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ONIS-PACS is a software device intended to be used by healthcare personnel and intended for viewing, reviewing, performing measurements/quantifications and reporting of medical images and data acquired from DICOM compliant medical imaging systems. Images and data can be stored, communicated, processed and displayed within the system or across computer networks at distributed locations. Lossy images and digitized film images must not be used for primary diagnosis or interpretation.

ONIS-PACS is a Picture Archiving Communication System (PACS) fully compliant with the DICOM version 3.0 Standard. It is suitable for storing, distributing, retrieving, visualizing, manipulating, performing measurements/quantifications and reporting various DICOM objects. ONIS Viewer is a desktop application that makes it possible to visualize, manipulate and process medical images of many different modalities. Basic and advanced tools are provided for manipulating and processing images, including multi-planar reconstruction and 3D volume rendering. The software also includes a Local Server service running in the background that can send and receive DICOM images and can respond to DICOM queries. The ONIS Viewer and the Local Server applications must run on the same computer. ONIS Remote is a desktop application identical to the ONIS Viewer, except that it runs without the Local Server. It must connect to a Site Server to retrieve the studies to be retrieved and processed. WebONIS is an ActiveX component loaded into an HTML page that provides the same functionality as the ONIS Remote application. It can only be used with the Microsoft Internet Explorer browser. The browser must connect to a web server to load the ActiveX component, and the latter then connects directly to an ONIS Site Server or an ONIS Organization Server. ONIS Site Server is a server application that supports the storage and retrieval of a wide range of DICOM Storage objects. It also supports the storage and retrieval of graphical annotations and reports when connected to ONIS client applications (ONIS Viewer, ONIS Remote, and WebONIS). ONIS Organization Server is a server that provides a single access point to multiple Site Servers.

This Premarket Notification (510k) Summary for ONIS-PACS does not include detailed acceptance criteria or a dedicated study showing the device meets specific performance criteria. Instead, it relies on substantial equivalence to an existing predicate device (iQ-System PACS) based on similar indications for use and technological characteristics.

However, it does state that "Software Validation and Verification testing was performed on the ONIS-PACS device to demonstrate safety and effectiveness." The summary does not provide specific details about these tests, the acceptance criteria used, or the results.

Therefore, many of the requested details cannot be extracted directly from the provided text.

Based on the available information, here's what can be inferred:

1. Table of Acceptance Criteria and Reported Device Performance

As specific performance acceptance criteria and reported numerical performance values are not provided in this 510(k) summary, this table cannot be populated as requested. The document emphasizes substantial equivalence rather than presenting discrete performance metrics against predefined criteria.

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

Not explicitly stated. The document mentions "Software Validation and Verification testing," which implies a test set was used, but details on its size, origin (country), or whether it was retrospective or prospective are absent.

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

Not explicitly stated. Given that the testing focuses on software validation and verification for a PACS system, ground truth might relate to technical specifications, image integrity, and functional correctness rather than clinical diagnosis. If clinical performance was assessed (which is not described), then expert review would be relevant, but details are not provided.

4. Adjudication method for the test set

Not explicitly stated.

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

No, an MRMC comparative effectiveness study is not mentioned. The device is a Picture Archiving and Communication System (PACS) and the comparison is largely about its functional similarity to another PACS system, not directly about improving human reader performance with AI assistance. The document does not describe AI components in the ONIS-PACS.

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

No, this level of detail is not provided. The "Software Validation and Verification testing" likely encompasses standalone software performance, but no specific study design or results are presented.

7. The type of ground truth used

Not explicitly stated. For a PACS system, ground truth for software validation would typically involve verifying that images are displayed correctly, measurements are accurate, data is stored and retrieved properly, and functionalities match specifications. It's unlikely to involve clinical outcomes or pathology in the context of this 510(k) summary for a PACS.

8. The sample size for the training set

Not applicable. The document does not describe a machine learning or AI component that would require a dedicated training set. The "Software Validation and Verification testing" would involve testing the software's functionality, not training a model.

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

Not applicable, as there is no mention of a training set for a machine learning model.

Summary Statement from the Document:

The most relevant statement regarding performance and acceptance criteria is: "Software Validation and Verification testing was performed on the ONIS-PACS device to demonstrate safety and effectiveness." This indicates that the software was tested to ensure it operates as intended and meets its non-clinical functional requirements, consistent with a "moderate level of concern" software as per FDA guidance. The successful completion of these tests serves as the proof that the device meets the necessary criteria for its intended use, based on the principle of substantial equivalence to the predicate device.

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ETIAM PACS Components™ is intended to be used by healthcare personnel to import, review, edit and send medical images.

ETIAM PACS Components™ is not labeled for diagnostic use.

ETIAM PACS Components™ is a device that receives medical images (including mammograms) and data from various imaging sources. Images and data can be stored, communicated, processed and displayed within the system or across computer networks at distributed locations.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using an FDA approved monitor that meets other technical specifications reviewed and accepted by FDA.

Typical users of this system are trained professionals, e.g. physicians, radiologists, nurses, medical technicians, and assistants.

ETIAM PACS Components™ are software applications that make possible the capturing, storage, distribution, and networking of medical images at distributed locations. In cases where DICOM images are not directly available to ETIAM PACS Components™, the system can acquire medical images using a DICOM gateway, which generates DICOM-type files. For example, film digitizers obtain images from old film and convert them to meet DICOM standards and stored in an archive. Stored files are transmitted using a network and can be viewed or manipulated from an imaging workstation.

The ETIAM PACS Components™ device did not undergo a clinical study as the manufacturer deemed it unnecessary to evaluate the safety or effectiveness. This determination was made because the device "only utilizes standard lossy (irreversible) compression techniques defined and supported by DICOM Standard" and the compression function must be activated by an administrator. Therefore, the information typically derived from such studies (like acceptance criteria, sample sizes, expert qualifications, etc.) is not available in the provided text.

The primary method for demonstrating safety and effectiveness was through non-clinical performance evaluations and a comparison to predicate devices, showing substantial equivalence in technological characteristics.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance: Not applicable, as no clinical test set was used for performance validation. Non-clinical performance was validated through "test bench" activities.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as no clinical test set was used for performance validation.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable, as no clinical test set was used for performance validation.

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: Not applicable. The device is a PACS component, not an AI-assisted diagnostic tool. No MRMC study was performed.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The device itself is a standalone software product for managing medical images. Its performance was assessed through non-clinical bench testing.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable for the clinical performance, as none was conducted scientifically. For non-clinical performance, the "ground truth" would be the expected functionality and output based on design specifications and DICOM standards.

8. The sample size for the training set: Not applicable, as no AI/machine learning component is described that would require a training set.

9. How the ground truth for the training set was established: Not applicable, as no AI/machine learning component is described.

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