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interWORKS is intended to provide a completely scalable PACS solution for hospitals and other related sites, which will distribute, retrieve archive, and display, data and images from a variety of different modality and information systems. This also includes the display of structured reports and mammography images that have been created according to DICOM "For Presentation", and will include standard features and other Mammo tools. Lossy compressed mammography images and digitized film screen images must not be used for primary image interpretations. Mammographic images may only be interpreted using an FDA cleared display that meets technical specifications reviewed and accepted by FDA.

Application areas include imaging centers, radiologist central reading rooms and any other locations where trained medical professionals would require access or desire patient images, demographic information or other patient medical information captured in the system.

InterWorks™ is comprised of software modules that can work together to deliver an integrated solution that provides image capture, storage, distribution, enhancement, manipulation, and networking of medical images at distributed locations.

The Image Management module that manages your imaging needs for a Radiology Enterprise. All of the different facets are unified under a single system that acquires, distributes, storages, displays and prints medical images. By interVIEW supporting DICOM 3.0 it can Plug-n-Play with other DICOM 3.0 compliant devices with minimal effort. For Practices that are in need of only Tele-Radiology functionality interVIEW TR will meet all the needs.

The Dictation/Transcription module that provides for reliable creation of Radiology reports for your Radiology Enterprise. Reports are created via Voice Recognition with interSCRIBE VR or Digital Dictation with interSCRIBE DD depending upon the individual preferences of the Radiologist and requirements of the Radiology Enterprise. In either case digital files are created by the Radiologist and sent to transcription worklists for transcribing or editing by transcriptionists using interSCRIBE DT. Once the report has been finalized it is electronically signed by the Radiologist and automatically delivered via fax and/or e-mail to referring physicians.

The Radiology Information System module manages your radiology workflow for your Front Desk, Back Office and Clinical operations. All of the different areas of operations are unified under a single system that extends beyond the physical walls to exchange information and perform transactions with important business partners. Most importantly, the rules engine that drives interFLOW can be tailored.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Avreo interWORKS System (K141556):

Overall Assessment: The provided document is a 510(k) summary for a PACS system, not a detailed clinical study report for a diagnostic AI device. Therefore, much of the requested information regarding acceptance criteria, specific performance metrics, and detailed study design for a diagnostic AI is not present. This submission focuses on demonstrating substantial equivalence to a predicate PACS device (K070755) and primarily addresses functional modifications related to image storage.

Acceptance Criteria and Reported Device Performance

The submission does not outline specific numerical acceptance criteria or quantitative performance metrics typically seen for diagnostic AI devices (e.g., sensitivity, specificity, AUC). Instead, the "acceptance criteria" are implied by the claim of substantial equivalence to the predicate device and success in a regression test.

Study Details (Based on the provided text)

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

   • Sample Size: Not specified. The document mentions a "regression test" but does not provide details on the number of cases, images, or subjects used in this test.
   • Data Provenance: Not specified. It's likely that the "regression test" used internal data or test cases to verify the functionality of the software changes. There's no indication of clinical data from specific countries or whether it was retrospective or prospective.

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

   • Not applicable. The "regression test" described is a software verification test, not a clinical study requiring expert ground truth for diagnostic accuracy.

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

   • Not applicable. No clinical adjudication method is mentioned as this was not a diagnostic accuracy study.

4. 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, an MRMC comparative effectiveness study was not done. This submission is for a PACS system, not an AI-assisted diagnostic device. The device's primary function is image management, archiving, and display, not automated interpretation or direct assistance in clinical diagnosis beyond providing the images and related information.

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

   • No, a standalone performance study was not done. The device itself is a PACS, an infrastructure component, not an algorithm performing a diagnostic task in isolation.

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

   • Not applicable in the context of a clinical diagnostic ground truth. For the "regression test," the "ground truth" would be the expected software behavior and output as defined by the system's specifications.

7. The sample size for the training set:

   • Not applicable. This is not a machine learning or AI algorithm development submission, so there is no mention of a "training set."

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

   • Not applicable (as above).

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