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DICOM 3.0 Software Version 2.0 is intended to provide communication and data interchange between multi-modality medical imaging devices, while maintaining the integrity of the image data.

DICOM 3.0 Software Version 2.0 is a connectivity package software developed according to the ACR-NEMA standard for Digital Imaging and Communication in Medicine (DICOM 3.0). This software converts medical images, such as NM, CT, MRI, or Ultrasound, that are in DICOM 3.0 specified format into Pegasys image format and vice-versa to enable data communication between ADAC Pegasys systems and other medical imaging devices.

Three major operations can be performed with DICOM 3.0 Software Version 2.0 - image data transfer between ADAC systems, data output to DICOM compatible printers, and transfer of image data between ADAC and non-ADAC systems.

Here's an analysis of the provided text regarding the acceptance criteria and study for the DICOM 3.0 Software Version 2.0 (K973239):

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state a specific sample size (e.g., number of images, patient cases) for the test set. It broadly refers to "Import and Export functions" and "Print functions" being "comprehensively tested" and "All tests passed."

• Test Set Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

The document does not mention the use of experts to establish a ground truth for the test set. The testing appears to be focused on the functional performance of the software against its technical specifications, rather than evaluating clinical performance that would require expert review.

4. Adjudication Method for the Test Set

No adjudication method is mentioned as there's no indication of multiple readers or subjective assessment required for the functional testing conducted.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The device is a connectivity software, not an AI-assisted diagnostic tool for which such a study would be relevant.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, in a way. The testing described is a standalone evaluation of the software's functional performance – how well it transfers and handles image data. There's no human element being integrated into the performance evaluation itself for this type of device. The focus is purely on the algorithm's ability to execute its intended technical functions.

7. The Type of Ground Truth Used

The ground truth used for this study was the expected functional behavior and technical specifications of the DICOM 3.0 standard and the software itself. For instance, if an image was imported, the "ground truth" would be that the exported image should be an exact, integrity-maintained replica of the original, as defined by DICOM 3.0.

8. The Sample Size for the Training Set

The concept of a "training set" is not applicable to this device. This is a software application for image communication and conversion, not a machine learning or AI-driven system that requires a training set.

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

As there is no training set for this type of software, this question is not applicable.

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