(23 days)
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
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified in the provided text, but implied as "pre-determined Pass/Fail criteria" for verification, validation, and testing activities. | Not specified in the provided text beyond "The device passed all of the tests." |
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.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).