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The EC-WEB FS-PACS is intended for the manipulation, management, and display of medical images. It can manage and display images from different modalities and interfaces and can distribute those images to various workstation, image storage and printing devices using, DICOM or similar standards. Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using cleared monitors intended for mammography display. Typical users of this system are trained medical professionals, including physicians, nurses, technicians and computer system professionals.

The EC-WEB FS-PACS system is based on DICOM standard application. The main function of FS-PACS is about medical image management within a PACS environment. It's including image archival, retrieval and distribution of medical images.

FS-PACS can support the following modality: CR, ES, NM, RF, US, CT, MG, OT, RT, XA, DX, MR, PT, SC, VR, IO, SR. There is no 3D image manipulation functions. FS-PACS provides the following measurement tools: 2-point distance, 3-point angles, ellipse area, and square area. FS-PACS can support the iOS device (i.e., iPad and iPhone). Images reviewed on the mobile device are not intended for diagnostic use. FS-PACS does not include any type of imaging hardware. It does not provide any masking or image filtering functions.

The provided text is a 510(k) summary for the EC-WEB FS-PACS, a Picture Archiving and Communications System (PACS). This document primarily focuses on establishing substantial equivalence to a predicate device (DATACOM DC-PACS K083182) rather than providing detailed acceptance criteria and a study demonstrating performance against those criteria.

Therefore, many of the requested details about acceptance criteria, device performance metrics, and study specifics are not available in the provided text. The document states that "Non-clinical data are employed upon submission of this 510(k) premarket notification according to the FDA guidance document," and that "This submission contains the results of software validation that the risks analysis and the potential hazards have been classified as Moderate Level of Concern." However, it does not explicitly describe the test methods, acceptance criteria, or performance results in detail.

Here's a breakdown of the available information:

1. Table of acceptance criteria and the reported device performance:

This information is not explicitly provided in the document. The submission focuses on functional equivalence to a predicate device. It states that "The conclusions drawn from the non-clinical tests demonstrate that the device is as safe, as effective, and performs as well as the legally marketed device identified in the submission." However, specific numerical acceptance criteria or performance metrics are not presented.

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

This information is not available in the provided text. The document mentions "non-clinical data" and "software validation" but does not specify details about test sets, sample sizes, or data provenance.

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):

This information is not available. The document focuses on the technical characteristics and functional equivalence of the PACS system. It is a device for managing and displaying medical images, not an AI diagnostic tool that requires ground truth established by experts for performance evaluation. The "typical users" are described as "trained medical professionals, including physicians, nurses, technicians and computer system professionals."

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

This information is not available. As noted above, the provided document does not detail specific performance studies that would involve adjudication for a 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:

An MRMC comparative effectiveness study was not done or at least not reported in this 510(k) summary. This device is a PACS system for image management and display, not an AI-powered diagnostic aide. Therefore, the concept of human readers improving with AI assistance is not applicable in this context.

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

A standalone performance study of an algorithm is not described. The device is a PACS system that supports human review and interpretation of images. The document explicitly states that "A physician, providing ample opportunity for competent human intervention, interprets images and information being displayed and printed."

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

This information is not applicable or available. As a PACS system, it handles and displays medical images but does not generate diagnoses or interpretations that would require a "ground truth" in the diagnostic performance sense. The validation would likely be against functional requirements and DICOM compliance.

8. The sample size for the training set:

This information is not available. The FS-PACS is a software system for image management, not a machine learning model that requires a training set.

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

This information is not available and not applicable. As stated above, this device is not a machine learning model requiring a training set and associated ground truth.

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