. The Type of Ground Truth Used

For the software validation described, the "ground truth" would be the expected behavior or output of the software as defined by its specifications and requirements. For example, during unit testing, the ground truth for a specific module's output would be what the developer intended it to produce given a set of inputs. For integration testing, it would be the correct interaction between modules. There is no mention of clinical ground truth (e.g., pathology, outcomes data, or expert consensus on patient data) being used for performance evaluation.

8. The Sample Size for the Training Set

There is no mention of a training set in the context of machine learning or AI models. This submission is from 2011, and while some "advanced" features are listed (e.g., "Segmentation Wizard"), the documentation does not describe an AI/ML-driven system that would typically require a training set of labeled data in the modern sense. The "training" here refers to software development and validation processes, not machine learning model training.

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

Not applicable, as there is no mention of a training set as understood in AI/ML context.

Summary of Approach in the Document:

The provided document details a 510(k) Special Premarket Notification for SimPlant 2011. The primary focus of a 510(k) is to demonstrate substantial equivalence to a predicate device. This typically involves:

• Comparing Intended Use: Showing the new device has the same purpose.
• Comparing Technological Characteristics: Identifying similarities and differences with the predicate.
• Demonstrating Safety and Effectiveness of Differences: Proving that any novel features or modifications do not introduce new risks or reduce effectiveness. This generally relies on non-clinical performance data (e.g., engineering tests, software validation, bench testing) and adherence to recognized standards, rather than large-scale clinical trials or detailed performance studies with patient data and expert ground truth.

Therefore, the "acceptance criteria" and "studies" mentioned are largely about internal software development validation, quality system compliance, and regulatory comparison, not comprehensive clinical performance evaluation against a defined clinical ground truth.

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Materialise's 3Matic is intended for use as software for computer assisted design and manufacturing of medical exo- and endo-prostheses, patient specific medical and dental/orthodontic accessories and dental restorations.

Software for computer assisted design and manufacturing of medical and dental prostheses.

This 510(k) summary for the 3Matic software does not include a detailed study proving the device meets specific acceptance criteria with reported performance metrics. Instead, it relies on substantial equivalence to predicate devices (SimPlant System and Etkon ES-1) without presenting new performance data for 3Matic itself.

Therefore, many of the requested details about acceptance criteria and study design are not available in the provided document.

Here's a breakdown of what can be extracted and what is not available:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size (Test Set): NA

• Data Provenance: NA

  The document does not describe any specific test set or data used to evaluate 3Matic's performance.

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

• Number of Experts: NA

• Qualifications of Experts: NA

  As there is no described test set or ground truth establishment, this information is not applicable.

4. Adjudication method for the test set

• Adjudication Method: NA

  No test set or ground truth adjudication process is mentioned.

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

• MRMC Study: No.

• Effect Size: NA

  The document does not mention any MRMC study or an assessment of human reader performance with or without AI assistance.

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

• Standalone Performance Study: No.

  No standalone performance study is described for the 3Matic software.

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

• Type of Ground Truth: NA

  No ground truth is described as having been used to evaluate 3Matic.

8. The sample size for the training set

• Sample Size (Training Set): NA

  The document does not provide details about a training set for the 3Matic software.

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

• Training Set Ground Truth Establishment: NA

  No training set or its ground truth establishment is described.

Summary of the Document's Approach:

The provided 510(k) summary for the Materialise 3Matic system establishes its safety and effectiveness through substantial equivalence to existing predicate devices (SimPlant System and Etkon ES-1). This means that the manufacturer asserted, and the FDA agreed, that the 3Matic device is as safe and effective as the predicate devices because it shares similar technological characteristics and is intended for the same use.

This regulatory pathway typically does not require new clinical or performance studies if the differences from the predicate device do not raise new questions of safety or effectiveness. Therefore, the document focuses on demonstrating these similarities rather than presenting novel performance data for 3Matic itself against predefined acceptance criteria.

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This device employs previously scanned DICOM CT images in a software tool which serves as an aid to visualizing and pre-planning of dental implant surgery.

Virtual Implant Placement, or simply VIP, is a software program that will allow dental implant clinicians to pre-plan their implant surgeries and/or to design surgical appliances that will be used during surgery. The program will presents the clinician with various reformatted CT images of their patient's jaw(s), allow the placement and manipulation of virtual implants, and provide measurement and other tools to assist the clinician. In typical usage a dentist evaluating a patient for dental implant surgery will often refer the patient for a CT scan to better visualize the patient's anatomy, and check the amount and density of the bone for its suitability for placing implants. The CT scan site will return the axial images from the CT scan on a CD in industry-standard DICOM format. Upon receipt of the CD, the doctor will "process" the case using VIP. Axial images are well-known to radiologists, but foreign to dentists. Processing involves the removal of unnecessary images which are outside the region of interest, and drawing a curve which will be used for the later reformatting of the data to produce images more familiar to dentists. After opening a disk of images, VIP will display the axial images and thumbnails of these, along with a scout view and a checklist of stops to follow in processing the case. After the case has been processed, the axial data will be processed to make panoramic images, which are parallel to the curve that was drawn during processing, and cross-sectional images, which are perpendicular to the panoramic image. Both types of images are normally generated by the Panorex machines dentists are familiar with. Since the primary purpose of VIP is to aid in the planning of implant surgeries, VIP will allow the surgeon to place simulated implants on the image and to gauge their size and position relative to the surrounding anatomy. The simulated implants will be generic models of standard dental implants, which range from cylindrical to conical. When the data becomes available from various implant manufacturers, VIP will allow the user to pick from specific, currently-manufactured implants to approximately model any of their favorite implants.

The provided text is a 510(k) summary for the Virtual Implant Placement™ (VIP) Dental Implant Surgery Planning Software. It details the device's intended use and compares it to legally marketed predicate devices to establish substantial equivalence.

Based on the provided text, here's an analysis of the requested information:

1. Table of acceptance criteria and the reported device performance:

The document does not explicitly state "acceptance criteria" in a quantitative, measurable form for the device's performance. Instead, it focuses on establishing substantial equivalence to predicate devices through a qualitative comparison of features and intended use. The device's performance is implicitly judged by its ability to perform similar functions as the predicate devices.

No specific quantitative performance metrics (e.g., accuracy, precision, sensitivity, specificity) or corresponding acceptance criteria are provided in this document. The "device performance" is described through its functionalities and comparison to existing devices.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

The document does not mention any specific sample size for a test set or the provenance of any data used for testing. The submission is focused on establishing substantial equivalence based on a comparison of features and indications for use.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

The document does not mention the use of experts to establish ground truth for a test set. No details are provided regarding any clinical validation studies with expert review.

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

Since no test set or expert ground truth establishment is mentioned, there is no information on adjudication methods.

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:

The document does not indicate that an MRMC comparative effectiveness study was done. The device itself is described as a "software tool which serves as an aid," implying human-in-the-loop, but no data on human performance improvement with or without the software is provided.

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

The device is explicitly described as "an aid to visualizing and pre-planning," meaning it's intended to be used with human involvement. Therefore, a standalone (algorithm only) performance assessment would not be directly relevant to its intended use, and no mention of such a study is made.

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

The document does not describe the type of ground truth used because it does not refer to any formal performance study that would require ground truth. The basis for substantial equivalence is primarily a functional and indications-for-use comparison with predicate devices.

8. The sample size for the training set:

The document does not mention any sample size for a training set. As the application describes software for planning based on existing DICOM CT images, it's not clear if a machine learning model requiring a traditional "training set" (in the AI/ML sense) was used or if it's primarily rule-based or image processing software.

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

Since no training set is mentioned, no information is provided on how ground truth for a training set was established.

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The Vimplant is intended for use as a software interface for the transfer of imaging information from a CT scanner and also as a pre-operative software for simulation and evaluation dental implant placement and surgical treatment options.

VImplant™ is a dental implant simulation software for dentists and implantologists. By use of VImplant™, dental practicians can plan and practice their surgery in advance so they can reduce some risks which can happen during their real surgery. Vimplant™ provides very useful and needful functions. It contains functions of implant simulation, manipulation with 2D and 3D mode, nerve identification, and evaluation of bone density. By using Vimplant TM, dental practicians can quickly and easily simulate implant surgery on their desktop PCs. This enables them to conduct implant surgery more effectively by isolating the exact implant position site and angle and to assist in deciding the proper implant diameter, length, etc.

The provided text is a 510(k) summary for the Vimplant™ Dental implant simulation software. It focuses on demonstrating substantial equivalence to predicate devices and describes the software's functionalities and intended use. However, it does not contain the specific information required to answer your questions regarding acceptance criteria, a dedicated study proving device performance against those criteria, or details about ground truth establishment for a test set or training set.

Here's an analysis of what is available and what is missing based on your request:

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

• Missing: The document does not provide a table of acceptance criteria or reported device performance metrics against specific criteria. The submission methodology is one of "substantial equivalence" to existing predicate devices, not performance against pre-defined quantitative acceptance criteria.

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

• Missing: There is no mention of a dedicated test set, its sample size, or the provenance of any data used for testing. The basis of equivalence is functional similarity and technical characteristics to predicate devices.

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

• Missing: As there's no mention of a test set where ground truth was established, this information is not present.

4. Adjudication method for the test set:

• Missing: No specific adjudication method is mentioned as there's no described test set requiring one.

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:

• Missing: This 510(k) summary does not describe an MRMC study. The device is software for planning and simulation, not an AI diagnostic aid that would typically undergo such a study to evaluate reader performance improvement.

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

• Missing: While the device is a standalone software, the document does not describe "standalone performance" in the context of a rigorous, quantitative study with metrics like sensitivity, specificity, etc. It focuses on the capabilities and functions of the software.

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

• Missing: No ground truth is mentioned as there's no performance study described. The "reliability and validity" of the 3D image construction method were "already verified in Vworks™" (a predicate device by the same manufacturer), suggesting reliance on prior work rather than a new ground truth establishment process for Vimplant.

8. The sample size for the training set:

• Missing: This document focuses on the software's features and its substantial equivalence to predicate devices. It does not contain information about a training set, which would typically be relevant for machine learning or AI algorithms that undergo a training phase.

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

• Missing: As there's no training set mentioned, this information is not provided.

Summary of available information:

• Device Name: Vimplant™ Dental implant simulation software
• Intended Use: As a software interface for transferring imaging information from a CT scanner, and as pre-operative software for simulation and evaluation of dental implant placement and surgical treatment options.
• Key Functions: Import DICOM 3.0 data, 2D image reformation (panoramic, cross-sectional), 3D image construction (Surface rendering, verifiable through Vworks™), nerve creation and display, implant simulation (placement, manipulation, property changes), collision detection, sinus bone graft volume estimation, measurement functions (bone density, length, angle, 3D distance), report and image library.
• Predicate Devices: SimPlant System (MATERIALISE N.V., K033849) and V-works™ (CyberMed, Inc., K013878).
• Basis for Clearance: Substantial equivalence to the predicate devices, implying similar safety and effectiveness based on similar technological characteristics and intended use. The 3D image construction's reliability and validity were already verified in the predicate V-works™ system.

The 510(k) summary provided here is characteristic of a submission for a software device that relies on functional equivalence to established technology rather than a novel AI algorithm requiring extensive performance studies with ground truth data.

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