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View is a software application that displays, processes, and analyzes medical image data and associated clinical reports to aid in diagnosis for healthcare professionals. It streamlines standard and advanced medical imaging analysis by providing a complete suite of measurement tools intended to generate relevant findings automatically collected for export and save purposes.

Typical users of this system are authorized healthcare professionals.

Mammography images may only be interpreted using a monitor compliant with requirements of local regulations and must meet other technical specifications reviewed and accepted by the local regulatory agencies. Lossy compressed mammographic images and digitized film screen images should not be reviewed for primary image interpretations with use of the View.

View is a cloud-native software application designed to support healthcare professionals in the display, processing, and analysis of medical image data. It enhances diagnostic workflows by integrating intelligent tools, streamlined accessibility, and advanced visualization capabilities, including specialized support for breast imaging.

View brings together 2D imaging, basic 3D visualization and advance image analysis in a single, intuitive interface. This simplifies information access, improves workflow efficiency, and reduces the need for multiple applications.

Key features include:

• Smart Reading Protocol (SRP) which uses machine learning for creating and applying hanging protocols (HP)
• AI workflow to support both DICOM Secondary Capture Object & DICOM Structured Report for displaying AI findings and enabling rejection/modification of the AI findings.
• Displays 2D, 3D, and historical comparison exams in customizable layouts.
• Enables smooth transitions between 2D and 3D views, either manually or as part of hanging protocols.
• Advantage Workstation integration for deeper analysis through dedicated 3D applications.
• Offers a full suite of measurement, annotation and segmentation tools for DICOM images.
• Captures all measurements and annotations in a centralized findings panel.
• Enhanced access for DICOM images stored in the cloud server.
• Easy way to integrate with external systems using FHIRcast.
• Better user experience by having a native MIP/MPR/Smart Segmentation/Volume Rendering
• Seamless access to breast images through cloud with specific tools for Mammo images.

The provided FDA 510(k) clearance letter and summary for the "View" device (K253639) offer limited details regarding specific acceptance criteria and the studies conducted to prove device performance. The information is high-level and generalized.

Based on the available text, here's the breakdown of what can and cannot be extracted:

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

The document does not explicitly state quantitative acceptance criteria (e.g., minimum accuracy, sensitivity, specificity) or specific reported performance metrics for the device. It focuses on functional equivalence and verification/validation testing without presenting performance data.

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

The document states: "The Smart Reading Protocol function, which uses machine learning for creating and applying hanging protocols, was tested on various imaging modality datasets representative of the clinical scenarios where View is intended to be used."

• Sample Size: "various imaging modality datasets" – The exact sample size (number of images, cases, or patients) is not specified.
• Data Provenance: "representative of the clinical scenarios" – The country of origin and whether the data was retrospective or prospective are not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not provide any information on the number or qualifications of experts used to establish ground truth for the test set.

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

The document does not specify any adjudication method used for the test set.

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

The document mentions a "comparison was performed between the predicate device (Universal Viewer) and the subject device (View) and showed that the devices are equivalent" for the Smart Reading Protocol function. However, this is a comparison between devices, not an MRMC study designed to assess human reader improvement with AI assistance. Therefore:

• A specific MRMC comparative effectiveness study involving human readers with and without AI assistance is not described.
• Consequently, an effect size for human reader improvement is not provided.

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

The document implies that the Smart Reading Protocol (SRP) function, which uses machine learning, was tested. The statement "A comparison was performed between the predicate device (Universal Viewer) and the subject device (View) and showed that the devices are equivalent" regarding SRP suggests an evaluation of the algorithm's output. However, whether this testing was strictly standalone performance (algorithm only) versus integrated system performance is not explicitly detailed. Given the context of a 510(k) summary for a "Medical Image Management And Processing System," the testing likely covers the integrated system.

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

The document does not specify the type of ground truth used for its testing.

8. The sample size for the training set

The document does not provide any information on the sample size used for the training set.

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

The document does not provide any information on how the ground truth for the training set was established.

Summary of available information regarding acceptance criteria and study data:

Unfortunately, the provided FDA 510(k) summary is very high-level and primarily focuses on justifying substantial equivalence based on technological characteristics and general verification/validation processes. It does not contain the detailed performance metrics, sample sizes, ground truth establishment methods, or reader study details typically found in more comprehensive clinical study reports. The approval is based on the device having "substantial equivalent technological characteristics" and being "as safe and as effective" as the predicate device.

The only specific functional testing mentioned is for the "Smart Reading Protocol" using machine learning, which was compared to the predicate device to show equivalence. However, the details of this comparison (e.g., performance metrics, specific acceptance criteria for equivalence, or study design) are not included.

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