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MEDIPACS™is a device that receives digital images and data from various sources (i.e. CT scanners, MR scanners, ultrasound systems, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways, etc.). Images and data can be captured, stored, communicated, processed and displayed within the system and or across computer networks at distributed locations.

MEDIPACS™ handles and displays various objects in a Picture Archive and Communication System (PACS) environment. These objects can be images, requests, patients, examination etc. The system transmits digital electronic images and generates reports over a high-speed network to centralized storage. After transmission, patient information and images are available throughout the facility to many users simultaneously.

1. Table of Acceptance Criteria and Reported Device Performance:

The provided document does not contain an explicit table of acceptance criteria or detailed performance metrics. The submission focuses on establishing substantial equivalence to a predicate device, Marotech, Inc.'s system (K012844), rather than presenting novel performance data against specific acceptance criteria. The approval is based on the device's technological characteristics being similar to the predicate and its adherence to general controls and voluntary standards.

Therefore, for this specific document, a typical performance table cannot be generated. The "performance" described is the device's ability to handle, store, communicate, process, and display digital images and data, which is stated to be substantially equivalent to the predicate.

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

The document does not describe a specific test set, sample size, or data provenance (e.g., country of origin, retrospective/prospective) for evaluating the device's performance. The review appears to be based on a comparison of the device's features and intended use with those of the predicate device.

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

No information is provided regarding experts used to establish ground truth for a test set. This type of evaluation is typically not part of a 510(k) submission for a PACS system like MEDIPACS™ when demonstrating substantial equivalence.

4. Adjudication Method for the Test Set:

No adjudication method is mentioned, as there is no described test set or performance evaluation against ground truth.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

No MRMC comparative effectiveness study is described. The submission does not claim to improve human reader performance with or without AI assistance, as it is a Picture Archiving Communications System (PACS) for handling and displaying images, not an AI diagnostic tool.

6. Standalone (Algorithm Only) Performance Study:

No standalone (algorithm only) performance study is mentioned. The MEDIPACS™ system is described as a system that handles and displays images, with a physician providing interpretation. It is not an algorithm performing a diagnostic task independently.

7. Type of Ground Truth Used:

No specific type of ground truth is mentioned. The submission focuses on substantial equivalence based on functional and technological characteristics, not on diagnostic accuracy against a ground truth.

8. Sample Size for the Training Set:

No information is provided regarding a training set. This is consistent with a PACS system, which primarily manages and displays data rather than being a machine learning algorithm requiring training data for a specific task.

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

No ground truth for a training set is mentioned, as there is no described training set or machine learning component in the context of this 510(k) submission.

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