(262 days)
MediOMx is a software-based viewer intended to be used with off-the-shelf hardware for the display of DICOM data to aid in diagnosis for healthcare professionals. It performs operations relating to the transfer, storage, and display of image data. MediOMx allows users to perform image manipulations, including window/level, pan, zoom, and rotation. MediOMx provides 2D display, Multi-planar Reformatting of medical image data. MediOMx is not intended for primary mammography interpretation.
Mobile usage is for reference and referral only.
MediOMx is a software-based medical image viewer used with web browsers for the 2D visualization of DICOM and non-DICOM medical images. MediOMx is intended for storage, display, manipulation, and processing of radiological data, including images, reports and other clinical information. It has the following primary features and functions:
- Zero-footprint HTML5 medical image upload, transfer and display of medical images between facilities
- Easy access to images for all participants in the healthcare process, including radiologists, technologists, physicians, nurses and other patient care practitioners
- Serve as information and data management system for for DICOM and non-DICOM medical images
- Tools for image manipulation (e.g.pan, zoom, rotate, window level)
- Metadata information and orientation labels display
- Advanced image manipulation functions like view synchronization across series, 3D visualization like MIP and MPR
- Encrypted transmission of medical images through secured networks
- Encrypted storage of medical images
- HIPAA-compliant data management, including centralized storage of user activity via audit trails.
- Management of users, roles, and permissions
- It supports optional integration with FDA-cleared 3rd party AI models. It sends the input study to the 3rd party AI model, receives the AI output and displays outputs of the 3rd party AI model "as-is" in MediOMx for visualization by the Radiologist to assist in diagnosing the study. Outputs are displayed in accordance with the 3rd party provider's regulatory clearance. The original image is always accessible
MediOMx consists of a cloud-based application that displays and processes digital medical images, and associated medical information to aid in the day-to-day operations and workflow of clinicians and healthcare practitioners. The web browser based medical image viewer serves as the frontend module which users interact with in viewing data. The backend module handles the connection and processing of data from a variety of sources within the health system, in view of preparing visualizations to be rendered by the viewer. MediOMx can connect and access the medical images across different sources in a health system: an existing PACS or VNA, cloud storage or local server-based storage. Users can also upload images securely into MediOMx which can be shared and enables collaboration with other users. The data connection and imaging data processing is handled by the MediOMx backend module which supports the standardized transmission protocol as defined in the DICOM standard. Users interact with MediOMx through a standard web browser, thus providing access to full quality images from anywhere and supporting a greater efficiency for care. MediOMx utilizes authorization and authentication mechanisms that enforces authorized users to access the imaging data. The system extends beyond the hospital and its internal network. With a proper network, MediOMx can be accessed by clinical users outside of the hospital network. This way referring physicians can easily call up the imaging data of their patients or external expert accessing the imaging data for additional opinion. MediOMx provides end-users with the ability for industry standard features such as Window/ Level, Image Flip and Rotate. Images are initially displayed in the 2D view mode, but with the ability to toggle into advanced viewing mode of MPR for relevant exam types. It supports processing and displaying Multiplanar Reconstruction (MPR). MediOMx provides an image rendering mechanism that preloads lower resolution images during image scrolling to improve interactivity and performance for users operang in lower network bandwidth while the full quality image is loaded in the background. The use of a secure data transmission protocol and data encryption ensure high data security for data management via the internet. MediOMx tracks user activity via audit trails and stores the audit data on the centralized server.
This document does not contain the detailed information necessary to answer all parts of your request. It's a 510(k) summary for a medical image viewer called MediOMx, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study report for specific acceptance criteria and performance metrics.
Here's what can be extracted based on the provided text, and where information is missing:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state acceptance criteria or provide a table of reported device performance in the way a clinical study report would. It generally states that "Safety and performance of the PMX MediOMx device has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification, validation, standalone and clinical performance testing." However, specific numerical performance metrics (e.g., accuracy, sensitivity, specificity, or response times for image display) and their corresponding acceptance criteria are not presented.
The "Substantial Equivalence" section (page 5-7) compares features and functions of the proposed device (MediOMx) with the predicate device (MD.ai Viewer (K223425)). This comparison implicitly serves as a form of "performance" assessment in the context of a 510(k) submission, confirming that MediOMx largely matches the predicate's capabilities.
| Feature/Function | Proposed Device (MediOMx) Performance | Predicate Device (MD.ai Viewer) Performance | Substantially Equivalent |
|---|---|---|---|
| User Install Requirements | Thin Client - no install, runs within browser | Thin Client - no install, runs within browser | Yes |
| Communication | DICOM, non-DICOM | DICOM, non-DICOM | Yes |
| Modalities | CT, MR, X-ray, US | CR, CT, DX, IVOCT, MR, MG, NM, OCT, OT, PT, RF, SC, US, XA | Yes |
| Window Level, Rotate/Pan/Zoom, Reset, Presets, Invert | Yes | Yes | Yes |
| Multi-study viewing, Image Export, Image Sharing compliant | Yes | Yes | Yes |
| Metadata Display/Hide | Yes | Yes | Yes |
| Orientation Labels | Yes | Yes | Yes |
| Keyboard Shortcuts | No | Yes | Yes |
| Measurements, Annotations | No | Yes | Yes |
| Full Screen Mode | Yes | Yes | Yes |
| Multi-monitor, Layouts | No | Yes | Yes |
| Linking Series | No | Yes | Yes |
| Image Scrolling, Linked Scrolling, Reference Lines | Yes | Yes | Yes |
| GSPS, KIN | No | No | Yes |
| Multiplanar Reformat (MPR) | Yes | Yes | Yes |
| Maximum Intensity Projection (MIP) | Yes | Yes | Yes |
| Oblique, Volume Rendering, Opacity Presets, Scalpel tool, bone removal | No | No | Yes |
| Sharpen, blur, emboss, edge filters | No | Yes | Yes |
| Histogram Equalization filter | No | Yes | Yes |
2. Sample size used for the test set and the data provenance
The document mentions "software verification, validation, standalone and clinical performance testing" but does not specify the sample size of any test sets (e.g., number of images, number of cases) or the data provenance (e.g., country of origin, retrospective/prospective nature).
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 in the document.
4. Adjudication method for the test set
This information is not provided in the document.
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
This device is described as a "software-based viewer" that "supports optional integration with FDA-cleared 3rd party AI models." It "receives the AI output and displays outputs of the 3rd party AI model 'as-is' in MediOMx for visualization by the Radiologist to assist in diagnosing the study." It does not appear to be an AI-powered diagnostic algorithm itself, nor does the document describe an MRMC study to measure the effect size of human readers improving with AI assistance from this specific device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document states "standalone and clinical performance testing" was performed, but it does not elaborate on what "standalone testing" specifically entailed in terms of algorithmic performance metrics or without human-in-the-loop performance. Given its role as a viewer, it's more likely standalone testing would relate to its functionality and display accuracy rather than a diagnostic algorithm's performance.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
This information is not provided in the document.
8. The sample size for the training set
This device is an image viewer, not an AI algorithm that would typically have a "training set" in the machine learning sense. While software is developed and tested, the concept of a training set as used for AI models is not applicable here.
9. How the ground truth for the training set was established
As above, this is not applicable for an image viewer.
§ 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).