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EzDent-i is dental imaging software that is intended to provide diagnostic tools for maxillofacial radiographic imaging. These tools are available to view and interpret a series of DICOM compliant dental radiology images and are meant to be used by trained medical professionals such as radiologist and dentist.

EzDent-i is intended for use as software to acquire, view, save 2D image files, and load DICOM project files from panorama, cephalometric, and intra-oral imaging equipment.

EzDent-i is a device that provides various features to acquire, transfer, edit, display, store, and perform digital processing of medical images. EzDent-i is a patient & image management software specifically for digital dental radiography. It also provides server/client model so that the users upload and download clinical diagnostic images and patient information from any workstations in the network environment.

EzDent-i supports general image formats such as JPG and BMP for 2D image viewing as well as DICOM format. For 3D image management, it provides uploading and downloading support for dental CT Images in DICOM format. It interfaces with a 3D imaging software made by our company, the Ez3D-i (K131616, K150761, K161246, K163539, K173863, K190791, K200178) but the EzDent-i itself does not view, transfer or process 3D radiographs.

EzDent-i supports the acquisition of dental images by interfacing with OpenCV library to import the intra-oral camera images. It also supports the acquisition of CT/Panoramic/Cephalo/Intra-Oral Sensor /Intra-Oral Scanner images by interfacing with Xray capture software.

The provided text describes a 510(k) premarket notification for a dental imaging software named EzDent-i. The primary purpose of this submission is to demonstrate substantial equivalence to a previously cleared predicate device, not to showcase the performance of an AI algorithm based on comparative studies. Therefore, much of the requested information regarding acceptance criteria and performance studies (especially relating to AI, human readers, and ground truth establishment) is not detailed in this document because it is not typically required for a software device demonstrating substantial equivalence by adding convenience features.

However, based on the context of the document, we can infer some details and explicitly state what is not provided.

Here's an attempt to answer your questions based on the provided text, indicating where information is not present:

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

The document does not explicitly state quantitative acceptance criteria or detailed performance metrics. Instead, it relies on demonstrating that the modified device's performance aligns with its intended use and is comparable to the predicate device.

The "Performance Data" section states: "SW verification/validation and the measurement accuracy test were conducted to establish the performance, functionality and reliability characteristics of the modified devices. The device passed all of the tests based on pre-determined Pass/Fail criteria." This indicates that internal testing was performed, and the device met its internal "Pass/Fail criteria," but these specific criteria and results are not provided in this summary.

Given that this is a 510(k) for a medical image management and processing system (not an AI-driven diagnostic aid that independently identifies pathologies), the "performance" here refers to its ability to correctly acquire, view, save, load, and manipulate dental images, similar to its predicate.

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

The document does not specify any sample size for a test set in terms of patient images. The "performance data" section refers to "SW verification/validation and the measurement accuracy test," implying software-level testing rather than clinical study data on a patient image test set. No information is available regarding data provenance (country of origin, retrospective/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 applicable and not provided. The device (EzDent-i) is described as "dental imaging software that is intended to provide diagnostic tools for maxillofacial radiographic imaging. These tools are available to view and interpret a series of DICOM compliant dental radiology images and are meant to be used by trained medical professionals such as radiologist and dentist." It is a viewer and manager, not an AI diagnostic tool that produces a finding requiring expert ground truth for performance evaluation in the described context. The performance verification likely focuses on technical accuracy of image display, manipulation, and data handling, not diagnostic accuracy against a ground truth.

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

Not applicable and not provided. As per point 3, there's no indication of a diagnostic test set requiring adjudication.

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 such study was mentioned or required for this 510(k) submission. This device is a general image management and processing system, not an AI-assisted diagnostic tool that would typically warrant an MRMC study to show human reader improvement with AI assistance.

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

This device is not an algorithm performing a standalone diagnostic task. It is software that provides "diagnostic tools" for viewing and interpreting images by human professionals. Therefore, a standalone algorithm performance evaluation would not be applicable or described here.

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

Not applicable and not provided. As this is not an AI diagnostic device, the concept of ground truth for diagnostic accuracy (e.g., concerning a disease or finding) is not relevant to the described performance evaluations. The "performance" relates to the software's ability to achieve its technical specifications.

8. The sample size for the training set

Not applicable and not provided. This document describes a traditional software upgrade and substantial equivalence claim, not a machine learning or AI device that would have a training set.

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

Not applicable and not provided. As per point 8, there is no mention of a training set for an AI model in this submission.

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EzDent-i is dental imaging software that is intended to provide diagnostic tools for maxillofacial radiographic imaging. These tools are available to view and interpret a series of DICOM compliant dental radiology images and are meant to be used by trained medical professionals such as radiologist and dentist.

EzDent-i is intended for use as software to acquire, view, save 2D image files, and load DICOM project files from panorama, cephalometric, and intra-oral imaging equipment.

EzDent-i is a device that provides various features to acquire, transfer, edit, display, store, and perform digital processing of medical images. EzDent-i is a patient & image management software specifically for digital dental radiography. It also provides server/client model so that the users upload and download clinical diagnostic images and patient information from any workstations in the network environment.

EzDent-i supports general image formats such as JPG and BMP for 2D image viewing as well as DICOM format. For 3D image management, it provides uploading and downloading support for dental CT Images in DICOM format. It interfaces with a 3D imaging software made by our company, the Ez3D-i (K131616 & K150761 & K163533 & K173863 & K190791) but the EzDent-i itself does not view, transfer or process 3D radiographs.

EzDent-i supports the acquisition of dental images by interfacing with OpenCV library to import the intra-oral camera images. It also supports the acquisition of CT/Panoramic/Cephalo/Intra-Oral Sensor images by interfacing with X-ray capture software.

EzDent-i makes it easier to diagnose and analyze 2D dental images with simple and convenient user interface. EzDent-i's main functions are;

• · Easy and convenient data search function for patient information and clinical images
• · Various image viewing format for 2D dental images
• Various image processing functions including adjustment of brightness and contrast for images
• · Measurement function of length and angle for 2D images
• · Dental implant simulation for treatment planning and effective patient consultation
• · Crown simulation for more effective patient consultation
• · Print function supporting various viewing output format

The provided text is a 510(k) summary for the EzDent-i/E2/Prora View/Smart M Viewer dental imaging software. While it states that performance data was collected and the device "passed all of the tests based on pre-determined Pass/Fail criteria," it does not provide specific details about the acceptance criteria, the study itself, or the quantitative performance metrics.

Therefore, I cannot populate the table or answer the specific questions about sample sizes, ground truth establishment, expert qualifications, or MRMC studies. The document focuses on demonstrating substantial equivalence to a predicate device based on similar intended use and technical characteristics, with modifications described as "additional features for user convenience" that "do not raise the questions of safety or effectiveness."

Here's a breakdown of what can be extracted and what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified. (The applicant is based in Republic of Korea, but this does not confirm the data origin).
• Retrospective or Prospective: Not specified.

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

• Not specified. The document indicates that the software's tools are "meant to be used by trained medical professionals such as radiologist and dentist" and that "Results produced by the software's diagnostic, treatment planning and simulation tools are dependent on the interpretation of trained and licensed radiologists, clinicians and referring physicians as an adjunctive to standard radiology practices for diagnosis." However, this pertains to the intended clinical use, not the ground truth establishment for software validation.

4. Adjudication method for the test set:

• Not specified.

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:

• Not specified. The device is described as "dental imaging software that is intended to provide diagnostic tools" and offers "various image processing functions." It is not presented as an AI-powered diagnostic aid that would typically undergo an MRMC study to show human reader improvement with AI assistance. It is classified as "Picture archiving and communications system" (PACS).

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

• Not explicitly stated. Given its classification as PACS software with "diagnostic tools" and "image processing functions," some standalone performance tests would be expected (e.g., accuracy of measurement tools, image processing fidelity), but no details are provided.

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

• Not specified.

8. The sample size for the training set:

• Not applicable as this documentation describes a PACS/imaging software, not an AI/ML algorithm that requires a separate training set. The changes described are primarily feature additions and graphical updates.

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

• Not applicable for the same reason as above.

In summary, the provided 510(k) letter focuses on demonstrating substantial equivalence to a predicate device, primarily through a comparison of intended use and technical specifications. It makes a general statement about performance data collected and tests passed, but it lacks the detailed quantitative information that would typically be available for a device with specific performance claims, especially for AI/ML-driven diagnostics.

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