(51 days)
VitreaView is a medical image viewing and information distribution application that provides access, through the internet and within the enterprise, to multi-modality softcopy medical images, reports and other patient-related information, that may be hosted within disparate archives and repositories for review, communication and reporting of DICOM and non-DICOM data. VitreaView is not intended for primary diagnosis. When accessed from a mobile tablet, VitreaView is for informational purposes only and not intended for diagnostic use.
Display monitors used for reading medical images for diagnostic purposes must comply with applicable regulatory approvals and with quality control requirements for their use and maintenance.
Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5 MP resolution and meets other technical specifications reviewed and accepted by FDA.
VitreaView is a cross-browser, cross-platform, zero-footprint universal image viewer solution capable of displaying both DICOM and non-DICOM medical images. VitreaView enables clinicians and other medical professionals to access patients' medical images with integrations into a variety of medical record systems, such as Electronic Health Record (EHR), Electronic Medical Record (EMR), Health Information Exchange (HIE), Personal Health Record (PHR), and image exchange systems. It supports the physician in medical image viewing and treatment planning.
VitreaView offers medical professionals a universal viewer for accessing imaging data in context with reports from enterprise patient health information databases, fosters collaboration, and provides workflows and interfaces appropriate for referring physicians and clinicians. IT departments will not have to incur time to install client systems, due to the zero footprint, zero- download nature of VitreaView. VitreaView offers scalability to add new users as demand grows, may be deployed in a virtualized environment, and is designed to be integrated with enterprise patient health information databases. When accessed from a mobile tablet, no advanced image processing is performed by the tablet.
Some of the general features include:
- . Performance speed
- . Zero-footprint architecture
- DICOM and non-DICOM display
- Vendor neutrality .
- Function within a virtual environment .
- Multi-modality review of data .
- . Basic image review tools (zoom, pan, measure)
- Easy study navigation and search capability .
- Radiology key images .
- Comparative review .
- . Cross-platform viewing capabilities (Windows, Linux, Mac OS)
- Leveraging of next-generation protocols for image viewing (i.e. MINT) .
- Single sign-on .
- . MPR and 3D viewing
- . Access on various iOS and Android tablet devices through the default internet browser
This device is a Picture Archiving and Communications System called VitreaView. It is intended for viewing medical images and information, not for primary diagnosis.
Here is an analysis of the provided text:
1. Table of acceptance criteria and the reported device performance:
The provided text for VitreaView is a 510(k) summary for an addition to its Indications for Use. It describes the device, its intended use, and summaries of non-clinical and clinical tests. However, it does not contain explicit acceptance criteria or a quantifiable table of device performance metrics such as sensitivity, specificity, accuracy, or any statistical measures that would typically be seen in a study evaluating an AI device's diagnostic performance.
The document primarily focuses on verifying the software's functionality, usability, and compliance with standards. The "performance" mentioned in the document refers to aspects like "Performance speed" and a general statement that "The software verification team had a primary goal of assuring that the software fully satisfies all expected new and previously defined detailed level system requirements and features."
Therefore, a table with specific acceptance criteria and reported numeric performance cannot be generated from the given text.
2. Sample size used for the test set and the data provenance:
- Test Set Sample Size: The document does not specify a distinct "test set" and its sample size in the context of an accuracy or performance study. The verification and validation activities involved "previously acquired medical images" and "workflow testing," but no specific number of cases for a test set is provided.
- Data Provenance: The document states "Testing included verification, validation, and evaluation of previously acquired medical images." It does not provide information on the country of origin of this data or if it was retrospective or prospective.
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 document does not describe the establishment of ground truth by experts for a specific test set. The testing focused on software functionality and compliance rather than diagnostic accuracy against expert consensus.
4. Adjudication method for the test set:
Not applicable, as no described study involved adjudication for establishing ground truth on a test set for diagnostic performance.
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, a multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned. The device's intended use is for viewing and information distribution, explicitly stating "VitreaView is not intended for primary diagnosis." Therefore, a study comparing human reader performance with and without AI assistance for diagnostic tasks would not be relevant to this submission.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
The device is a medical image viewer. While its performance elements (like measurement accuracy and spatial accuracy) were verified, there isn't a "standalone" performance study in the context of an AI algorithm making diagnostic decisions. The device itself is a tool for humans to view images.
7. The type of ground truth used:
For the measurement accuracy, the ground truth was established using imaging phantoms with "known positions, distances, and angles." For general software verification and validation, the ground truth was essentially the "system requirements and features" and "user needs and intended uses." There is no mention of ground truth established by expert consensus, pathology, or outcomes data for diagnostic purposes, as the device is not intended for primary diagnosis.
8. The sample size for the training set:
Not applicable. This device is a medical image viewing and information distribution application, not an AI algorithm that undergoes machine learning training on a "training set" to make diagnostic predictions. The documentation describes software development, verification, and validation, not machine learning model training.
9. How the ground truth for the training set was established:
Not applicable, as there is no mention of a training set or machine learning in the context of this device's 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).