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Ambra PACS software is intended for use as a primary diagnostic and analysis tool for diagnostic images for hospitals, imaging centers, radiologists, reading practices and any user who requires and is granted access to Patient image, demographic and report information.

Ambra Pro Viewer, a component of Ambra PACS, displays, modifies and manages diagnostic quality DICOM images including 3D visualization and reordering functionality.

Lossy compressed mammographic images and digitized film screen mages must not be reviewed for primary diagnosis or image interpretations. Mammographic images may only be viewed using cleared monitors intended for mammography Display.

Not intended for diagnostic use on mobile devices.

Ambra PACS software is intended for use as a primary diagnostic and analysis tool for diagnostic images and reporting for hospitals, imaging centers, radiologists, reading practices and any user who requires and is granted access to patient image, demographic and supplementary information.

Ambra PACS is considered a 'Continuous Use' device is compliant with HIPAA and 21 CFR Part 11 regulations regarding patient privacy (such as restricting access to particular studies, logging access to data), data integrity, patient safety and best software development and validation practices.

Ambra PACS provides common diagnostic and analytic radiology functionality. Specifically, Ambra PACS enables:

• Real-time viewing and management of DICOM images for diagnostic, clinical, research and education purposes;
• Ingestion and normalization of DICOM content for review and archiving;
• Electronic distribution and secure storage of images;
• Off-site viewing and reporting (distance education, tele-diagnosis).

Ambra ProViewer, a component of Ambra PACS, displays, modifies, and manages diagnostic quality DICOM images, including 3D visualization and reordering functionality. Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary diagnosis or image interpretations. Mammographic images may only be viewed using cleared monitors intended for mammography display.

The provided text does not contain detailed acceptance criteria or a study that proves the device meets specific performance metrics. Instead, it focuses on demonstrating substantial equivalence to a predicate device (Ambra PACS with ProViewer, K202335) through software verification and validation testing, especially concerning a change in the programming language for the transcoding component.

Therefore, many of the requested details, such as specific performance metrics, sample sizes for test sets, expert qualifications, and details of clinical studies, are not available in this document.

However, based on the information provided, here's what can be inferred and what is not available:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states: "All verification and validation acceptance criteria were met." However, it does not provide the specific numerical acceptance criteria or the reported device performance. It generally refers to functional, design, measurement, and deployment requirements.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not specified. The document mentions "validation testing of DICOM images" and "simulated use testing," but no numbers for the test set size or number of images are provided.
• Data Provenance: Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable as no clinical study or ground truth establishment by experts is mentioned for this substantial equivalence submission. The testing was verification and validation against software requirements.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not applicable as no expert adjudication or clinical study is mentioned.

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 comparative effectiveness study was done or mentioned. This submission is for a PACS system, not an AI-assisted diagnostic tool for human readers.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

• Not applicable in the context of specific performance metrics for AI algorithms. The testing was for the overall PACS software functionality.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Not explicitly stated for a test set. The validation focused on verifying that the software continues to meet defined requirements, rather than establishing diagnostic accuracy against a clinical ground truth.

8. The sample size for the training set:

• Not applicable, as this is a PACS system and the document describes verification and validation rather than the training of a machine learning model.

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 model in this context.

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MD.ai Viewer is a software-based viewer intended to be used with off-the-shelf hardware for the display of DICOM and non-DICOM medical images and other healthcare data to aid in diagnosis for healthcare professionals. It performs operations relating to the transfer, storage, display, and measurement of image data.

MD.ai Viewer allows users to perform image manipulations, including window/level, rotation, measurement and markup. MD.ai Viewer provides 2D display, Multi-Planar Reformatting and 3D visualization of medical image data.

Mobile usage is for reference and referral only.

MD.ai Viewer is not intended for primary mammography interpretation.

MD.ai Viewer is a software-based medical image viewer used with off-the-shelf workstation and web browsers for the 2D & 3D visualization of DICOM and non-DICOM medical images. MD.ai Viewer is intended for storage, display, manipulation, measurement 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, annotation and measurement.
• . Metadata information and orientation labels display
• . Advanced image manipulation functions like view synchronization across series, 3D visualization like MIP and MPR
• . Advanced image processing filters like histogram equalization (CLAHE) filter to aid in visualization of pathological features in the images
• . Encrypted transmission of medical images through secured networks
• . Encrypted storage of medical images
• . HIPAA-compliant data management, including centralized storage of user activities via audit trails.
• . Management of users, roles, and permissions

MD.ai Viewer consists of configurable software-only modules that display and process 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 the imaging 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.

MD.ai Viewer 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 MD.ai Viewer which can be shared and enables collaboration with other users. The data connection and imaging data processing is handled by MD.ai Viewer backend module which supports the standardized transmission protocol as defined in the DICOM standard. In situation where secure network link is not available between health system and MD.ai cloud instance, the MD.ai Viewer proxy server can provide a secure and encrypted transfer of imaging data.

Users interact with MD.ai Viewer through a standard web browser, thus providing access to full quality images from anywhere and supporting a greater efficiency for care. MD.ai Viewer 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 proper authorization, MD.ai Viewer 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.

MD.ai Viewer provides end-users with the ability for industry standard features such as Window/ Level, Image Flip and Rotate, Invert, Hanging Protocol, Image Measurements, and Keyboard/Mouse shortcuts. Images are initially displayed in the 2D view mode, but with the ability to toggle into advanced viewing mode of 3D/MPR for relevant exam type. It supports processing and displaying Multiplanar Reconstruction (MPR) and different intensity rendering modes based on user-defined slab thickness. It also provides image processing filters like histogram equalization (CLAHE) filter to better visualize pathological features when displaying low contrast images from some modality devices.

MD.ai Viewer provides an image rendering mechanism that preloads lower resolution images during image scrolling to improve interactivity and performance for users operating 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. MD.ai Viewer 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 complete all parts of your requestregarding acceptance criteria and a specific study proving the device meets them. The provided text is a 510(k) summary for the MD.ai Viewer, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed performance study with acceptance criteria.

Here's what can be extracted and what information is missing:

1. A table of acceptance criteria and the reported device performance

The document does not provide a formal table of acceptance criteria with corresponding performance statistics. Instead, it describes general functionalities and features, indicating that "the design requirements were successfully met" and "Intended use and user needs were successfully validated." It also states that "The measurement features of MD.ai Viewer were validated using Digital Reference Objects and comparison with the reference device - K202335." However, specific numerical acceptance criteria (e.g., accuracy within X% for measurements) and the measured performance are not detailed.

Here's a table based on the functional comparisons and statements, but it lacks specific quantitative acceptance criteria and detailed reported performance metrics:

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the document. The filing mentions "non-clinical performance testing" and "Digital Reference Objects" for measurement validation, but no details on sample size, data origin, or whether the study 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 in the document. No clinical study involving expert interpretation for ground truth is described.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not provided in the document, as no expert-adjudicated test set is described.

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

A MRMC comparative effectiveness study was not done. The document explicitly states: "No clinical performance data were performed for this submission." The MD.ai Viewer is a viewer, not an AI diagnostic aid in the context of this 510(k), thus an MRMC study comparing human readers with and without AI assistance would not be directly applicable to its stated indications for use as a viewer.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

A standalone performance study for an AI algorithm was not done. The MD.ai Viewer is a display and processing system for medical images, not an AI algorithm intended for standalone diagnostic performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

For the "measurement features" validation, the ground truth was established using Digital Reference Objects. The nature of the ground truth for other "design requirements" is not specified but would generally relate to technical specifications and functional correctness rather than clinical ground truth like pathology.

8. The sample size for the training set

This is not applicable as the MD.ai Viewer, as described in this 510(k), is a medical image viewer and processing system, not an AI model that requires a training set. If it incorporates AI filters, the training of those specific filters is not detailed here.

9. How the ground truth for the training set was established

This is not applicable for the same reason as point 8.

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