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SICAT Implant is a software application of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or CBCT scanners. SICAT Implant is intended for use as planning software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. The dental professionals' planning data can be exported from SICAT Implanning data includes in particular the positions and types of implants and drill sleeves to be used in the surgical procedures. This data may be used as input to design and manufacture surgical guides for dental implants.

SICAT Implant V2.0 is a pure software device. SICAT Implant V2.0 is a software application for the visualization of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or CBCT scanners. SICAT Implant V2.0 is intended for use as planning software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. SICAT Implant V2.0 allows to name, position, move, rotate, resize and visualize dental implants and other planning objects (i.e. nerves) within the visualized 3D volume. Thus, dental professionals like implantologists are enabled to precisely plan the positions, orientations, types and sizes of implants to be placed in the patient's mandible/maxilla together with the related surgical procedures. The dental professionals' planning data can be exported from SICAT Implant. This planning data includes in particular the positions, orientations and types of implants and drill sleeves to be used in the surgical procedures. This data may be used as input to design and manufacture surgical quides for dental implants.

The provided text, a 510(k) summary for SICAT Implant V2.0, primarily focuses on demonstrating substantial equivalence to a predicate device (SICAT Implant V1.2) rather than presenting a detailed performance study with explicit acceptance criteria. Therefore, several requested pieces of information are not available in this document.

However, based on the information provided, we can deduce some aspects of the device's validation and the general nature of its acceptance criteria.

1. Table of Acceptance Criteria and Reported Device Performance:

   The document does not provide a quantitative table of acceptance criteria and reported device performance in the format typically seen for novel AI algorithms demonstrating clinical effectiveness. Instead, it relies on demonstrating consistency with the predicate device and success in general software verification and validation activities.

   Inferred Acceptance Criteria (Qualitative) and Reported Performance:

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

   The document does not specify a "test set" in the context of a clinical performance study with a particular sample size of patient data. The testing described is primarily in the realm of software verification and validation (unit tests, integration tests, system verification and validation tests, usability tests). Data provenance (country of origin, retrospective/prospective) related to patient data is not mentioned because this is a software update (V2.0 of SICAT Implant) and its substantial equivalence is being demonstrated against a previous version (V1.2).

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):

   This information is not provided. Since the device is a planning software, and not an AI diagnostic algorithm that produces a "ground truth" for disease detection, the concept of establishing ground truth by expert consensus would not apply in the same way. The software's "accuracy" is defined by its ability to correctly measure and manipulate 3D data as per its specifications (e.g., bone density, nerve canals, implant positions). The ground truth for measurement accuracy would typically be established through phantom or calibrated physical models.

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

   Not applicable in the context of this 510(k) submission, as it focuses on software verification and equivalence rather than a reader study adjudicating findings.

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 MRMC study is mentioned. This device is described as "planning software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments." It's not presented as a device with AI assistance that's being compared to unassisted human readers for diagnostic improvement.

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

   The document states "SICAT Implant V2.0 is a pure software device." It's a planning tool for dental professionals, not an AI algorithm that makes a standalone decision or diagnosis. Its performance is always in the context of aiding a human professional. The stated measurement accuracies (100 µm, 1 degree) could be considered "standalone" technical performance metrics for the software's internal calculations.

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

   For the core functionalities like measurement accuracy, the ground truth would typically be established through validation against known physical phantoms or highly precise imaging standards, as indicated by the stated accuracies of 100 µm and 1 degree. For general software function, the "ground truth" is that the software performs according to its functional specifications, as verified by various software tests.

8. The sample size for the training set:

   This information is not applicable and not provided. The document describes a traditional software development process ("complete reengineering including significant software re-write") rather than a machine learning model that requires a training set.

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

   Not applicable, as no machine learning training set is described.

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• Aiding diagnosis in the oral-maxillofacial region
• Aiding comparisons of different treatment options
• · Aiding endodontic treatment planning
• · Aiding treatment planning for endodontic surgical guides

SICAT Endo is a pure software device. SICAT Endo is a software tool intended for viewing and analyzing medical information:

• medical 3D volume data such as volumetric X-ray data from Cone Beam CT (CBCT) and CT scanners, and
• . intraoral images, and
• . 3D optical surface data like optical impression data from optical scanners, and
  SICAT Endo provides tools for analyzing the root canal and to mark it visually. It allows to define a drill canal and ordering of a corresponding surgical quide with a drill sleeve.

The provided document is a 510(k) summary for the SICAT Endo device. It outlines the device's intended use, comparison to predicate devices, and non-clinical performance testing. The information required for a detailed description of acceptance criteria and the study proving compliance is scattered throughout the text.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document explicitly states quantitative acceptance criteria for length and angular measurements.

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

The document does not specify the sample size used for the test set.
It states that "Special bench testing has been performed with non-clinical data" to verify the endodontic planning visualization quality and effectiveness and overall quantitative accuracy of the root canal treatment planning. The provenance of this "non-clinical data" (e.g., country of origin, retrospective/prospective) is not explicitly mentioned. Given the device is for dental applications and comparison to a predicate sold in the EU/USA, the data would likely be from relevant patient populations, but this is not stated.

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

The document does not provide information on the number of experts used or their qualifications for establishing ground truth during the non-clinical bench testing.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method used for the test set.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or the effect size of human readers improving with AI vs. without AI assistance. The testing focuses on "non-clinical data" and "visualization quality and effectiveness," and "quantitative accuracy," implying an evaluation of the software's inherent functions rather than human-in-the-loop performance.

6. Standalone Performance Study

Yes, a standalone study (algorithm-only performance) was done. The "Special bench testing" focused on verifying the "endodontic planning visualization quality and effectiveness" and "overall quantitative accuracy of the root canal treatment planning." This type of testing evaluates the device's performance in isolation.

7. Type of Ground Truth Used

The document states "Special bench testing has been performed with non-clinical data" to verify accuracy. For quantitative accuracy measurements like length and angle, the ground truth would typically be established based on known physical measurements or highly precise anatomical references within the non-clinical data (e.g., phantoms, synthetic models, or highly accurate previous measurements from a gold standard). The specific nature (e.g., expert consensus, pathology, outcomes data) is not explicitly stated beyond "non-clinical data." However, for "quantitative accuracy," it can be inferred that a precisely measured ground truth would be used.

8. Sample Size for the Training Set

The document does not provide any information regarding the sample size for the training set. This is a 510(k) summary for a premarket notification, and such details about internal model development (like training data) are not typically required or disclosed at this stage for a device like this, which is described more as a "visualization" and "planning" software rather than a purely AI-driven diagnostic tool that learns from large datasets. The device description emphasizes "tools for analyzing the root canal and to mark it visually" and "define a drill canal," suggesting a feature-based software rather than deep learning that requires extensive training data.

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

Since no information on a training set is provided, the method for establishing its ground truth is also not available in this document.

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In adult population

• · To reduce or alleviate snoring
• To reduce or alleviate mild to moderate obstructive sleep apnea (OSA)

SICAT OPTISLEEP is a pure hardware device. It is an oral appliance and will be manufactured by the SICAT dental laboratory to and on the order of a dentist, physician or licensed practitioner. Each appliance is customized for patients.

SICAT OPTISLEEP is fitted on the maxillary and mandibular arch covering all teeth. It consists of two main parts (upper and lower arch each) that are interconnected by exchangeable connectors. This device functions as a mandibular repositioner, maintaining the lower jaw in a forward position during sleep. This mechanical protrusion acts to increase the patient's pharyngeal space, improving the ability to exchange air during sleep by reducing mechanical obstructions of the airway. Further this results in lower tendency to snore. The device has an adjustment mechanism, achieved by exchangeable connectors of different length, to adjust the mandibular advancement to be set by the dentist or physician at the time of fitting. The SICAT OPTISLEEP will be worn only during the sleep. The device will be used before replacement up to two years.

The provided document is a 510(k) summary for the SICAT OPTISLEEP device. It focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a study proving the device meets specific acceptance criteria with performance metrics. Therefore, a table of acceptance criteria and reported device performance, and details on ground truth, sample sizes, and expert adjudication are not available in this document.

Here's what can be extracted based on the limitations of the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not available in the provided 510(k) summary. The document focuses on comparing the new device, SICAT OPTISLEEP, to predicate devices (Narval CC and SomnoDent Classic) to establish substantial equivalence. It does not present specific performance metrics against pre-defined acceptance criteria for the SICAT OPTISLEEP device itself. The "Non-Clinical Performance Testing and Verification and Validation Activities" section lists various standards and guidance documents that were followed, implying that compliance with these standards served as a form of acceptance criteria, but no specific quantitative performance results are reported.

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

This information is not available in the provided 510(k) summary. The document mentions "verification and validation activities" including "usability testing" and "bench testing," but does not specify the sample sizes or data provenance for these tests. In a 510(k) submission, clinical study details are often summarized if they are necessary to demonstrate substantial equivalence and typically involve comparing the new device to a predicate rather than a standalone performance study with a test set as described in the prompt.

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

This information is not available in the provided 510(k) summary. Given the absence of a detailed study on a "test set" with ground truth established by experts, this detail is not present.

4. Adjudication method for the test set

This information is not available in the provided 510(k) summary. Since there's no described test set with expert ground truth, an adjudication method is not 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

A multi-reader multi-case (MRMC) comparative effectiveness study and details about human readers improving with AI assistance are not applicable to this device. SICAT OPTISLEEP is described as "a pure hardware device," an oral appliance that is custom-manufactured. It does not involve AI or human readers for interpretation, but rather a physical intervention.

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

A standalone algorithm performance study is not applicable to this device. As noted above, this is a physical, hardware-based medical device, not a software algorithm.

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

This information is not available in the provided 510(k) summary. Since no specific study establishing ground truth for a test set is detailed, the type of ground truth is not mentioned.

8. The sample size for the training set

This information is not available in the provided 510(k) summary. The device is a physical appliance, not an AI/ML algorithm that requires a training set.

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

This information is not available in the provided 510(k) summary. As no training set for an algorithm is applicable, the establishment of ground truth for it is also not applicable.

Summary of what is available from the document relevant to device equivalence:

The document focuses on demonstrating substantial equivalence of the SICAT OPTISLEEP to legally marketed predicate devices rather than proving performance against specific acceptance criteria in a clinical study for the new device.

• Predicate Devices: Narval CC (K113201) and SomnoDent Classic (K050592).
• Mechanism of Action: All three devices (SICAT OPTISLEEP and predicates) are mandibular repositioners that maintain the lower jaw in a forward position during sleep to increase pharyngeal space and improve air exchange, thereby reducing snoring and alleviating mild to moderate OSA.
• Materials: SICAT OPTISLEEP uses Polymethylmethacrylate and Polyamide. Predicates use Acrylic/Stainless steel (SomnoDent Classic) or Polyamide (Narval CC).
• Manufacturing: SICAT OPTISLEEP is produced by milling at a dental laboratory using CAD/CAM technology, similar to the Narval CC predicate (selective laser sintering of polymer) and the general "Dental laboratory" manufacturing for SomnoDent Classic.
• Key Verification/Validation Activities (Non-Clinical):
  • Evaluation of biocompatibility (following ISO 10993 series).
  • Usability testing.
  • Bench testing of compliance with specifications and handling.
  • Compliance with various ISO standards and FDA guidance documents related to medical device labeling, risk management, usability engineering, and biological evaluation.
• Conclusion for Substantial Equivalence: The FDA's review concludes that SICAT OPTISLEEP is substantially equivalent in design, material type, technological characteristics, and function to the predicate devices. Minor differences between the devices do not raise new questions concerning safety and effectiveness.

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• Protection of teeth and restorations from injury due to bruxism.
• Temporary relief of Temporal Mandibular Disorder (TMD) and bruxism by reducing muscle tension.
• Temporary treatment of Temporal Mandibular Disorder (TMD) along with the relief of associated headaches and pains.

Not Found

The provided documents do not contain information about the acceptance criteria, reported device performance, or the details of a study proving the device meets acceptance criteria. The documents are a 510(k) clearance letter from the FDA for the SICAT OPTIMOTION device and its Indications for Use.

The letter explicitly states: "We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices..."

This indicates that the FDA's decision was based on a determination of substantial equivalence to a predicate device, rather than a direct evaluation against specific acceptance criteria presented in a study within this submission.

Therefore, I cannot provide the requested information from the given text.

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SICAT Function is a software application for the visualization and segmentation of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or CBCT scanners. It is also used as a software system to aid qualified dental professionals with the evaluation of dental treatment options. The dental professionals' planning data may be exported from SICAT Function and used as input data for CAD or Rapid Prototyping Systems.

SICAT Function is a pure software device. SICAT Function is a software application for the visualization and segmentation of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or Cone Beam - CT scanners. This information can be complemented by the imaging information from optical impression systems and jaw tracking devices. The additional information about the exact geometry of the tooth surfaces and the mandibular movement can be visualized together with the radiological data. SICAT Function is also used as a software system to aid qualified dental professionals with the evaluation and planning of dental treatment options. The dental professionals' treatment planning information may be exported from SICAT Function to be used as input data for the manufacturing of therapeutic devices such as oral appliances.

The provided text describes the regulatory submission for "SICAT Function," a software device. While it outlines the device's intended use, comparison with predicate devices, and a list of verification and validation activities, it does not contain specific acceptance criteria or a detailed study report proving the device meets those criteria with quantitative performance metrics, sample sizes, or ground truth establishment details.

The document primarily focuses on demonstrating substantial equivalence to predicate devices based on design, material, and function, rather than presenting a standalone performance study with defined acceptance criteria.

However, based on the provided text, here's an attempt to answer the questions, highlighting where information is not available:

1. Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria for the SICAT Function's performance. Instead, it relies on demonstrating that the device "passed all verification and validation activities" and "safety and effectiveness of the product has been demonstrated in the context of its intended use" as compared to predicate devices.

Acceptance Criteria (Implicit from the text):

• Successful correct import, registration, and visualization of jaw motion data.
• Demonstrated safety and effectiveness of image segmentation features.
• Substantial equivalence in design, material, and function to predicate devices for visualization, segmentation, and evaluation of dental treatment options.

Reported Device Performance:

Missing Information Details:

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

• Not Available. The document mentions "Special bench testing has been performed with non-clinical data" (Page 5) but does not specify the sample size (number of cases/patients), the type or origin of this non-clinical 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 (e.g. radiologist with 10 years of experience)

• Not Available. The document does not describe how ground truth was established for the "special bench testing."

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

• Not Available. There is no mention of any adjudication method for establishing ground truth or evaluating test results.

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 Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done. The document primarily describes software verification and validation, along with bench testing. There is no indication of a study involving human readers or comparing performance with and without AI assistance. This device is described as "aid[ing] qualified dental professionals" (Page 1) and "pure software device" (Page 2), implying it's a tool, but no human-in-the-loop performance study is reported.

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

• Yes, implicitly. The "Special bench testing" (Page 5) for jaw motion data and image segmentation appears to be a standalone performance evaluation of the algorithms/software features, as it doesn't mention human interaction. The overall verification and validation activities also support a standalone software evaluation. However, specific quantitative metrics are not provided.

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

• Not Available directly. For the "special bench testing" with "non-clinical data" (Page 5), the method for establishing ground truth is not described. Given the nature of a software device for visualization and segmentation, ground truth would likely be based on idealized or known synthetically generated data for bench testing, or expert manual segmentation/annotation on real data, but this is not specified.

8. The sample size for the training set

• Not Applicable / Not Available. The document does not mention the use of a "training set" in the context of machine learning or AI development. The device is purely software for visualization and segmentation, which might rely on deterministic algorithms, rather than models that require training data. If any machine learning components were present, the training set size is not disclosed.

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

• Not Applicable / Not Available. As no training set is mentioned, the method for establishing its ground truth is also not provided.

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The SICAT Implant V 1.2 is a software application for the visualization of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or DVT scanners. The SICAT Implant V 1.2 is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. The dental professionals' planning data may be exported from SICAT Implant V 1.2 and used as input data for CAD or Rapid Prototyping Systems.

SICAT Implant V1.2 is a pure software device. SICAT Implant V1.2 is a software application for the visualization of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or DVT scanners. SICAT Implant V1.2 is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments.

SICAT Implant V1.2 allows to name, position, move, rotate, resize and visualize dental implants and other planning objects (i.e. nerve canals) within the visualized 3D volume. Thus, dental professionals like implantologists are enabled to precisely plan the positions, orientations, types and sizes of implants to be placed in the patient's mandible/maxilla together with the related surgical procedures.

The dental professionals' planning data may be exported from SICAT Implant V1.2 and used as input data for CAD or Rapid Prototyping Systems.

The provided text describes the SICAT Implant V 1.2 device and its 510(k) submission, but it does not contain information about specific acceptance criteria or a detailed study proving the device meets those criteria, as typically found in a clinical study report.

The text focuses on establishing substantial equivalence to predicate devices based on intended use, features, and technical characteristics. It mentions "Performance testing to validate the safety and effectiveness of the SICAT Implant V 1.2 system included validation testing and bench tests of the software functions," but provides no details on these tests, their results, or the methodology.

Therefore, I cannot fulfill your request for a table of acceptance criteria, reported device performance, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth details, or training set information. This information is not present in the provided 510(k) summary.

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GALILEOS Implant is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical implant treatments. GALILEOS Implant is based on medical imaging information presented by the Sirona GALAXIS 3D viewer and produced by Sirona GALILEOS medical cone beam scanner. The dental professionals' input information may be exported from GALILEOS Implant and used as input data for CAD (Computer-Aided Design) or Rapid Prototyping , Systems.

GALILEOS Implant V1.7 is a pure software device. GALILEOS Implant V 1.7 is a planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical implant treatments. GALILEOS Implant is based on medical imaging information presented by the Sirona GALAXIS 3D viewer and produced by Sirona GALILEOS medical cone beam scanner. GALILEOS Implant V1.7 allows to name, position, move, rotate, resize and visualize dental implants and other planning objects (i.e. nerve canals) within the visualized 3D volume. Thus, dental professionals like implantologists are enabled to precisely plan the positions, orientations, types and sizes of Implants to be placed in the patient's mandible/maxilla together with the related surgical procedures. The dental professionals' planning data may be exported from GALILEOS Implant V1.7 and used as input data for CAD (Computer-Aided Design) or Rapid Prototyping Systems.

The provided document is a 510(k) summary for the GALILEOS Implant V 1.7 software. It describes the device's intended use and claims substantial equivalence to predicate devices. However, it does not contain information about specific acceptance criteria, a study that proves the device meets those criteria, or the detailed performance metrics of the device itself.

The submission focuses on establishing substantial equivalence based on intended use, features, and technical characteristics, and mentions "Performance testing to validate the safety and effectiveness of the GALLEOS Implant V 1.7 system included validation testing and bench tests of the software functions." However, the results of this performance testing are not provided in this summary.

Therefore, I cannot provide the requested information. The document explicitly states:

• There is no table of acceptance criteria and reported device performance.
• There is no sample size used for a test set, nor data provenance.
• There is no information on the number or qualifications of experts used for ground truth.
• There is no adjudication method mentioned.
• There is no multi-reader multi-case (MRMC) comparative effectiveness study mentioned, nor any effect size.
• There is no standalone (algorithm only) performance study detailed.
• The type of ground truth used is not specified.
• There is no sample size for the training set.
• There is no information on how ground truth for the training set was established.

The document is a regulatory filing focused on establishing substantial equivalence, not a detailed technical report of performance studies.

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SICAT Implant is a software application for the visualization of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or DVT scanners. SICAT Implant is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. The dental professionals' planning data may be exported from SICAT Implant and used as input data for CAD or Rapid Prototyping Systems.

SICAT Implant is a pure software device. SICAT Implant is a software application for the visualization of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or DVT scanners. SICAT Implant is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. SICAT Implant allows to name, position, move, rotate, resize and visualize dental implants and other planning objects (i.e. nerve canals) within the visualized 3D volume. Thus, dental professionals like implantologists are enabled to precisely plan the positions, orientations, types and sizes of implants to be placed in the patient's mandible/maxilla together with the related surgical procedures. The dental professionals' planning data may be exported from SICAT Implant and used as input data for CAD or Rapid Prototyping Systems.

The provided text is a 510(k) summary for the SICAT Implant, a software device. It states that the device is "substantially equivalent" to predicate devices (SimPlant System and GALILEOS Implant System) based on intended use, features, and technical characteristics. The summary mentions "Performance testing to validate the safety and effectiveness of the SICAT Implant system included validation testing and bench tests of the software functions." However, it does not provide details regarding specific acceptance criteria, study methodologies, sample sizes, ground truth establishment, expert involvement, or statistical results for performance.

Therefore, the requested details about acceptance criteria and the study proving the device meets them cannot be extracted from this document. The document primarily focuses on establishing substantial equivalence to previously cleared devices rather than presenting a detailed performance study against specific acceptance criteria.

Based on the provided text, the following information is missing:

• A table of acceptance criteria and the reported device performance: Not provided.
• Sample sized used for the test set and the data provenance: Not provided.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not provided.
• 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: Not indicated or described.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not explicitly stated or detailed. The device is described as "aid[ing] qualified dental professionals," implying a human-in-the-loop scenario, but no performance metrics are given.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not provided.
• The sample size for the training set: Not provided.
• How the ground truth for the training set was established: Not provided.

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GALILEOS Implant is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical implant treatments. GALILEOS Implant is based on medical imaging information presented by the Sirona GALAXIS 3D viewer and produced by Sirona GALILEOS medical cone beam scanner. The dental professionals' input information may be exported from GALILEOS Implant and used as input data for CAD or Rapid Prototyping Systems.

GALILEOS Implant is a pure software device. GALILEOS Implant is an Add-On to the 3D-viewing software Sirona GALAXIS. GALILEOS Implant adds features for pre-operative simulation / evaluation of dental implant placement and surgical treatment options. GALILEOS Implant allows to name, position, move, rotate, resize and visualize generic dental implants and other planning objects (i.e. nerve canals) within the 3D volume data visualized by Sirona GALAXIS. Thus, dental professionals like implantologists are enabled to precisely plan the positions, orientations, types and sizes of implants to be placed in the patient's mandible/maxilla together with the related surgical procedures.

The GALILEOS Implant 510(k) summary does not contain the detailed information necessary to complete all sections of your request regarding acceptance criteria and a study proving the device meets those criteria. Specifically, it lacks a dedicated section outlining acceptance criteria, a detailed study design, sample sizes for training or test sets, expert qualifications, or adjudication methods.

This document describes the device as substantially equivalent to a predicate device (SimPlant System K033849) based on intended use, features, and technical characteristics. It mentions "Performance testing to validate the safety and effectiveness of the GALILEOS Implant system included validation testing and bench tests of the software functions," but provides no details of these tests, their results, or the criteria used to judge them.

Here's a breakdown of what information is not available in the provided text, and what can be inferred:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified. It can be inferred that the software processes 3D volume data from the Sirona GALILEOS medical cone beam scanner, but the origin of data used for testing (e.g., country, retrospective/prospective) is not mentioned.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The device is intended for "qualified dental professionals," but whether these professionals were involved in establishing ground truth for testing is not detailed, nor are their credentials.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

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

• Was an MRMC study done? Not mentioned. The document focuses on demonstrating substantial equivalence of the software rather than a comparative effectiveness study involving human readers with and without AI assistance.
• Effect Size: Not applicable, as no MRMC study is reported.

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

• Was a standalone study done? Yes, implicitly. The "validation testing and bench tests of the software functions" would fall under standalone performance assessment. However, no specific metrics or results from such a study are provided. The device itself is described as "pure software," thus any "performance testing" would inherently be standalone.

7. Type of Ground Truth Used

• Type of Ground Truth: Not specified. Given the nature of dental implant planning software, it's possible that ground truth could involve:
  • Expert consensus on optimal implant placement.
  • Comparison to physically planned models or surgical outcomes (though outcomes data is less likely for initial software validation).
  • Pathology (e.g., histological verification) is highly unlikely for this type of software.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not specified. (It's possible, given the time frame of the submission (2006) and the nature of "pure software" for planning, that traditional machine learning 'training' data in the modern sense might not have been a primary component of its development or validation. It might have been rule-based or algorithmic.)

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

• How Ground Truth Was Established (Training Set): Not specified. (As above, if traditional machine learning was not the primary methodology, then a 'training set' ground truth might not be applicable in the same way.)

Summary of what is known/inferred:

• Device Name: GALILEOS Implant
• Intended Use: Planning and simulation software to aid qualified dental professionals in placing dental implants and planning surgical treatments, based on medical imaging from Sirona GALILEOS 3D viewer.
• Regulatory Clearance: 510(k) (K061472) based on substantial equivalence to SimPlant System (K033849).
• Performance Claim: "Performance testing to validate the safety and effectiveness of the GALILEOS Implant system included validation testing and bench tests of the software functions."
• Nature of Software: Pure software device, an add-on to Sirona GALAXIS 3D-viewing software.

The provided 510(k) summary is typical for this type of submission from that era, focusing on substantial equivalence rather than detailed performance study reports with specific metrics, acceptance criteria, and ground truth methodologies that are common in more recent AI/ML device submissions.

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