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The KODAK Oncology Image Manager (OIM) is a picture Archiving and Communications System (PACS) which acquires, communicates, stores, and displays images, including radiographic images and patient data connected with oncology treatment.

KODAK Oncology Image Manager is designed to archive patient information and images gathered as the patient progresses through cancer treatment. The consolidated information and images can be reviewed as treatment progresses by onocologist and nurses. The general hardware configuration of the KODAK Oncology Image Manager contains the following major components: Touch screen Monitor, Central Processing Unit (CPU), Film and Document scanners, CD-ROM writer and reader.

This submission is a 510(k) premarket notification for a Picture Archiving and Communication System (PACS) from 1997. Due to the nature of PACS devices, particularly those from this era, the submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting detailed performance studies with acceptance criteria, ground truth, and statistical analyses typically associated with AI/ML-powered diagnostic devices.

Therefore, many of the requested sections (e.g., acceptance criteria, sample sizes for test/training sets, expert qualifications, MRMC studies, standalone performance, ground truth establishment) are not applicable or available in this document as it predates the rigorous performance evaluation standards now expected for AI/ML medical devices.

Here's a breakdown based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

This document does not contain a table of acceptance criteria or reported device performance in the way a modern AI/ML device submission would. The performance is implied through substantial equivalence to the predicate device, IMPAC, IMAGErt, Image Management System. The "performance" in this context refers to its ability to perform PACS functions, not diagnostic accuracy.

The comparison of features in Section VII (reproduced below) serves as the primary "performance" and "acceptance criteria" through showing functional equivalence.

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

• Sample Size: Not specified or applicable in the context of this 510(k) for a PACS device. No "test set" in the sense of a dataset for evaluating diagnostic performance metrics was used or discussed. The evaluation focused on functional equivalence.
• Data Provenance: Not specified or applicable.

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

• Not specified or applicable. Ground truth for diagnostic performance is not relevant to a PACS functional equivalence submission.

4. Adjudication Method for the Test Set

• Not applicable. No test set requiring adjudication was described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, and Effect Size

• No, an MRMC comparative effectiveness study was not performed. This type of study is for evaluating diagnostic performance, which is not the focus of a PACS functional equivalence claim.

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

• No, a standalone performance study as understood for AI/ML algorithms was not performed. This device is a PACS, an infrastructure tool, not a diagnostic algorithm.

7. The Type of Ground Truth Used

• Not applicable. Ground truth for diagnostic performance is not relevant to a PACS functional equivalence submission. The "ground truth" here is the established functionality of the predicate device.

8. The Sample Size for the Training Set

• Not applicable. This device is a PACS, not an AI/ML model that requires a training set.

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

• Not applicable. As there is no training set for an AI/ML model, no ground truth needed to be established for it.

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