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PC/MAC version:
The RealGUIDE software is intended for the following uses:

1. Support to the diagnosis for trained professionals. The input DICOM files acquired by a CT/CBCT scanner are not modified in any way but they are showed to the doctor through the classical imaging and volume rendering techniques. It is a stand-alone product. No information of the patient is modified, all the parameters used for the image processing are read from the DICOM file itself. Neither automatic diagnosis is made, nor automatic disease detection is performed. This software is not connected to any medical instrumentation and it doesn't control any medical or energy supplying device. The user imports DICOM data coming from any CT/CBCT imaging device and the software enables him to view the Patient exam in different multi-planar 2D images and easily reconstruct the 3D volume for an immediate visualization of bone structures and surrounding tissues.
2. Virtual oral and maxillofacial surgery planning. Doctors can plan virtual implants and surgeries on 2D/3D reconstructions and export the projects in open or proprietary format for further processing. The user can choose different implant models (for example dental implants models) from a library provided by the Manufacturers and simulate the positioning in the Patient reconstructed volume (this operation is called "virtual plan")
3. Dental/maxillofacial surgical guides and prosthetic modelling. The virtual plan is used to design a surgical guide that is used by the doctor to drive the surgery drills according to the planned implants direction and depth. This surgical guide can be manufactured by any 3D printer working from STL files. The user can also design the patient (typically a denture) with the surface and volume free-form tools implemented in the result is exported in STL format for 3D printing or CAD/CAM technologies.

Mobile version:
The RealGUIDE software APP is intended for the following uses:

1. Projects visualization and editing. The input PROJECT files, pre-processed with the RealGUIDE desktop version, are used by trained professionals to evaluate the implants projects, edit them with other colleagues through the cloud, as well as for a more effective Patient treatment communication.
   The RealGUIDE APP version is NOT INTENDED for managing a 3D diagnosis starting from DICOM images, due to the mobile devices screen resolution limitations. For this reason, the APP is not reading directly the DICOM files but only pre-processed project files, exported through the cloud by the RealGUIDE desktop version.

RealGUIDE Graphic Station is a fully-featured 3D imaging application in medicine. Its unique open architecture and modular framework make customization options trivial. RealGUIDE Graphic Station is meant to be a multiplatform application, running on PC, MAC and mobile devices (not provided by 3DIEMME). The RealGUIDE software is capable of displaying oral/maxillofacial radiology. The user is then able to navigate through different views, segmented analysis (cross sections), and 3D perspective. In addition, the user is able to simulate various objects within the radiograph for the purpose of treatment planning.
Once treatment planning and visual simulation is complete, users can generate reports and simulated images for the purpose of evaluation and diagnosis, as well as perform a surgical guide and prosthesis modelling, to be exported in STL format for the manufacturing with any RP or CAD/CAM machine.
The output format of the software is a STL file, mainly focused on dental, maxillofacial and orthognatic surgery. A list of the possible devices that can be modelled with the software is reported below:

• -Surgical guides for dental implants and surgical screws planning
• -Bone cutting and bone reduction guides for maxillofacial surgery
• -Bone graft models for mandible/maxilla regenerative procedures
• -Dental and maxillofacial prosthesis

Here's an analysis of the acceptance criteria and the study information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly present a table of acceptance criteria with specific performance metrics (e.g., accuracy, sensitivity, specificity, or error rates). Instead, it makes general claims about the device being tested and functioning as intended, and being substantially equivalent to predicate devices.

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

• Sample Size: The document states that "Significant clinical studies have been performed by different medical professionals on many CT/CBCT images." It does not provide an exact number or range for the "many" CT/CBCT images.
• Data Provenance: The document does not explicitly state the country of origin of the data. It mentions "The input DICOM files acquired by a CT/CBCT scanner" for the PC/MAC version. The retrospective or prospective nature of the data is not specified directly, though using "input DICOM files acquired" could imply retrospective use of existing data.

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

• Number of Experts: The document states that "Significant clinical studies have been performed by different medical professionals". It does not specify the exact number of medical professionals involved.
• Qualifications of Experts: The document refers to them as "different medical professionals" and "leading doctors" who have published relevant scientific literature. It does not provide specific qualifications such as years of experience or subspecialty (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set:

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for establishing ground truth or evaluating the test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance:

The document does not indicate that an MRMC comparative effectiveness study was performed to measure how much human readers improve with AI assistance. The described "clinical studies" focused on the software's effectiveness in improving surgical planning and diagnostic approach, and verifying data correspondence, but not in a comparative "with AI vs. without AI" reader study setup. The RealGUIDE software as described primarily functions as an imaging and planning tool, not an AI-driven diagnostic aid that assists human readers in real-time interpretation. It explicitly states: "Neither automatic diagnosis is made, nor automatic disease detection is performed."

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

The "PC/MAC version" is described as a "stand-alone product" in the context of its diagnostic support function and not being connected to medical instrumentation. However, this refers to the software itself not requiring external hardware to function, rather than an "algorithm-only" performance evaluation. The overall use of the device is for "trained professionals" for "Support to the diagnosis" and "Virtual oral and maxillofacial surgery planning," implying a human-in-the-loop workflow.

7. The Type of Ground Truth Used:

The document implies that the ground truth for "verifying that the data shown by RealGUIDE were correspondent to the patient's anatomical features" was established through clinical assessment and comparison to actual patient anatomy/features (presumably from the original CT/CBCT images and potentially outcomes). It also states that "Results of these studies are provided in a separate supplement" and "Relevant scientific literature has been published... on the effectiveness of medical imaging technology," suggesting comparison to established medical understanding and potentially expert consensus on anatomical representations.

8. The Sample Size for the Training Set:

The document does not provide any information regarding the sample size used for the training set for the software. This is common for 510(k) summaries where the focus is on performance validation rather than details of the development process.

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

Since no information is provided about a training set, there is no information on how its ground truth was established.

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NobelClinician® (DTX Studio Implant) is a software interface for the transfer and visualization of 2D and 3D image information from equipment such as a CT scanner for the purposes of supporting the diagnostic process, treatment planning and follow-up in the dental and cranio-maxillofacial regions.

NobelClinician® (DTX Studio Implant) can be used to support guided implant surgery and to provide design input for and review of dental restorative solutions. The results can be exported to be manufactured.

NobelClinician® is a software interface used to support the image-based diagnostic process and treatment planning of dental, cranio-maxillofacial, and related treatments. The product will also be marketed as DTX Studio implant.

The software offers a visualization technique for (CB)CT images of the patient for the diagnostic and treatment planning process. In addition, 2D image data such as photographic images and X-ray images or surface scans of the intra-oral situation may be visualized to bring diagnostic image data together. Prosthetic information can be added and visualized to support prosthetic implant planning. The surgical plan, including the implant positions and the prosthetic information, can be exported for the design of dental restorations in NobelDesign® (DTX Studio design).

Surgical planning may be previewed using the software and the related surgical template may be ordered.

This document is a 510(k) premarket notification summary for the NobelClinician® (DTX Studio Implant) software. The information provided heavily focuses on regulatory comparisons to a predicate device rather than detailed study protocols and results for meeting specific acceptance criteria in a typical clinical performance study.

Based on the provided text, the device is a "Picture archiving and communications system" (PACS) software, classified under 21 CFR 892.2050. The performance data presented are primarily non-clinical.

Here's an attempt to answer your questions based only on the provided text:

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

The document does not explicitly present a table of acceptance criteria and reported device performance in the manner typically found in a clinical study report. Instead, it states that "Software verification and validation per EN IEC 62304:2006" was performed. This implies that the acceptance criteria would be related to the successful completion of these verification and validation activities, demonstrating that the software meets its specified requirements and is fit for its intended use, as defined by the standard.

Since specific performance metrics (e.g., accuracy, precision) for diagnostic or treatment planning tasks are not quantified or presented in this regulatory summary, a table showing such criteria and performance cannot be constructed from this document.

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 provide details on the sample size used for a test set or the provenance of any data beyond indicating "non-clinical studies". This suggests that a traditional clinical test set with patient data for evaluating performance metrics was not the focus of the "performance data" section in this 510(k) summary. The listed activity "Software verification and validation per EN IEC 62304:2006" typically involves testing against synthetic data, simulated scenarios, or existing clinical datasets used for functional and performance testing of software, rather than a prospective clinical study with a defined "test set" in the sense of patient cases.

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 in the document. Given that the performance data mentioned refers to "Software verification and validation per EN IEC 62304:2006," it is unlikely that a formal ground truth establishment by a panel of clinical experts for a test set (as in a diagnostic accuracy study) was conducted or reported in this summary.

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

This information is not provided in the document.

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 mention any multi-reader multi-case (MRMC) comparative effectiveness study. The "performance data" section is limited to "Non-Clinical Studies - Software verification and validation per EN IEC 62304:2006".

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

The document does not explicitly state whether a standalone algorithm-only performance study was conducted. The device is described as "a software interface for the transfer and visualization of 2D and 3D image information from equipment... for the purposes of supporting the diagnostic process, treatment planning and follow-up...". This indicates a human-in-the-loop context for its intended use. The verification and validation activities would assess the software's functional correctness for these tasks.

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

Since the document only refers to "Software verification and validation per EN IEC 62304:2006" as performance data, the specific type of "ground truth" used for these non-clinical tests is not detailed. For software testing, ground truth could involve:

• Pre-defined expected outputs for given inputs.
• Comparisons to known good results from previous versions or manually calculated values.
• Adherence to specifications and design requirements.
  Formal clinical ground truth (like pathology or outcomes data) is not mentioned.

8. The sample size for the training set

This document does not indicate that the device involves AI/machine learning requiring a "training set" in the conventional sense. The "performance data" refers to software verification and validation, which focuses on the deterministic functionality of the software according to its specifications, not statistical learning from data.

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

As there is no mention of an AI/machine learning component requiring a training set, this information is not applicable and not provided in the document.

In summary, the provided FDA 510(k) summary focuses on demonstrating substantial equivalence to a predicate device based primarily on non-clinical software verification and validation activities. It does not contain detailed information about a clinical performance study with specific acceptance criteria, test sets, expert ground truth establishment, or comparative effectiveness studies of the type you've inquired about.

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