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The iiSYS PACS System is a complete PACS System for the Transmission, Display, Archive, and printing of patient images and demographic information. The system is indicated for the assembly, organization, sharing, and display of patient images and demographic information for diagnostic and referral purposes.

Application areas include radiologist central reading rooms or any location where a medical professional would require or desire access to patient image and demographic information.

The iiSYS PACS System is a full featured PACS System capable of transmission, archive, display, and print of patient image and demographic information. Its purpose is to facilitate these operations utilizing shared data to promote the availability of information at remote facilities and at locations other than that at which it was acquired. Data may be received as digital information, video signals, or hard copy prints and may be reviewed via monitor or printed hardcopy. The iiSYS PACS system consists of the following major components:

• Series of Viewing and Reading Workstations
• Teleradiology devices for digitizing and transmission of images over wide area or local area networks for remote or at home review
• Archive for short or long term storage
• LINX Network system for secondary capture and transmission of images to other devices (workstation, telerad, printers, etc.)
• Printers

The provided text describes the "iiSYS PACS System," which is a Picture Archiving and Communication System. This document is a 510(k) submission, confirming that the device is substantially equivalent to existing, cleared devices.

Here's an analysis of the requested information based on the provided text:

Key Takeaway: The provided document is a 510(k) submission for a PACS system, which is essentially an integration of existing, cleared components. The "study" referenced is primarily an integration testing protocol to ensure connectivity and efficiency of these components when used together, rather than a clinical performance study measuring diagnostic accuracy. Therefore, many of the typical acceptance criteria and study details for an AI/CAD device (like sensitivity, specificity, reader performance improvements) are not applicable or not reported in this type of submission.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of the device (a PACS system integrating existing components) and the provided document (a 510(k) summary focused on substantial equivalence), the acceptance criteria are centered on connectivity, compatibility, and functional integrity rather than diagnostic performance metrics like sensitivity or specificity.

Study Proving Device Meets Acceptance Criteria

The study described is an integration testing protocol.

• Study Type: Integration Testing / Verification and Validation (V&V) of a system composed of previously cleared devices.
• Purpose: To ensure the connectivity, compatibility, and efficient operation of various PACS components (workstations, teleradiology devices, archive, network system, printers) when assembled as the "iiSYS PACS System." The goal is to demonstrate that the integrated system functions as intended without negatively impacting the safety or efficacy of its individual cleared components.
• Methodology: The document states that the integration is accomplished via a "comprehensive testing protocol" and an "Integration Test Protocol." While specifics of the tests are not detailed, in general, such protocols would involve end-to-end testing of image transmission, storage, retrieval, display, and printing functionalities, along with checks for data integrity and system performance under various loads. Given the 1998 date, these would have likely been manual and automated functional tests.

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

• Sample Size for Test Set: Not explicitly stated as a number of patient cases or images. The "test set" for this type of system would likely involve a varied set of representative digital images and demographic data to test the system's core functionalities (transmission, archive, display, print) and interoperability standards (e.g., DICOM). The focus is on system functionality rather than diagnostic accuracy on a specific disease cohort.
• Data Provenance: Not specified. Given it's a PACS system for general use, the test data would likely be synthetic, anonymized clinical data, or existing institutional data used for system testing. The country of origin and retrospective/prospective nature are not mentioned, but it would typically be retrospective internal testing data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Number of Experts: Not applicable or not specified. For a PACS integration study, ground truth in the sense of diagnostic accuracy (e.g., presence/absence of disease) is not the primary focus. The "ground truth" would relate to whether the system correctly transmits, stores, retrieves, and displays images and data according to specifications. This is typically verified by engineers, IT professionals, and potentially radiologists acting as end-users validating system functionality, rather than establishing diagnostic ground truth on patient cases.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable or not specified. Adjudication (like 2+1, 3+1 for resolving diagnostic discrepancies) is used when establishing a diagnostic ground truth for patient cases. For a pure PACS integration test, the "judgement" is whether the system performs according to its functional specifications, which is typically a pass/fail outcome for each test case, often determined by a single tester or a testing team without a formal adjudication panel.

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

• MRMC Study: No. An MRMC study is designed to evaluate the diagnostic performance of a device, often a CAD or AI algorithm, and its impact on human reader performance. The iiSYS PACS System is an infrastructure device, not a diagnostic aid or AI algorithm. Its purpose is to manage images, not to interpret them or improve human interpretation accuracy.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

• Standalone Study: No. This question applies to AI/CAD algorithms that provide a diagnostic output. The iiSYS PACS System does not have a standalone "algorithm" for diagnostic purposes. Its standalone performance relates to its functional operability (e.g., successful image transfer rate, storage capacity, display capabilities), which would have been part of the integration testing.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: For this PACS system, the "ground truth" is primarily based on functional specifications and expected system behavior. This means:
  • Data Integrity: Verifying that images and demographic information are transmitted, stored, and retrieved without corruption or loss.
  • Display Accuracy: Ensuring images are displayed correctly with appropriate resolution and tools.
  • Connectivity: Confirming that all integrated components can communicate and exchange data.
  • Performance: Demonstrating that the system responds within acceptable timeframes for various operations.
  • This is not "expert consensus, pathology, or outcomes data" in the diagnostic sense, but rather verification against engineering and functional requirements.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. For a PACS system that integrates existing components, there isn't a "training set" in the context of machine learning. The system's "knowledge" or functionality is engineered through software development and configuration, not learned from data in the AI sense.

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

• How Ground Truth for Training Set Was Established: Not applicable, as there is no "training set" for this type of device in the context of learning or AI.

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