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Materialise Dental's SimPlant Go software is indicated for use as a medical front-end software that can be used by medically trained people for the purpose of visualizing gray value images. It is intended for use as a pre-operative software program for simulating /evaluating dental implant placement and surgical treatment options.

SimPlant Go allows the individual patient's CT image to be assessed in a three-dimensional way, to see the anatomical structures without patient contact or surgical insult. It includes features for dental implant treatment simulation. Additional information about the exact geometry of the tooth surfaces can be visualized together with the CT data and periodontic procedures with dental implants can be simulated. The output file is intended to be used in conjunction with diagnostic tools and expert clinical judgment.

The provided text describes the 510(k) submission for SimPlant Go, a pre-operative software program for simulating and evaluating dental implant placement. The document focuses on demonstrating that SimPlant Go is substantially equivalent to a predicate device (SimPlant 2011; K110300) rather than presenting a performance study with specific acceptance criteria and detailed quantitative results.

Here's an analysis based on the information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not provide a table of acceptance criteria with specific quantitative performance metrics (e.g., accuracy, sensitivity, specificity, or error margins) for SimPlant Go. Instead, it describes various software validation and testing activities and concludes with a qualitative statement of equivalence.

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

• Test Set Sample Size:
  • For design validation by Macadamian: The text mentions "interviews and usability tests were performed" but does not specify the number of cases or data points used in these tests.
  • For beta validation: "additional usability tests were performed with in total 28 external users and 5 internal users." This refers to the number of users, not necessarily the number of clinical cases or data sets they evaluated. The number of cases is not specified.
  • For clinical case planning validation: The text states "Clinical case planning in SimPlant GO was validated" but does not specify the number of clinical cases used.
• Data Provenance: Not explicitly stated. The nature of the device (pre-operative planning software) suggests that the data would be medical images (CT scans). The country of origin and whether the data was retrospective or prospective are not mentioned.

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

The document does not describe the establishment of a "ground truth" for the test set in the traditional sense of expert consensus on image interpretation or pathology. The validation activities focus on software functionality and usability.

• Design validation involved an "external usability company i.e. Macadamian." Their qualifications are not detailed beyond being a "usability company."
• Beta validation involved "external users" and "internal users." Their qualifications are not specified, though the device is intended for "medically trained people."

4. Adjudication Method for the Test Set

No explicit adjudication method (e.g., 2+1, 3+1) is mentioned, as the validation focuses on comparing the software's output to the predicate device and usability, rather than expert interpretation of medical findings against a "ground truth."

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

No multi-reader multi-case (MRMC) comparative effectiveness study is mentioned. The submission primarily focuses on functional equivalence to a predicate device and usability, not on comparing human reader performance with and without AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The validation conducted appears to be a form of standalone testing in that the software's output was compared to the predicate device's output. The statement "Compared to the predicate device SimPlant 2011, SimPlant Go yielded an identical output when using identical input data" suggests an algorithm-only comparison for functional correctness. However, this is presented within the context of demonstrating substantial equivalence, not as a standalone performance study with specific metrics like sensitivity or specificity.

7. Type of Ground Truth Used

The primary "ground truth" for demonstrating performance is implicitly the output of the predicate device (SimPlant 2011) when subjected to identical input data. For usability testing, the 'ground truth' would be user feedback and whether "validation criteria were met" as determined by the usability studies. There is no mention of pathology, outcomes data, or consensus from clinical experts for specific diagnostic or planning accuracy.

8. Sample Size for the Training Set

The document does not mention a separate "training set" or its sample size. This suggests that the device, being an updated version of existing planning software (SimPlant 2011), likely relies on well-established algorithms and logic rather than a machine learning model that requires a distinct training phase with a large labeled dataset. The testing focused on ensuring the new C# implementation produced identical results and maintained usability.

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

Since no training set is mentioned, the method for establishing its ground truth is not applicable or described in the provided text.

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The SimPlant Immediate Smile System is intended for use in treatment planning and placement of dental implants, in order to restore masticatory function.

The SimPlant Immediate Smile System is intended for use in treatment planning and placement of dental implants, in order to restore masticatory function. The SimPlant Immediate Smile System enables a predictable dental implant restoration procedure according to case planning done by the clinician.

The SimPlant Immediate Smile System enables a provisional prosthesis to be produced prior to and attached in the same session as implant installation.

The SimPlant Immediate Smile System includes SimPlant software that provides a method of importing medical imaging information from radiological imaging systems such as a Computer Tomography (CT) or Magnetic Resonance Imaging (MRI) to a computer file that is usable in conjunction with other diagnostic tools and expert clinical judgment. Visual representations of the imaged anatomical structures (e.g. the jaw) are derived, allowing for a three-dimensional assessment of the patient without patient contact. SimPlant enables the clinician to plan the dental implant positions including orientations pre-operatively in a virtual, 3D environment. The case planning can be used to produce patient specific SurgiGuide guides, thus transferring the virtual case planning into physical tools enabling the intra-operative preparation of the implant sites for the installation of implants in accordance to the virtual case planning.

The SimPlant Immediate Smile System is based upon knowledge of the locations and orientations of the implants prior to surgery. This knowledge enables the production of a SurgiGuide surgical guide. Aided by the SurgiGuide surgical guide, the implant sites can be prepared and the dental implants placed in the predetermined locations, enabling the immediate installation of the custom-made prefabricated provisional Immediate Smile bridge.

The SimPlant Immediate Smile System is a medical device intended for use in treatment planning and placement of dental implants. The device includes SimPlant software for image processing and pre-operative planning, and enables the production of patient-specific SurgiGuide surgical guides and prefabricated provisional Immediate Smile bridges.

Here's an analysis of the acceptance criteria and study data provided:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes and Data Provenance

• Test Set (Clinical Evaluation):
  • Sample Size: 20 patients
  • Data Provenance: Clinical setting, "different countries" (retrospective or prospective not specified, but the phrase "Case follow-up was done after 12 weeks" suggests it was a prospective study).
• Test Set (Bench Top Testing / Engineering Tests):
  • Specific sample sizes for individual bench tests (e.g., number of bridges for fracture, number of abutments for pull-out) are not provided, only the general statement that "Several Engineering tests and evaluations were undertaken."
• Training Set: Not explicitly mentioned in the provided text. The software validation refers to testing against "specifications," but a distinct "training set" for an AI/algorithm is not discussed in the context of this device. This suggests the device is likely rule-based or uses traditional image processing, rather than a machine learning model that requires a labeled training set in the modern sense.

3. Number of Experts and Qualifications for Ground Truth

• Clinical Evaluation: 11 doctors from different countries. Their specific qualifications (e.g., experience level, specialization beyond "doctor") are not detailed.
• Bench Top Testing / Engineering Tests: Not applicable, as ground truth for these tests is based on physical measurements and engineering specifications, not expert interpretation.
• Software Validation: "The software is validated together with end-users." The number and qualifications of these "end-users" are not specified.

4. Adjudication Method for the Test Set

• Clinical Evaluation: The text states, "Clinical feedback was gathered for all cases relative to the usability, aesthetic result and adequacy of the material properties of the system components. Predefined acceptance thresholds were met for all criteria indicative for the system safety and effectiveness." This implies that the feedback from the 11 doctors was aggregated and compared against predefined thresholds. There's no mention of a formal adjudication method (like 2+1 or 3+1 consensus) for individual cases or disagreements among the doctors.
• Bench Top Testing / Software Validation: Not applicable, as these tests rely on objective measurements and predefined specifications.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study to measure the effect size of human readers improving with AI vs. without AI assistance was not reported. The study focused on the clinical evaluation of the system (including the software and physical components) by doctors in a clinical setting, rather than comparing human reader performance with and without the AI component. The device is for treatment planning and guide fabrication, not for diagnostic interpretation by human readers.

6. Standalone Performance Study

• Yes, a standalone performance assessment of the algorithm/software (without direct human-in-the-loop performance measurement) was conducted through "Software Validation" and "bench top performance testing."
  • Software Validation: "The software is thoroughly tested in accordance with documented test plans and in accordance to internal software development and testing procedures. This test plan is derived from the specifications and ensures that all controls and features are functioning properly. The software is validated together with end-users."
  • Bench Top Performance Testing: This included evaluations of the connection dimensions, predicted micro-movement, and cement bonding, which are standalone assessments of the physical components designed or influenced by the software.

7. Type of Ground Truth Used

• Clinical Evaluation: Clinical feedback from 11 doctors and follow-up data (after 12 weeks) served as the primary ground truth for usability, aesthetic results, material adequacy, and overall safety and effectiveness. This is a form of expert assessment/outcomes data.
• Bench Top Testing / Engineering Tests: Ground truth was based on engineering specifications, physical measurements, and predictive models (e.g., for micro-movement).
• Software Validation: Ground truth was based on predefined specifications for software functionality and controls.

8. Sample Size for the Training Set

• Not explicitly mentioned. The nature of the device (treatment planning software and physical guides) suggests it might not rely on a machine learning model that typically uses a "training set" in the same way as, for example, an image classification AI. The software is described as using "medical imaging information" to derive "visual representations of the imaged anatomical structures," implying a more traditional computational geometry and image processing approach.

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

• Not applicable, as a distinct training set and its associated ground truth establishment method are not described for this device. The software's functionality is validated against specifications that presumably stem from anatomical knowledge and clinical requirements.

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SimPlant Navigator Personalized Dental Care System 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. SurgiGuide® guides and the BIOMET 3i Navigator Surgical Kit, which are used intra-operatively to prepare the osteotomy for placement of BIOMET 3i implants pre-operatively determined in the software.

SimPlant Navigator Personalized Dental Care System provides a method of importing medical imaging information from radiological imaging systems such as Computer Tomography (CT) or Magnetic Resonance Imaging (MRI) to a computer file that is usable in conjunction with other diagnostic tools and expert clinical judgment. Visual representations of the imaged anatomical structures (e.g. the jaw) are derived allowing for a three-dimensional assessment of the patient without patient contact. Dental implant positions including orientations are planned pre-operatively. Computer visualization of the 3D anatomical jaw models, planned implants, planned tooth setup, and numerical measurements assist the surgeon in the creation and approval of a pre-surgical plan. SurgiGuide® guides and the BIOMET 3i Navigator Surgical Kit are used intra-operatively to prepare the osteotomy for placement of BIOMET 3i implants pre-operatively determined in the software.

Here's a breakdown of the acceptance criteria and study information based on the provided 510(k) summary:

Note: The provided document is a 510(k) summary, which often focuses on establishing substantial equivalence to a predicate device rather than presenting detailed clinical study results with specific acceptance criteria and performance against those criteria in a tabular format. The document highlights software and bench testing for compatibility, but not a standalone clinical performance study with defined metrics. Therefore, some sections below will indicate "Not explicitly stated" or will infer information based on the presented context.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not include a table of explicit acceptance criteria with specific performance metrics (e.g., accuracy, sensitivity, specificity, or quantitative error bounds) and corresponding reported device performance values. The performance data section broadly mentions "Software Validation in addition to bench top performance testing was conducted to ensure the compatibility of all system components."

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

• Sample Size for Test Set: Not explicitly stated. The document refers to "Software Validation" and "bench top performance testing" without detailing the specific datasets or number of cases used for these tests.
• Data Provenance (e.g., country of origin, retrospective/prospective): Not explicitly stated.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated.

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

• MRMC Study Conducted?: No. The document does not mention any MRMC study comparing human reader performance with or without AI assistance.
• Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was reported.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study Conducted?: Yes, to a degree. The "Software Validation" and "bench top performance testing" could be interpreted as standalone performance evaluations of the software's functionality and compatibility. However, specific metrics of clinical performance (e.g., accuracy of implant placement prediction) were not provided from such a study. The focus is on ensuring the software correctly performs its intended functions (image segmentation, pre-operative planning, transfer of information).

7. Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated for specific metrics. The software's function involves processing medical images (CT/MRI) for 3D reconstruction and pre-operative planning. The "acceptance testing" and "formal testing" likely validated the software's output against expected computational results or pre-defined clinical scenarios, but the source of the "ground truth" for these comparisons is not detailed. Given the nature of the device (planning software), ground truth would typically relate to the accuracy of anatomical visualization and the precise positioning of planned implants relative to a known anatomical model or expert-defined optimal plan.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device is explicitly described as an "Image processing system" leveraging "Visual representations" and "numerical measurements" for pre-operative planning based on CT/MRI data. It's a software tool for clinicians to use, not a machine learning or AI-driven diagnostic algorithm that would typically require a training set in the conventional sense for learning patterns from data. The software's design is based on established imaging and anatomical principles, rather than being "trained" on a large dataset of patient cases to learn to identify patterns or make predictions.

9. How Ground Truth for the Training Set Was Established

• Ground Truth for Training Set: Not applicable, as this device does not appear to utilize a training set in the context of machine learning or AI. Its functionality is based on deterministic algorithms for image processing and 3D visualization.

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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 Dental's SimPlant Ortho; Vistadent 3D software is indicated for use as a medical front-end software that can be used by medically trained people for the purpose of visualizing gray value images. It 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 used as a software system for simulating/evaluating orthodontic treatment i.e. dental bite options.

Simplant Ortho provides a method of segmenting CT images. This file allows the individual patient's CT image to be assessed in a three-dimensional way, to see the anatomical structures without patient contact or surgical insult. It includes features for cephalometric analysis of the patient and orthodontic treatment simulation. Additional information about the exact geometry of the tooth surfaces can be visualized together with the CT data and orthodontic procedures with TADs (temporary anchorage devices) can be simulated.

Osteotomies and distractions can be visualized to simulate the desired relation of both jaws and the result on the soft tissue profile of the patient can be visualized.

The output file is intended to be used in conjunction with diagnostic tools and expert clinical judgment.

SimPlant Ortho; Vistadent 3D is software for simulating/evaluating orthodontic treatment (i.e. dental bite options) programmed in C++ language and running on the Windows operating system.

The provided text describes a 510(k) premarket notification for the "SimPlant Ortho; Vistadent 3D" software. However, it does not contain specific acceptance criteria for the device's performance, nor does it detail a study that proves the device meets such criteria in terms of quantitative metrics (e.g., accuracy, sensitivity, specificity, or error rates).

The "TESTING & VALIDATION" section merely states:
"The software is thoroughly tested in accordance with a documented test plan. This test plan is derived from the specifications and ensures that all controls and features are functioning properly. The software is validated together with end-users."

This is a general statement about their validation process but lacks the specifics requested in your prompt regarding acceptance criteria and performance data.

Therefore, I cannot populate the table or answer the specific questions about the study design, sample sizes, ground truth establishment, or human reader effectiveness because this information is not present in the provided document.

If you have other documents that contain these details, please provide them, and I would be happy to analyze them.

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