(59 days)
EzDent Web is a dental imaging software that is intended to provide viewer and image processing tools for maxillofacial radiographic images. 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 Web is intended for use as software to acquire, view, and save 2D and 3D image files, to load DICOM project files from panorama, cephalometric, and intra-oral imaging equipment, and to provide 3D visualization and 2D analysis.
EzDent Web v1.2 is a dental imaging software that enables you to save, manage, view and process patients' images. EzDent Web is equipped with management and processing system for various 2D and 3D images. In addition, EzDent Web provides media contents for patient consultation and user friendly instruction to assist your use of the software.
EzDent Web provides you with the following functions using patient images in 2D and 3D.
- . Manage patient information
- View patient images in 2D/3D using tools for image processing and view function.
- . Use high resolution 3D volume rendering to view 3D images in the optimized view for user intent.
- . Consult patients using media contents provided for patient consultation.
EzDent Web can be used in a networked environment. If EzDent Web is installed in several computers, the patient and image database can be shared among them and used on different workstations.
The software level of concern is Moderate.
The provided FDA 510(k) summary for EzDent Web (K230468) indicates that the device has the same indications for use and technical characteristics as its predicate device (K211700). The submission focuses on demonstrating substantial equivalence for software modifications and does not contain information about a study proving the device meets specific acceptance criteria related to AI/ML performance, diagnostic accuracy, or human interpretability studies (e.g., MRMC).
The changes described are primarily feature upgrades for user convenience and system requirements, not changes to core diagnostic capabilities or the introduction of AI/ML algorithms that would necessitate the types of performance studies you are asking about (e.g., sensitivity, specificity, human reader improvement with AI assistance).
Therefore, based on the provided text, I cannot fill out the requested table or answer most of your detailed questions about acceptance criteria and study proving device performance in the context of AI/ML or comparative diagnostic accuracy.
Here's a breakdown of what can be inferred from the provided text, and what cannot:
Information that CANNOT be Extracted from the Provided Text:
- A table of acceptance criteria and reported device performance (in terms of diagnostic accuracy/AI performance): The document does not define such criteria or report on diagnostic accuracy metrics (e.g., sensitivity, specificity, AUC) for the device's performance. The "performance data" section briefly mentions "SW verification/validation and the measurement accuracy test," but does not provide details of what was measured, the criteria, or the results beyond "passed all of the tests based on pre-determined Pass/Fail criteria." This refers to software functionality and measurement accuracy of existing tools, not AI/ML performance.
- Sample size used for the test set and data provenance: No information on a test set for diagnostic performance.
- Number of experts used to establish ground truth & qualifications: Not applicable for this type of submission focused on substantial equivalence of software features.
- Adjudication method for the test set: Not applicable.
- Multi-Reader Multi-Case (MRMC) comparative effectiveness study, including effect size: Not applicable. The device is described as "viewer and image processing tools," not an AI-powered diagnostic assist tool that would typically undergo MRMC studies.
- Standalone (algorithm only) performance: Not applicable as it's not an AI algorithm.
- Type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable.
- Sample size for the training set: Not applicable as it's not an AI model.
- How the ground truth for the training set was established: Not applicable.
Information that CAN be Inferred or Directly Stated from the Provided Text (regarding software changes, not diagnostic performance):
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Acceptance Criteria & Reported Performance:
- Acceptance Criteria (Implied for Software Functionality): The device "passed all of the tests based on pre-determined Pass/Fail criteria." These tests relate to "SW verification/validation and the measurement accuracy test" for the modified features.
- Reported Device Performance: The modifications are described as "additional features for user convenience" and "do not affect the device safety or effectiveness." This implies that the software performed as intended for these new convenience features and that existing functionalities (e.g., image viewing, manipulation, measurement) maintained their accuracy. No quantitative performance metrics are provided for the "measurement accuracy test" beyond a pass/fail.
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Sample Size and Data Provenance:
- Not applicable for diagnostic performance studies. The submission is about software functionality updates.
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Experts and Ground Truth Establishment for Test Set:
- Not applicable as this is a software update for an existing image management system, not a diagnostic AI system requiring expert-adjudicated ground truth. The device is
"meant to be used by trained medical professionals such as radiologist and dentist."
- Not applicable as this is a software update for an existing image management system, not a diagnostic AI system requiring expert-adjudicated ground truth. The device is
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Adjudication Method for Test Set:
- Not applicable.
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MRMC Comparative Effectiveness Study:
- No MRMC study was done. The document does not describe the device as providing AI assistance to human readers for diagnostic interpretation. Its function is "viewer and image processing tools."
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Standalone (Algorithm Only) Performance:
- No standalone algorithm performance was done. This device is an image management and processing system, not a standalone diagnostic algorithm.
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Type of Ground Truth Used:
- Not applicable for this type of software modification. The existing "measurement accuracy test" for tools like linear distance, angle, etc., would rely on known measurements in test images, but details are not provided.
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Sample Size for Training Set:
- Not applicable. This is not an AI/ML device that requires a training set.
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How Ground Truth for Training Set was Established:
- Not applicable.
Summary of the Study (Based on Provided Text):
The study described in the 510(k) summary for EzDent Web (K230468) was a software verification and validation effort. Its purpose was to demonstrate that the updated version of EzDent Web (v1.2) is substantially equivalent to its predicate device (EzDent Web v1.0, K211700).
The "study" assessed the performance, functionality, and reliability of the modified device through "SW verification/validation and the measurement accuracy test." The primary conclusion was that the device "passed all of the tests based on pre-determined Pass/Fail criteria." The changes were identified as "additional features for user convenience" and enhancements to "PC System Requirement Information," "EzDent Web Settings," "PATIENT Page," and "VIEWER Page," rather than fundamental changes impacting diagnostic accuracy or introducing new AI capabilities. No clinical study involving human readers or diagnostic performance metrics was conducted or required for this type of submission.
§ 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).