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The KODAK PACS is an image management system whose intended use is to provide completely scaleable local and wide area PACS solutions for hospital and related institutions/sites, which will archive/distribute/retrieve and display images and data from all hospital modalities and information systems. This includes the display of structured reports from CAD systems with DICOM "for presentation" mammography images.

The KODAK PACS is an image management system.

The provided documents are a 510(k) clearance letter for the KODAK PACS and its Statement of Intended Use. They do not contain sufficient information to answer the detailed questions about acceptance criteria, study design, and performance metrics for a medical device's AI component. The KODAK PACS described here is an "image management system," which based on the 2006 date, likely refers to a Picture Archiving and Communication System (PACS) for storing, retrieving, distributing, and displaying medical images. It notes the display of "structured reports from CAD systems with DICOM 'for presentation' mammography images," implying it can integrate with CAD (Computer-Aided Detection) systems, but it is not itself a CAD system or an AI-based diagnostic tool.

Therefore, most of the specific questions cannot be answered from the provided text.

Here's a breakdown of what can and cannot be answered:

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

   • Cannot be answered. The document describes a PACS system for image management and display, not an AI diagnostic device with specific performance metrics. There are no acceptance criteria or performance data provided.

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

   • Cannot be answered. No study details are provided as this is a PACS system clearance, not a performance study for a diagnostic algorithm.

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):

   • Cannot be answered. Ground truth establishment is not relevant for a PACS system's clearance as described here.

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

   • Cannot be answered. Not applicable for a PACS system clearance.

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:

   • Cannot be answered. No MRMC study is mentioned. The device is a PACS, not an AI assistance tool.

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

   • Cannot be answered. Not applicable; this is an image management system, not a standalone algorithm.

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

   • Cannot be answered. Not applicable to the clearance of a PACS system.

8. The sample size for the training set:

   • Cannot be answered. No training set for an AI algorithm is mentioned.

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

   • Cannot be answered. Not applicable.

Summary of available information:

• Trade/Device Name: KODAK PACS
• Regulation Number: 21 CFR 892.2050
• Regulation Name: Picture archiving and communications system
• Regulatory Class: II
• Product Code: LLZ
• Intended Use: To provide completely scalable local and wide area PACS solutions for hospital and related institutions/sites, which will archive/distribute/retrieve and display images and data from all hospital modalities and information systems. This includes the display of structured reports from CAD systems with DICOM "for presentation" mammography images. (This last part indicates its ability to integrate with CAD, not that it is a CAD system itself).

In conclusion, the provided documents pertain to the regulatory clearance of a PACS system, which is infrastructure for medical image management, and not a device that would require the kind of performance study details requested for an AI diagnostic or assistance tool.

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