(87 days)
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 and save 2D image files, load DICOM project files from panorama, cephalometric, and intra-oral imaging equipment.
EzDent-i is a dental imaging software solution that stores, analyzes and diagnoses patient images that have been acquired through VATECH dental equipment.
EzDent-i is equipped with everything you need for digital panoramic and cephalometric image storage, processing and viewing. EzDent-i functions as a central storage point for digital images and associated patient data. Images can be acquired directly from equipment that EzDent-i currently supports. In addition, images can be imported from other digital sources.
The Main Functions of EzDent-i
With EzDent-i you can perform the following operations assuming that all the other equipment is ready to use.
- Create and store new patient information in a database.
- Capture and store digital X-ray images with exposure values from the device.
- Capture and store intraoral photographs.
- Export and import digital images.
- Process images to enhance their diagnostic value with dental-specific tools.
- Analyze the image with application-specific measurement tools.
- Build an environment with multiple workstations using a database shared over a network.
- Printing images and image related information.
EzDent-i can be used in a network environment. If EzDent-i is installed in several computers, the patient and image database can be shared among them and used from different workstations.
This document describes EzDent-i, a dental imaging software. The submission focuses on demonstrating substantial equivalence to a predicate device rather than a comprehensive study proving the device meets specific acceptance criteria based on quantifiable performance metrics.
Here's an analysis of the provided text in relation to your request:
1. Table of acceptance criteria and the reported device performance:
The document does not provide a table with specific, quantifiable acceptance criteria (e.g., sensitivity, specificity, accuracy) and corresponding reported device performance metrics. Instead, it asserts substantial equivalence based on a comparison of features and intended use with a predicate device.
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 mention any specific test set size, data provenance, or the nature of any "test data" used for performance evaluation that would typically be associated with a clinical or non-clinical study for performance metrics.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not provided. The submission focuses purely on software validation against internal criteria and comparison with a predicate device's features.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
No adjudication method is mentioned as there is no described test set involving human interpretation for performance evaluation.
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 was performed or described. The device is a dental imaging software; there is no mention of "AI assistance" in the context of improving human reader performance.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
No standalone performance study of an algorithm is mentioned. The device is software for viewing, processing, and storing images, not an AI diagnostic algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
No ground truth is described in the context of performance evaluation because no study to measure diagnostic performance is detailed. The "ground truth" for the software's functionality would have been its internal design specifications.
8. The sample size for the training set:
Not applicable. The document describes software that facilitates image viewing and processing, not an AI or machine learning model that requires a training set.
9. How the ground truth for the training set was established:
Not applicable, as there is no training set for an AI/ML model described.
Summary of the "Study" and "Acceptance Criteria" presented in the document:
The document describes nonclinical testing as the basis for proving the device meets criteria.
- Acceptance Criteria Mentioned: "in-house testing criteria" and "predetermined acceptance criteria" (within the Software Validation Report).
- Reported Device Performance: The only reported performance is that the device "passed all in-house testing criteria" and "the risk analysis and individual performance results were within the predetermined acceptance criteria." The validation testing "verified and validated the risk analysis and individual performance results were within the predetermined acceptance criteria."
- Study Description: The "study" was a Software Validation Test, which was "designed to evaluate all input functions, output functions, and actions performed by EzDent-i. Each operational mode and the process followed are documented in the Software Validation Report."
- Safety and Performance Data: The submission cites compliance with IEC 62304 (Medical device software - Software life-cycle processes) and ISO 14971 (Application of risk management to medical devices).
Conclusion from the document:
The conclusion states that the premarket notification contains adequate information and data to determine substantial equivalence to the predicate device in terms of technical characteristics, general function, application, and intended use. It explicitly states, "The new device does not introduce a fundamentally new scientific technology and the nonclinical tests demonstrate that the device is safe and effective."
In essence, this 510(k) submission relies on a comparison to a predicate device and internal software validation against pre-defined functional and safety criteria, rather than a clinical study demonstrating diagnostic performance against specific acceptance metrics.
§ 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).