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The CSIST DICOM Gateway and Image Manager™ is a device that captures 2 dimensional images and data or receives images and data from various medical imaging sources (i.e. ultrasound systems, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways or other imaging sources). Images and data (2 dimensional or 3 dimensional) can be stored, communicated, processed and displayed within the system and or across computer networks at distributed locations.

CSIST DICOM Gateway and Image Manager™ consists of three modules: A DICOM Gateway, Image Manager Server, and Image Manager Client.

• The DICOM Gateway captures (2D) images from a non-DICOM imaging modality, generates a DICOM medical image, and stores it in the CSIST Image Manager Server.
• The CSIST Image Manager Server accepts DICOM medical images (2D and 3D) from different acquisition modalities, stores these in the Image Archive and then transfers the generated images to the requested Image Manager Client.
• The Image Manager Server also has a "Performed Procedure Step Manager", which can issue a message to department system scheduler/order filler and the Image Manager Server, when the acquisition modality informs the Performed Procedure Step Manager that a specific procedure step has been started or completed.

The provided document is a 510(k) summary for the CSIST DICOM Gateway and Image Manager™ System. It focuses on demonstrating substantial equivalence to predicate devices and describes the device's function and intended use. However, it does not contain information regarding specific acceptance criteria, a detailed study proving performance against those criteria, or the methodology for establishing ground truth for a test set.

Therefore, the requested information cannot be fully extracted from the provided text.

Here's a breakdown of what can and cannot be answered based on the document:

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

• Cannot be provided. The document does not specify any quantitative acceptance criteria (e.g., accuracy, sensitivity, specificity, image quality metrics) or report performance metrics against such criteria. It states that "Any differences between the two devices will not affect safety and effectiveness" based on a finding of "substantial equivalence" to predicate devices.

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

• Cannot be provided. The document does not describe any specific test set used to evaluate the device's performance. The submission relies on demonstrating substantial equivalence, not a standalone performance study with a test set.

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 provided. Since no specific test set and ground truth establishment process is described, this information is absent.

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

• Cannot be provided. No adjudication method is mentioned as there's no described test set.

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 provided. The device is a Picture Archiving and Communications system (PACS) component, not an AI-assisted diagnostic tool. No MRMC study is mentioned, nor would it be typical for this type of device to evaluate AI reader improvement.

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

• Cannot be provided. The document describes the device as a system for capturing, storing, communicating, processing, and displaying medical images. It's a foundational PACS component, not an algorithm with standalone diagnostic performance. The document explicitly states: "A physician, providing ample opportunity for competent human intervention interprets images and information being printed."

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

• Cannot be provided. No ground truth definition is mentioned as no performance study is detailed.

8. The sample size for the training set

• Cannot be provided. The device is not described as an AI/ML device that requires a training set. It's a data management and display system.

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

• Cannot be provided. No training set or ground truth establishment for a training set is mentioned.

Summary based on the document:

This 510(k) emphasizes "substantial equivalence" to existing predicate devices (OLICON 02 Workstation & PACS View Software K973959 and ALI 3D Tool Module for Medical Images K003762). The "proof" is the comparison against these already cleared devices, arguing that the CSIST system's design, general function, intended use, safety, and efficacy are similar, and any differences "will not affect safety and effectiveness."

The device's role is described as:

• Capturing 2D images from non-DICOM modalities, generating DICOM images.
• Accepting, storing, and transferring 2D and 3D DICOM images from various modalities.
• Providing a "Performed Procedure Step Manager."

It is explicitly stated that a "physician, providing ample opportunity for competent human intervention interprets images and information being printed." This reinforces that the device is a tool for managing medical images, not an automated diagnostic system that requires performance metrics like sensitivity or specificity against a ground truth.

The document mentions "hazard analysis" with all potential hazards classified as "Minor," and adherence to "voluntary standards," which are common safety and effectiveness considerations for medical devices, but not specific performance metrics in the way requested for an AI/ML diagnostic algorithm.

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