(147 days)
XVWeb is a Picture Archiving and Communications System (PACS) that enables dental facilities to query and access digitally stored hard and soft tissue intraoral radiological images using an internet/web browser.
A web-based interface provides users the needed functionality to display patient images and studies in commercially available web browsers. Patient images/studies can be accessed by users locally within the system or across a wide-area network at distributed locations.
Acquisition can be included via integration with a DICOM-compatible Imaging and/or PACS system configured to forward images to the XVWeb database. XVWeb is compatible with programs that run on standard "off-the-shelf personal computers, business computers, and servers running standard operating systems.
The system allows users to: manipulate (e.g. rotate, flip, etc.); enhance (e.g. increase or decrease brightness/contrast, gamma correction); add labels (e.g. measurements, lines, arrows, etc.), annotations to patient images/studies and other relevant operations for diagnostic purposes.
XVWeb is designed for medium to large dental practices and is intended for trained dental professionals and technicians to access, manipulate, and enhance dental images for diagnostic purposes only.
XVWeb is a web-based dental picture archiving and communications system that provides a dental facility the ability to access their patient radiological image library over the internet. It is designed for deployment in medium to large dental facilities with multiple viewing stations and/or across remote sites using standard TCP/IP network infrastructures. The system uses a web-based interface that allows users to display patient images and studies in commercially available web browsers such as Internet Explorer, Mozilla, Firefox, or Google Chrome.
XVWeb includes features for dental practices to: manipulate, enhance, add lahels, annotations to patient images/studies, and other relevant operations for diagnostic purposes. XVWeb includes image acquisition capabilities via integration with DICOM-compatible imaging systems that facilitate the production of original images. All images available for viewing in XVWeb have been received from DICOM compliant modalities or systems.
Here's an analysis of the acceptance criteria and study information for the XVWeb device, based on the provided text:
Preamble: It's important to note that the provided text is a 510(k) Premarket Notification summary for a Picture Archiving and Communications System (PACS) named XVWeb. The primary goal of a 510(k) is to demonstrate substantial equivalence to a predicate device, rather than to prove safety and effectiveness through extensive clinical trials. Therefore, the "acceptance criteria" and "study" described herein are primarily focused on demonstrating that XVWeb performs comparably to its predicates and meets general safety and performance standards for a medical device of its type, rather than an AI-specific performance study. The document does not describe an AI system, but rather a PACS.
Acceptance Criteria and Reported Device Performance
Given that XVWeb is a PACS and not an AI-driven diagnostic device, the "acceptance criteria" are related to its functional equivalence, technical characteristics, and safety compared to predicate PACS systems. The reported device performance is inherently its functionality mirroring or improving upon its predicates without raising new safety or effectiveness concerns.
Table 1: Acceptance Criteria and Reported Device Performance for XVWeb
| Acceptance Criteria Category | Specific Criteria (Implied from 510(k)) | Reported Device Performance (as described in 510(k) and comparison to predicates) |
|---|---|---|
| Intended Use | Query and access digitally stored intraoral/extraoral radiological images using an internet/web browser for dental facilities. | Substantially equivalent to predicate devices (Curve Image 2.0 and Centricity PACS Web Diagnostic); enables identical core functionality. |
| Environment of Use | Dental Offices | Operates in Dental Offices, same as predicates. |
| Basic Technology | Web-based software application | Web-based software application, same as predicates. |
| Core Features | - Query and view patient images/studies |
- View various image types
- Manipulate images (rotate, flip, zoom)
- Enhance images (brightness, contrast, gamma)
- Add labels/annotations/measurements
- DICOM compatibility
- Secure data transmission (HTTPS) | XVWeb provides all core features present in the predicate devices and several expanded features, without altering intended use or raising new safety/effectiveness questions. |
| Safety | No new types of safety questions raised compared to predicates. | Non-Clinical Testing performed, including Device Hazard Analysis. No new safety questions identified. |
| Effectiveness | No new types of effectiveness questions raised compared to predicates. | Non-Clinical Testing performed, including Module Testing, System Verification and Validation Testing. Effectiveness is considered equivalent to predicates. |
| Non-Clinical Testing | Pass stress testing in a simulated environment; successful module, system verification, and validation testing. | Performed and passed: Device Hazard Analysis, Module Testing, System Verification and Validation Testing. |
Study Details
The provided document describes a 510(k) Premarket Notification for a Picture Archiving and Communications System (PACS), not an AI-driven device. Therefore, the "study" is a demonstration of substantial equivalence to predicate devices through a comparison of intended use, technological characteristics, and performance, rather than a clinical study evaluating an AI algorithm's diagnostic performance.
1. Sample Size Used for the Test Set and Data Provenance:
* Test Set Sample Size: Not explicitly applicable in the context of an AI-driven device performance study. The "test set" here refers to the system itself being evaluated against a set of functional and performance requirements and compared to predicate devices. No specific "test set" of patient images or clinical cases with ground truth is mentioned for quantitative performance metrics.
* Data Provenance: Not applicable as there is no specific dataset of patient images being "tested" in the manner of an AI algorithm evaluation. The system handles "patient radiological image library over the internet," implying it would handle data from various dental imaging systems.
2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
* Not applicable. There is no mention of experts establishing a "ground truth" for a specific test set of images for diagnostic accuracy, as this is a PACS system and not a diagnostic AI algorithm. The ground truth for functional equivalence relies on regulatory standards and the established performance of the predicate PACS devices.
3. Adjudication Method for the Test Set:
* Not applicable as there is no test set of images requiring expert adjudication for ground truth.
4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:
* No. The document explicitly states "Clinical trials have been performed side-by-side with current systems to ensure proper performance of the software" (Section IX). However, this is presented in the context of performance assurance for a PACS, not a comparative effectiveness study with human readers for diagnostic accuracy improved by an AI. There is no mention of human readers or an AI component to improve diagnosis.
5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:
* Not applicable. XVWeb is a PACS, an infrastructure for viewing and managing images, not a standalone diagnostic algorithm.
6. The Type of Ground Truth Used:
* The "ground truth" for this 510(k) is the established functionality, safety, and effectiveness of the predicate PACS devices (Curve Image 2.0 and Centricity PACS Web Diagnostic). The claim is substantial equivalence to these devices, meaning XVWeb performs its intended functions similarly and safely. The "ground truth" is therefore regulatory precedent and comparative functional analysis, not specific pathology, expert consensus on images, or outcomes data.
7. The Sample Size for the Training Set:
* Not applicable. As a PACS, XVWeb is a software system for image management and viewing; it is not described as utilizing machine learning or AI that would require a "training set" of data.
8. How the Ground Truth for the Training Set Was Established:
* Not applicable, as there is no training set for a machine learning model described.
§ 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).