Search Results

Ez3D-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.

Ez3D-i is intended for use as software to load, view and save DICOM images from CT, panorama, cephalometric and intraoral imaging equipment and to provide 3D visualization, 2D analysis, in various MPR (Multi-Planar Reconstruction) functions.

Ez3D-i is 3D viewing software for dental CT images from CT, panorama, cephalometric and intraoral image equipment in DICOM format with a host of useful functions including MPR, 2-dimensional analysis and 3-dimensional image reformation. It provides advanced simulation functions such as Implant Simulation, Drawing Canal, and Implant Environ Bone Density, etc. for the benefit of effective doctor and patient communication and precise treatment planning.

Ez3D-i's main functions are:

• Image adaptation through various rendering methods such as Teeth/Bone/Soft tissue/MIP
• Versatile 3D image viewing via MPR Rotating, Curve mode
• "Sculpt" for deleting unnecessary parts to view only the region of interest.
• Implant Simulation for efficient treatment planning and effective patient consultation
• Canal Draw to trace alveolar canal and its geometrical orientation relative to teeth.
• "Bone Density" test to measure bone density around the site of an implant(s) .
• Various utilities such as Measurement, Annotation, Gallery, and Report
• 3D Volume function to transform the image into 3D Panorama and the Tab has been optimized for Implant Simulation.
• Provides the Axial View of TMJ, the Condyle/Fossa images in 3D and the Section images, and supports functions to separate the Condyle/Fossa and display the bone density
• STO/VTO Simulation to predict orthodontic treatment/ surgery results with 3D Photo image.
• Segmentation function to get tooth segmentation data from CT, label each segmented tooth data as an object and utilize them in simulation such as tooth extraction, implant simulation, etc.

The provided text describes a 510(k) summary for the Ez3D-i/E3 device, primarily focusing on proving its substantial equivalence to a predicate device (K211791) rather than detailing specific acceptance criteria and a comprehensive study proving the device meets them.

The filing states: "The 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." However, it does not provide the specific acceptance criteria, the detailed results of these tests, or the methodology of the study.

Therefore, many of the requested details cannot be extracted from the given text.

Based on the information provided, here's what can be extracted and what is missing:

Acceptance Criteria and Device Performance

The document does not explicitly state specific acceptance criteria in a quantitative manner or provide a table of reported device performance against such criteria. It generally states that the device "passed all of the tests based on pre-determined Pass/Fail criteria," but these criteria are not detailed.

Study Details

Given the context of a 510(k) for a software update (Ez3D-i v5.4 to Ez3D-i v5.3), the studies conducted appear to be software verification and validation (V&V) and measurement accuracy tests. These are typically internal tests to ensure the new version functions as intended and maintains the performance of the previous version, rather than large-scale clinical trials.

1. Sample sized used for the test set and the data provenance:

• Not explicitly stated for the "measurement accuracy test" or "SW verification/validation." The document mentions the device processes DICOM images from CT, panorama, cephalometric, and intraoral imaging equipment. The data provenance (country of origin, retrospective/prospective) is also not stated.

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

• Not stated. For a software update focusing on functionality and measurement accuracy of an image viewer, ground truth might be established through technical specifications or comparison to known accurate measurements rather than expert consensus on diagnostic interpretations. The document mentions the software is "meant to be used by trained medical professionals such as radiologist and dentist," but it doesn't specify if these professionals were involved in establishing ground truth for testing.

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

• Not stated. This method is typically relevant for human-in-the-loop studies where multiple readers interpret images to establish consensus. For software V&V and measurement accuracy, it's unlikely to be applicable in the traditional sense.

4. 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 study was not done or reported. The device is described as "dental imaging software that is intended to provide diagnostic tools" and is used by professionals as "an adjunctive to standard radiology practices for diagnosis." It is not presented as an AI-assisted diagnostic tool that directly improves human reader performance in the way an AI algorithm for disease detection might be. The focus of the submission is on software functionality and substantial equivalence.

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

• Not explicitly detailed as a "standalone performance study" in terms of diagnostic accuracy. The "measurement accuracy test" could be considered a form of standalone testing for specific functions, but no specific metrics (e.g., sensitivity, specificity for diagnostic tasks) are provided. The device is not an AI algorithm making diagnostic predictions in the absence of a human.

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

• Not explicitly stated. For "measurement accuracy," ground truth would likely be established by known physical dimensions or validated measurements. For general software verification/validation, ground truth often relates to the expected output from a given input based on design specifications.

7. The sample size for the training set:

• Not applicable/Not stated. This device is described as a medical image management and processing system, not an AI/ML device that requires a training set. While it performs "3D visualization, 2D analysis, in various MPR (Multi-Planar Reconstruction) functions," these are standard image processing techniques, not algorithms that learn from data.

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

• Not applicable/Not stated. As it's not described as an AI/ML device, there's no "training set."

Summary of what is known from the document:

• Device Name: Ez3D-i /E3 (K222069)
• Intended Use: Dental imaging software for maxillofacial radiographic imaging, providing diagnostic tools to view and interpret DICOM images from various dental imaging equipment, offering 3D visualization, 2D analysis, and MPR functions. Used by trained medical professionals (radiologists and dentists).
• Predicate Device: Ez3D-i /E3 v.5.3 (K211791)
• Studies Conducted: Software verification/validation and measurement accuracy tests.
• Conclusion: The device passed all tests based on pre-determined Pass/Fail criteria, leading to a conclusion of substantial equivalence to the predicate device.

What is demonstrably missing from the provided text:

• Specific, quantitative acceptance criteria.
• Detailed reported performance data against those criteria.
• Specific sample sizes for the test set.
• Data provenance (country of origin, retrospective/prospective).
• Details on experts and ground truth establishment methodologies for the test set.
• Adjudication methods for the test set.
• Any MRMC study details or effect sizes related to AI assistance.
• Detailed standalone performance metrics (e.g., diagnostic accuracy metrics).
• Ground truth type beyond general "measurement accuracy."
• Training set information (as it's not an AI/ML device).

Ask a specific question about this device

Ez3D-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.

Ez3D-i is intended for use as software to load, view and save DICOM images from CT, panorama, cephalometric and intraoral imaging equipment and to provide 3D visualization, 2D analysis, in various MPR (Multi-Planar Reconstruction) functions.

Ez3D-i is 3D viewing software for dental CT images in DICOM format with a host of useful functions including MPR, 2-dimensional analysis and 3-dimensional image reformation. It provides advanced simulation functions such as Implant Simulation, Drawing Canal, and Implant Environ Bone Density, etc. for the benefit of effective doctor and patient communication and precise treatment planning.

This FDA 510(k) summary for Ewoosoft Co., Ltd.'s Ez3D-i/E3 device (K211791) focuses on demonstrating substantial equivalence to a previous version of the same device (K200178). As such, it does not provide detailed acceptance criteria and a study proving the device meets those criteria in the way one might expect for a novel device or a significantly modified one. Instead, the performance data section states that "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 the study performed was primarily a verification and validation study to ensure the new version performed as expected and was equivalent to the predicate.

Given the information provided, I will extract and present the available details while noting where specific information, such as detailed acceptance criteria and comprehensive study results, is not present in this type of submission.

1. Table of Acceptance Criteria and Reported Device Performance

The FDA 510(k) submission does not provide specific quantitative acceptance criteria or detailed reported device performance metrics for a clinical study comparing the device to ground truth. Instead, it relies on demonstrating substantial equivalence to a predicate device (Ez3D-i/E3, K200178) through software verification and validation, and measurement accuracy tests. The performance is reported in terms of passing pre-determined Pass/Fail criteria.

Note: For this type of submission, detailed performance metrics like sensitivity, specificity, or AUC are not typically required if substantial equivalence is being claimed for minor software updates where the core diagnostic functionality remains unchanged and validated.

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

The document does not specify a "test set" in the context of a clinical performance study with human subjects. The performance data refers to "SW verification/validation and the measurement accuracy test." These typically involve testing the software against pre-defined test cases, simulated data, or existing (potentially de-identified) DICOM images, rather than a prospective clinical dataset.

• Sample Size (Test Set): Not specified.
• Data Provenance: Not specified. Given it's a software verification/validation, the data would likely be a mix of internal test datasets, and potentially de-identified DICOM images used for functionality testing. The country of origin and retrospective/prospective nature are not mentioned.

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

This information is not provided because the submission does not detail a clinical study where ground truth was established by experts for a specific test set. The validation performed was software-centric. The "Indications for Use" statement does, however, mention the intended users: "trained medical professionals such as radiologist and dentist."

4. Adjudication Method for the Test Set

This information is not provided as the submission does not describe a clinical study with expert adjudication of a test set.

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

• Was an MRMC study done? No. The document does not mention any MRMC study. The submission focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance data (software verification/validation).
• Effect size of human reader improvement with AI vs. without AI assistance: Not applicable, as no MRMC study was conducted or reported.

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

• Was a standalone study done? No, not in the typical sense of evaluating diagnostic accuracy of an AI algorithm against ground truth. The device is a "Medical image management and processing system" that provides tools for human interpretation, not an AI diagnostic algorithm meant to operate standalone. The performance data describes "SW verification/validation and the measurement accuracy test" for the software's functionality and reliability, which is a standalone evaluation of the software components but not in the context of diagnostic accuracy.

7. Type of Ground Truth Used

For the "SW verification/validation and the measurement accuracy test," the "ground truth" would likely be:

• Pre-defined expected outputs/behaviors for various software functions.
• Known measurements or anatomical landmarks in test images used for accuracy checks.
• Industry standards for DICOM compliance and image processing.

This is distinct from clinical ground truth such as pathology or outcomes data, which would be expected for a diagnostic AI system.

8. Sample Size for the Training Set

Not applicable. The Ez3D-i/E3 device is described as "3D viewing software for dental CT images" that provides diagnostic tools and image manipulation functions. It is not explicitly stated to be an AI/machine learning device that requires a "training set" in the context of supervised learning for a specific diagnostic task. The software's functionality is based on image processing algorithms and user interface design.

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

Not applicable, as no "training set" for AI/machine learning is described.

Ask a specific question about this device

Ez3D-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.

Ez3D-i is intended for use as software to load, view and save DICOM images from CT, panorama, cephalometric and intraoral imaging equipment and to provide 3D visualization, 2D analysis, in various MPR (Multi-Planar Reconstruction) functions.

Ez3D-i is 3D viewing software for prompt and accurate diagnosis dental CT images in DICOM format with a host of useful functions including MPR, 2-dimensional analysis and 3 dimensional image reformation. It provides advanced simulation functions such as Implant Simulation, Drawing Canal, and Implant Environ Bone Density, etc for the benefit of effective doctor and patient communication and precise treatment planning. Ez3D-i is a useful tool for an easier diagnosis and analysis by processing a 3D image with simple and convenient user interface. Ez3D-i's main functions are;

• · Image adaptation through various rendering methods such as Teeth/Bone/Soft tissue/MIP
• · Versatile 3D image viewing via MPR Rotating, Curve mode
• · "Sculpt" for deleting unnecessary parts to view only the region of interest.
• · Implant Simulation for efficient treatment planning and effective patient consultation
• · Canal Draw to trace alveolar canal and its geometrical orientation relative to teeth.
• · "Bone Density" test to measure bone density around the site of an implant(s)
• · Various utilities such as Measurement, Annotation, Gallery, and Report
• · 3D Volume function to transform the image into 3D Panorama and the Tab has been optimized for Implant Simulation.
• . Provides the Axial View of TMJ, the Condyle/Fossa images in 3D and the Section images, and supports functions to separate the Condyle/Fossa and display the bone density
• · STO/VTO Simulation to predict orthodontic treatment/ surgery results with 3D Photo image.
• · Segmentation function to get tooth segmentation data from CT, label each segmented tooth data as an object and utilize them in simulation such as tooth extraction, implant simulation, etc.

The provided text describes the Ez3D-i / E3 dental imaging software and its substantial equivalence to a predicate device (K173863). However, it does not contain a detailed study with specific acceptance criteria and performance metrics for the new device that would allow for a quantitative comparison in the format requested.

The document states that "Verification, validation and testing activities 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." However, it does not provide the specifics of these tests, the acceptance criteria, or the reported performance.

Therefore, I cannot populate the table or provide detailed answers to most of the questions based solely on the provided text.

Here's an assessment based on the information that is available:

Acceptance Criteria and Study Details for Ez3D-i / E3

The provided documentation does not include a specific table of acceptance criteria and reported device performance for the Ez3D-i / E3. It generally states that validation and verification activities were performed and the device passed pre-determined Pass/Fail criteria. The submission focuses on demonstrating substantial equivalence to a predicate device (Ez3D-i / E3, K173863) rather than presenting a de novo performance study with quantitative metrics against specific acceptance thresholds.

Missing Information:

• A table of specific acceptance criteria.
• Quantitative reported device performance against those criteria.
• Details about the study design that would prove the device meets these criteria.

Given the information provided, many sections below cannot be fully answered.

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

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

• Sample Size for Test Set: Not specified in the provided text.
• Data Provenance: Not specified in the provided text.

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 specified in the provided text. The document mentions that the software's results are dependent on the interpretation of "trained and licensed radiologists, clinicians and referring physicians," suggesting human expertise is involved in the clinical use, but it does not detail an expert ground truth process for a test set.

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

• Not specified in the provided text.

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 MRMC comparative effectiveness study is not mentioned in the provided text. The device is described as providing "diagnostic tools" and "advanced simulation functions" for use by trained medical professionals, but there's no study detailed to show improvement with or without AI assistance. The submission focuses on software functionality, not a comparative clinical trial.

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

• A standalone performance study for the algorithm is not explicitly described in the provided text in terms of quantitative metrics. The document emphasizes that the results are "dependent on the interpretation of trained and licensed radiologists, clinicians and referring physicians as an adjunctive to standard radiology practices for diagnosis." This suggests it's positioned as an adjunctive tool rather than a standalone diagnostic AI. The "verification, validation and testing activities" likely pertained to software functionality and safety rather than diagnostic accuracy as a standalone algorithm.

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

• Not specified in the provided text for any performance evaluation.

8. The sample size for the training set

• Not specified in the provided text. There is no mention of a "training set," which implies that the device, in this context, is not explicitly described as an AI/ML product that learns from data in the way typically discussed for training sets. It is a software tool with pre-programmed functions for visualization and analysis.

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

• Not applicable based on the lack of a specified "training set" in the provided text.

Ask a specific question about this device

Ez3D-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.

Ez3D-i is intended for use as software to load, view and save DICOM images from CT, panorama, cephalometric and intraoral imaging equipment and to provide 3D visualization, 2D analysis, in various MPR (Multi-Planar Reconstruction) functions.

Ez3D-i is 3D viewing software for prompt and accurate diagnosis dental CT images in DICOM format with a host of useful functions including MPR, 2-dimensional analysis and 3dimensional image reformation. It provides advanced simulation functions such as Implant Simulation, Drawing Canal, and Implant Environ Bone Density, etc for the benefit of effective doctor and patient communication and precise treatment planning. Ez3D-i is a useful tool for an easier diagnosis and analysis by processing a 3D image with simple and convenient user interface. Ez3D-i's main functions are;

• · Image adaptation through various rendering methods such as Teeth/Bone/Soft tissue/MIP
• · Versatile 3D image viewing via MPR Rotating, Curve mode
• · "Sculpt" for deleting unnecessary parts to view only the region of interest.
• · Implant Simulation for efficient treatment planning and effective patient consultation
• · Canal Draw to trace alveolar canal and its geometrical orientation relative to teeth.
• · "Bone Density" test to measure bone density around the site of an implant(s)
• · Various utilities such as Measurement, Annotation, Gallery, and Report
• · 3D Volume function to transform the image into 3D Panorama and the Tab has been optimized for Implant Simulation.

This document is a 510(k) premarket notification for the Ez3D-i / E3 dental imaging software. The purpose of this document is to demonstrate "substantial equivalence" to a predicate device, not necessarily to provide full clinical trial results as would be required for a PMA (Pre-Market Approval). Therefore, much of the requested information regarding detailed acceptance criteria, specific study designs, and performance metrics (like sensitivity, specificity, reader improvement, etc.) is not present in this document.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a formal table of quantitative acceptance criteria or specific performance metrics (like accuracy, sensitivity, specificity, etc.) for diagnostic performance. The “Performance Data” section broadly states:

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

This information is not provided in the document. The document refers to "system level validation tests" but does not specify the number of cases or the nature of the data (e.g., specific patient scans, simulated data). The provenance of any data (country, retrospective/prospective) is also not mentioned.

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

This information is not provided. The document states that the software's results "are dependent on the interpretation of trained and licensed radiologists, clinicians and referring physicians as an adjunctive to standard radiology practices for diagnosis." However, it does not detail how ground truth was established for the validation tests mentioned.

4. Adjudication Method for the Test Set

This information is not provided.

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

A MRMC comparative effectiveness study is not mentioned in the document. The document focuses on demonstrating substantial equivalence to a predicate device, which typically involves showing that the new device performs as intended and is safe, rather than proving improved human reader performance with AI assistance.

6. Standalone Performance Study

A standalone performance study (i.e., algorithm only without human-in-the-loop performance metrics like sensitivity and specificity) is not explicitly detailed in the document. The "Performance Data" section broadly states that "Verification, validation and testing activities were conducted to establish the performance, functionality and reliability characteristics of the modified devices" and that it "passed all of the tests." However, specific metrics for standalone diagnostic accuracy are not provided.

7. Type of Ground Truth Used

The type of ground truth used for validation (e.g., expert consensus, pathology, outcomes data) is not specified in the document.

8. Sample Size for the Training Set

The document is about a modified device and its validation. It does not mention a training set sample size, as the submission is not focused on the de novo development of a machine learning model from scratch but rather on a software update/modification.

9. How Ground Truth for the Training Set Was Established

Since no training set is mentioned or detailed, the method for establishing its ground truth is not provided.

Summary of what is available and what is not:

• Available: General statement that the device passed pre-determined Pass/Fail criteria in validation tests.
• Not Available:
  • Specific quantitative acceptance criteria (e.g., sensitivity, specificity thresholds).
  • Detailed performance metrics against those criteria.
  • Sample size and provenance of test data.
  • Details on ground truth establishment (number/qualifications of experts, adjudication method, type of ground truth).
  • Information on MRMC comparative effectiveness studies or standalone diagnostic performance metrics (e.g., AUC, sensitivity, specificity).
  • Information on training set size or ground truth establishment for a training set.

This level of detail is typical for a 510(k) submission for certain types of software modifications, where the focus is on maintaining substantial equivalence to an existing (predicate) device, rather than proving novel clinical efficacy or superior diagnostic performance with detailed clinical studies. The "performance data" referred to likely pertains to software verification and validation, ensuring functions work as designed and that new features (like 3D Panorama View, Navigator, and Collision Detection) do not introduce new safety or effectiveness issues.

Ask a specific question about this device