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DG 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.

DG Viewer, a component of DG PACS, displays and manages diagnostic quality DICOM images.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using cleared monitors intended for mammography display.

Not intended for diagnostic use on mobile devices.

DG PACS is considered a 'Continuous Use' device. This device is compliant with HIPAA/HITECH, Safe Harbor, 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.

DG PACS is a software application that handles and manipulates digital medical images for diagnostic, clinical, research or education purposes. The device does not contact the patient, nor does it control any life sustaining devices. A physician interprets images and information on DG PACS.

In general, a Picture Archiving and Communication System (PACS) is a medical imaging technology that provides storage of, and convenient access to, images from multiple modalities. Electronic images and reports are transmitted digitally via PACS: this eliminates the need to manually file, retrieve, or transport film jackets. The universal format for PACS image storage and transfer is the Digital Imaging and Communications in Medicine (DICOM 3.x). Non-image data, such as scanned documents or dictated reports, may be incorporated using consumer industry standard formats like Portable Document Format (PDF) once encapsulated in DICOM.

The provided text describes the 510(k) summary for DG PACS, a Picture Archiving and Communications System (PACS). However, it does not contain a study proving the device meets specific acceptance criteria in terms of clinical performance (e.g., diagnostic accuracy or improvement in human reader performance). The document primarily focuses on demonstrating substantial equivalence to a predicate device (eRADS RIS/PACS) through nonclinical testing and adherence to standards.

The nonclinical testing section discusses evaluation of access specifications, configuration, and functional specifications, data integrity, PHI confidentiality, and image display quality. There is no reported clinical study, comparative effectiveness study (MRMC), or standalone algorithm performance data.

Therefore, I cannot populate most of the requested fields as the information is not present in the provided text.

Here's what can be extracted and what is not available:

1. A table of acceptance criteria and the reported device performance:
   • Acceptance Criteria (Nonclinical): Based on the text, the acceptance criteria for nonclinical testing involved meeting "in-house testing criteria" for "all input functions, output functions, and actions performed by DG PACS software in each operational mode," as well as "data integrity and PHI confidentiality." For image display quality, it was "successful" based on "industry QC monitor benchmarks developed by Society of Motion Picture & Television Engineers (SMPTE), American Association of Physicists in Medicine (AAPM), and David Clunie's pixel spacing test images for DICOM."
   • Reported Device Performance: The text generally states that the device "passed all in-house testing criteria" and that verification and validation activities "demonstrated that the predetermined acceptance criteria were met." For image display quality, "All image display quality validation was successful."
   • No acceptance criteria or reported performance for clinical diagnostic metrics (e.g., sensitivity, specificity, AUC) are provided.

2. Sample sizes used for the test set and the data provenance: Not specified for any clinical performance testing. The nonclinical testing refers to "in-house testing" without detailing sample sizes of test cases or data provenance.

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 with expert-established ground truth is described.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable as no clinical study with ground truth establishment 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: No MRMC study was mentioned or performed according to the provided text. The device is a PACS, not an AI-assisted diagnostic tool in this context.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No standalone algorithm performance was mentioned or performed. This device is a PACS for displaying and managing diagnostic images, not a diagnostic algorithm itself.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable for clinical performance. For nonclinical tests, the "ground truth" was internal test plan requirements and industry standards (SMPTE, AAPM, DICOM for image quality).

8. The sample size for the training set: Not applicable as the device is a PACS, not a machine learning algorithm requiring a training set for diagnostic classification.

9. How the ground truth for the training set was established: Not applicable.

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