Search Results

The intended use of DIGITIZER DIRECTOR: Image Compression Module is as a DICOM image compressor that will enable any hospital or medical facility PC with adequate memory and the appropriate film digitizer to send images to other DICOM entities such as viewing stations and databases in an efficient manner. The compression algorithms used are fully supported by the DICOM 3.0 Standard.

DIGITIZER DIRECTOR: Image Compression Module is an expansion module to Digitizer Director. The module provides a compression solution which uses compression algorithms which are fully supported by DICOM. Digitizer Director is a DICOM 3.0 compliant secondary image capture application for the Howtek family of film digitizers, It is a query / retrieve service class provider (QRSCP). DIGITIZER DIRECTOR and the Image Compression Module are Microsoft Windows NT or Windows 95 based applications.

This document describes a Teleradiology System, specifically an "Image Compression Module" for the Digitizer Director. The submission primarily focuses on the device's functionality as a DICOM image compressor and its substantial equivalence to a predicate device.

Here's an analysis of the provided information regarding acceptance criteria and studies:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state specific, quantifiable acceptance criteria such as performance metrics (e.g., compression ratio, image quality preservation thresholds, speed targets for compression/decompression).

Instead, the "Validation of Effectiveness" section states: "Extensive testing of the software package has been performed by programmers, by nonprogrammers and by potential customers."
And also, under "Summary Statement of Safety and Effectiveness," it notes: "Software is only used for control purposes and has no bearing on image quality. There is no image processing or compression used with this software." (This contradicts the device name and intended use, which clearly state it's an "Image Compression Module" that uses "compression algorithms").

Given the contradiction and lack of specific performance metrics, it's impossible to create a table of acceptance criteria and reported device performance as requested. The device is being cleared based on its function as a DICOM image compressor that is fully supported by the DICOM 3.0 Standard, and its substantial equivalence to a predicate device (Applicare RadWorks) that also uses JPEG compression in DICOM format. The key "performance" claimed is compliance with DICOM 3.0 and the use of supported compression algorithms.

2. Sample size used for the test set and the data provenance:

• Sample Size for Test Set: Not specified. The document vaguely mentions "Extensive testing" and "nonprogrammers and by potential customers" but provides no numbers of images, cases, or users.
• Data Provenance: Not specified. There is no mention of the country of origin of the data or whether it was retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: Not specified. The document mentions "nonprogrammers and by potential customers" but does not define them as "experts" in the clinical sense, nor does it quantify how many were involved or their qualifications. Given the device's function as a compression module, clinical experts might not have been deemed necessary if the claim was simply about adherence to standards and not about diagnostic accuracy.
• Qualifications of Experts: Not specified.

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

Not specified. No adjudication method is mentioned.

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:

• MRMC Study: No, an MRMC comparative effectiveness study was not conducted or reported. This device is an image compression module, not an AI diagnostic tool.
• Effect Size: Not applicable.

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

• Standalone Performance: The documentation implies a form of standalone performance was assessed in terms of software functionality and DICOM compliance. The statement "Software is only used for control purposes and has no bearing on image quality. There is no image processing or compression used with this software" is highly contradictory to the device's name and intended use, which is an "Image Compression Module" that uses "compression algorithms." If we assume the intended meaning, the "testing of the software package" by programmers and non-programmers implies evaluating the compression algorithms' functionality (e.g., successful compression and decompression, compliance with DICOM JPEG) without human interpretation of the compressed images for diagnostic purposes. However, no specific metrics or studies for this standalone performance (e.g., compression ratio, fidelity) are provided.

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

• Ground Truth: Given the nature of the device as a compression module, the "ground truth" likely related to the technical correctness of the compression and DICOM compliance, rather than clinical ground truth (e.g., disease presence). For example, a successful compression and decompression resulting in an image that is still DICOM compliant and visually/digitally consistent with the original would be the "ground truth" for its function. However, the specific methodology for establishing this is not detailed.

8. The sample size for the training set:

• Sample Size for Training Set: Not applicable. This document describes a software module for image compression, not a machine learning or AI model that requires a "training set." The software uses established JPEG compression algorithms supported by DICOM.

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

• Ground Truth for Training Set: Not applicable, as no training set for a machine learning model is mentioned.

Ask a specific question about this device