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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. This data is hosted within disparate archives and repositories for diagnosis, review, communication, and reporting of DICOM and non-DICOM data.

Display monitors used for reading medical images for diagnostic purposes must comply with the applicable regulatory approvals and quality control requirements for their use and maintenance.

Lossy compressed mammography images and digitized film screen images must not be reviewed for primary image interpretations.

When accessing VitreaView from a mobile device, images viewed are for informational purposes only and not intended for diagnostic use.

VitreaView is a cross-browser, cross-platform, zero-footprint universal medical image viewer solution capable of displaying both DICOM and non-DICOM medical images for diagnosis, review, communication, and reporting.

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 multi-dimensional 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 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 accessing VitreaView from a mobile device, images viewed are for informational purposes only and not intended for diagnostic use.

The Acceptance Criteria and Study for the VitreaView software are as follows:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Test Set Sample Size: The document does not specify a numerical sample size for the test set used in the validation by radiologists. It generically refers to "datasets."
• Data Provenance: Not explicitly stated regarding country of origin or whether 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:

• Number of Experts: "Experienced radiologists" were used. The exact number is not specified.
• Qualifications of Experts: They were "experienced radiologists." Further specific qualifications (e.g., years of experience, subspecialty) are not provided.

4. Adjudication method for the test set:

• The document does not describe a formal adjudication method (e.g., 2+1, 3+1). It states that "In all cases the radiologists found the display to be of diagnostic quality," implying a consensus or individual judgment without a specific adjudication process 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:

• No MRMC comparative effectiveness study involving AI assistance for human readers was mentioned or performed. The device is a medical image viewing and information distribution application, not an AI-powered diagnostic aid that enhances human reader performance.

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

• The device described is a medical image viewer intended for human use and interpretation. There is no mention of a standalone algorithm-only performance assessment. The "external validation" involved human radiologists evaluating the display.

7. The type of ground truth used:

• The ground truth for assessing diagnostic quality was expert consensus/judgment from experienced radiologists. They evaluated "brightness, sharpness, artifacts, and overall diagnostic quality" of the displayed images.

8. The sample size for the training set:

• The document does not describe a training set or a machine learning model that would require one. The VitreaView software is a viewing and information distribution application, and its validation focused on software functionality, risk management, and the diagnostic quality of its image display as perceived by human readers, rather than training an algorithm.

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

• Not applicable, as no training set for a machine learning model is mentioned in the document.

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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.

Ask a specific question about this device

VitreaView is a medical image viewing and information 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.

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 referring clinicians and other medical professionals with access to patients' medical images in a seamless way, with integrations into their Electronic Health Record (EHR), Electronic Medical Record (EMR), and Health Information Exchange (HIE)

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.

Some of the general features include:

• 2D multi-modality review of data .
• . Basic 2D review tools (zoom, pan, measure)
• Easy study navigation .
• Comparative review .
• Displays of DICOM and non-DICOM images .
• A scalable, virtualizable infrastructure .
• Cross-platform viewing capabilities (Windows, Mac OS, etc.) .
• Leveraging of next-generation protocols for image viewing (i.e. MINT) .
• Single sign-on . .
• EMR integration ●

The provided text is a 510(k) summary for VitreaView, a medical image viewing software. However, it does not contain specific acceptance criteria, a detailed study protocol for performance claims, or quantitative data demonstrating device performance against such criteria.

Instead, the document focuses on:

• Substantial Equivalence: Arguing that VitreaView is substantially equivalent to a predicate device (Cedara WebAccess™) based on similar indications for use and "essentially identical technological characteristics."
• Design and Testing Philosophy: Stating that the software was "designed, developed and tested according to written procedures" and that "Software testing was completed to insure the new feature function according to the requirements and interacts without impact to existing functionality."

Since the document explicitly states, "The test results support a determination of substantial equivalence," but does not provide a summary of said test results, specific acceptance criteria, or performance metrics, I cannot fulfill most of your requests directly from the provided text.

Here's what I can extract and what I cannot:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size Used for the Test Set and Data Provenance:

• Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method:

• Not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• Not mentioned or implied. The document focuses on the device's functionality and equivalence to a predicate, not comparative effectiveness with human readers.

6. Standalone Performance Study (Algorithm Only):

• This is an image viewer and information distribution application, not an AI algorithm for diagnosis. Therefore, a "standalone algorithm performance" study as typically understood for AI diagnostic tools would not be applicable or expected. The document implies functional testing of the software itself.

7. Type of Ground Truth Used:

• Given it's a viewer that integrates with EHR/EMR and displays DICOM/non-DICOM data, the "ground truth" for its functional testing would likely revolve around:
  • Correct display of various image formats (DICOM, non-DICOM).
  • Accurate functionality of basic 2D review tools (zoom, pan, measure).
  • Successful navigation and comparative review.
  • Integration capabilities (EMR, HIE).
  • Data integrity during transfer and display.
• However, the document does not explicitly state the type of ground truth used for performance validation.

8. Sample Size for the Training Set:

• As a viewer and not an AI/ML algorithm that learns from data, there isn't a "training set" in the conventional sense for this device. Functional testing would involve various types of medical images and data, but not a dataset for model training.

9. How Ground Truth for the Training Set Was Established:

• Not applicable for a non-AI/ML device.

In summary: The provided 510(k) document is a regulatory submission focused on demonstrating substantial equivalence. It confirms that internal software testing was performed to ensure functionality, but it does not provide the detailed public study results, acceptance criteria, or performance metrics typically requested for a detailed AI/ML device analysis. The device itself is a medical image viewer, not an AI diagnostic tool, so many of the questions (e.g., training set, reader studies) are not directly applicable.

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