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The CDP 5000 is a software application that is used for receiving, managing, archiving, distributing and recording medical images onto portable digital media (including but not limited to Compact Disk and DVD).

The CDP 5000 receives digital images and data from various sources (including but not limited to CT, MR, US, NM, XA, RF, computed and direct radiographic devices, secondary capture devices, scanners, imaging gateways or imaging sources).

Users of CDP 5000 can perform the following tasks:

• Query and retrieve images from the CDP 5000 Archive, using u any third party workstation capable of performing DICOM query/retrieve functions.
• Use DIAGNET network viewer to search, retrieve and . manipulate patient images.
• Create DICOM compliant portable media (i.e. CD-R) I containing: original DICOM images as sent by imaging modality and the DIAGNET viewer application.

Tasks that users of DIAGNET may perform include, but are not limited to: navigation; adjustment of window width and level; image stacking; measurements; inversion; zoom; magnification; rotation, Linked navigation, MPR (Multi-Planar Reformatting).

Typical users of the CDP 5000 and DIAGNET are trained medical professionals, including but not limited to radiologists, clinicians, technologist, and others.

The CDP 5000 is a software application that is used for receiving, managing, archiving, distributing and recording medical images onto portable digital media (including but not limited to Compact Disk and DVD).

The CDP 5000 receives digital images and data from various sources (including but not limited to CT, MR, US, NM, XA, RF, computed and direct radiographic devices, secondary capture devices, scanners, imaging gateways or imaging sources).

Users of CDP 5000 can perform the following tasks:

• . Query and retrieve images from the CDP 5000 Archive, using any third party workstation capable of performing DICOM query/retrieve functions.
• L Use DIAGNET network viewer to search, retrieve and manipulate patient images.
• . Create DICOM compliant portable media (i.e. CD-R) containing: original DICOM images as sent by imaging modality and the DIAGNET viewer application.

Tasks that users of DIAGNET may perform include, but are not limited to: navigation; adjustment of window width and level; image stacking: measurements; inversion; zoom; magnification; rotation, Linked navigation, MPR (Multi-Planar Reformatting).

Typical users of the CDP 5000 and DIAGNET are trained medical professionals, including but not limited to radiologists, clinicians. technologist, and others.

The CDP 5000 Ensemble is a software application that is used for receiving, managing, archiving, distributing and recording medical images onto portable digital media (including but not limited to Compact Disk and DVD). The DIAGNET Viewer software, which is copied to every CD produced, runs automatically when the CD is placed in any Windows-based PC; it allows the examinee to view, manipulate, perform measurements on, and post-process the received images. The images can then be saved, copied, and/or sent to a file, emailed, or included in a report in process.

The provided document for CDP Ltd.'s CDP 5000 is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing detailed performance studies with acceptance criteria in the way one would see for a novel AI/CAD device.

Based on the provided text, here's a breakdown of the requested information:

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

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

The document does not specify a sample size for a test set, nor does it provide information about data provenance. The "Performance Data" section simply states that "Performance tests were performed by CDP Ltd. on the CDP 5000." This suggests internal testing, but no details on the data used are given.

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 mention the use of experts or the establishment of ground truth in the context of performance testing. This type of detail is typically associated with evaluating diagnostic accuracy, which is not the primary focus of this device's 510(k) submission.

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

The document does not describe any adjudication method.

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 was not conducted or reported. The CDP 5000 is a PACS (Picture Archiving Communications System) and not an AI or CAD device designed to assist in diagnostic interpretation, so such a study would not be relevant to its function.

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

The CDP 5000 is a software application for image management, not a diagnostic algorithm. Therefore, a "standalone algorithm only" performance study, as typically understood for AI/CAD, was not performed or reported. Its performance is demonstrated through its ability to perform its defined functions (receiving, managing, archiving, etc.).

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

The document does not specify any type of "ground truth" as it would be understood for an AI/CAD diagnostic device. The performance tests appear to be functional in nature, verifying that the system performs its intended operations correctly (e.g., images are received, archived, and displayed as expected).

8. The sample size for the training set

The document does not mention a training set. This device is an image management system and does not appear to utilize machine learning for diagnostic purposes.

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

Not applicable, as no training set or machine learning component is described.

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