Search Results

Image Medical Acquisition Station (IMAS) is a PACS and teleradiology software application used to acquire digitized film images, add and modify patient and study demographics, and transmit the results to DICOM PACS systems, archives and workstations. IMAS is for hospitals, imaging centers, radiologist reading practices and any user who requires and is granted access to patient image, demographic and report information.

Image Medical Acquisition Station (IMAS) is for use in hospitals, imaging centers, radiologist reading practices and any user who requires and is granted access to patient image, demographic and report information.

Lossy compressed mammography images and digitized film screen mammography images must not be reviewed for primary image interpretations. Mammography images may only be interpreted using an FDA approved monitor that offers at least 5 Mpixel resolution and meets other technical specifications reviewed and accepted by FDA.

IMAS is a software application used to acquire image data from film digitizers and send it to DICOM-compliant devices. IMAS executes on a Microsoft Windows NT, 2000 and XP workstation that is connected to a film digitizer via a SCSI cable. When IMAS initializes, it obtains some settings information from the film digitizer, and displays a user interface. From the user interface, a user logs onto IMAS using an account ID and password. Once logged into IMAS, the user has the ability to create patient and study information, instruct the film digitizer to scan one or more sheets of film and download the image data, group the data from one or more films into a folder, and send the resulting information to one or more configured destinations via DICOM.

When IMAS receives the image data from the film digitizer, it appears on the workstation monitor for review. At this point, the user can reorient the image by flipping and rotating it, adjust the window and level setting, or apply a zoom factor to it. If the digitized image is of a sheet of film containing multiple images, the user can separate the image into one or more images by defining the area of each image and creating a new image from the data in the selected area.

The provided 510(k) summary for the eRAD/ImageMedical Corp., Image Medical Acquisition Station (IMAS) does not contain the detailed study information typically requested regarding acceptance criteria and performance validation.

The document primarily focuses on demonstrating substantial equivalence to predicate devices (iCRco's Xscan32 (K002911) and Merge/eFilm's eFilm Scan (K020995)) for the purpose of regulatory clearance. It states that "Extensive testing of the software package has been performed by programmers, by non-programmers, quality control staff, and by potential customers," but it does not elaborate on the specifics of this testing in a way that would allow for the completion of the requested table and study details.

Here's an attempt to answer the questions based only on the provided text, highlighting what is missing:

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

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

• Sample Size: Not specified. The document only mentions "Extensive testing."
• Data Provenance: Not specified.

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)

• Not specified. The document mentions "programmers, non-programmers, quality control staff, and potential customers" performing testing, but does not identify them as "experts" establishing ground truth in a clinical sense, nor their qualifications.

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

• Not specified.

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. This device is an acquisition station, not an AI-assisted diagnostic tool.

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

• This device is a software application for acquiring and transmitting images. Its performance is inherent in its ability to correctly perform these functions. The concept of "standalone performance" as it applies to an AI diagnostic algorithm does not directly translate here. The document implies its standalone functionality by stating it "acquires image data" and "sends it to DICOM-compliant devices."

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

• Not specified in a clinical sense. For this type of device (an image acquisition and transmission system), the "ground truth" would likely relate to the integrity and accuracy of the digital image representation compared to the original film, correct DICOM formatting, and successful transmission. The document does not detail how these aspects were validated.

8. The sample size for the training set

• Not applicable. This device is an image acquisition and communication software, not a machine learning or AI model that requires a training set.

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

• Not applicable (as it's not an AI/ML device requiring a training set).

Conclusion based on the provided text:

The 510(k) summary for the IMAS device provides a high-level overview of its function and claims of substantial equivalence. It does not contain the detailed validation study information, specific acceptance criteria with quantitative metrics, sample sizes, or expert involvement typically found in submissions for diagnostic AI or more complex medical image analysis devices. The "validation and effectiveness" section merely states "extensive testing" was performed by various internal and external groups, without detailing the methodology or results of this testing. The focus is entirely on functional equivalence to existing cleared devices rather than a detailed performance study against a clinical ground truth.

Ask a specific question about this device

PracticeBuilder 1-2-3 is a PACS and teleradiology system used to receive DICOM images, scheduling information and textual reports, organize and store them in an internal format, and to make that information available across a network via web and customized user interfaces.

PracticeBuilder 1-2-3 is for use in hospitals, imaging centers, radiologist reading practices and any user who requires and is granted access to patient image, demographic and report information.

PracticeBuilder 1-2-3 is a PACS system, comprised of acquisition components (GatewayServer and SendServer), a central system manager component (SmartServer), a diagnostic workstation component (Workstation and Viewer), and an archiving component (ArchiveServer). The data flow is such that patient and procedure information is optionally delivered to the central system manager, followed by the acquisition of the image objects directly from the image sources or by one of the acquisition components. After receiving the procedure information or after receiving image objects, the central system manager searches for and retrieves relevant prior procedure data from the archive component. When the central system manager registers the acquired image objects and the retrieved prior procedure data, a user can access the information by selecting the item from the operator worklist. The image data is transmitted to and rendered on the user's workstation using the diagnostic workstation components. After using the workstation to view the images, the user optionally dictates a report into the system, after which, a user can play back the dictation and transcribe it to text. Once PracticeBuilder 1-2-3's central system manager registers a report, the report is available for access by the referring physician, or it can be exported into an information system. At some configured point in time, the image data and the report information is delivered to the archiving component for backup and long-term storage.

PracticeBuilder 1-2-3 is also a teleradiology system used to receive DICOM images, scheduling information and textual reports, organize and store them in an internal format, and to make that information available across a network via web and customized user interfaces.

The provided text is a 510(k) summary for the PracticeBuilder 1-2-3 PACS system. It focuses on demonstrating substantial equivalence to predicate devices and does not contain information about specific acceptance criteria, performance studies with quantitative metrics, or details about ground truth establishment, sample sizes for training/test sets, or expert qualifications for a standalone AI algorithm or MRMC study.

Here's a breakdown of what is and is not available in the provided document, based on your request:

1. Table of Acceptance Criteria and Reported Device Performance:

• Not Available. The document does not specify quantitative acceptance criteria (e.g., sensitivity, specificity, AUC, image quality metrics) or report device performance against such criteria. The "Validation and Effectiveness" section only states that "Extensive testing of the software package has been performed by programmers, by non-programmers, quality control staff, and by potential customers." This is a general statement about testing practices, not a detailed performance study.

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

• Not Available. The document does not mention any specific test set, its sample size, or the provenance of any data used for testing.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Not Available. Ground truth establishment, the number of experts involved, or their qualifications are not mentioned.

4. Adjudication Method:

• Not Available. Since no ground truth establishment is described, no adjudication method is mentioned.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• Not Available. There is no mention of an MRMC study or any comparison of human reader performance with or without AI assistance. This device is a PACS system, not an AI diagnostic aid.

6. Standalone (Algorithm Only) Performance Study:

• Not Available. The device is a PACS system designed for image management and display, not a standalone AI algorithm. Therefore, no standalone performance study in the context of an AI algorithm is described.

7. Type of Ground Truth Used:

• Not Available. Given the nature of the device (PACS), the concept of "ground truth" for a diagnostic algorithm is not applicable here in the way it would be for an AI diagnostic device. The document focuses on the functional equivalence of the PACS system.

8. Sample Size for the Training Set:

• Not Available. This information is not relevant for a PACS system that is not an AI algorithm requiring a training set.

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

• Not Available. Same reasoning as point 8.

In summary:

This 510(k) submission for the PracticeBuilder 1-2-3 PACS system relies on establishing substantial equivalence to existing legally marketed predicate devices (Stentor iSite, Agfa IMPAX, Ultravisual Vortex). The "effectiveness" is primarily demonstrated through the functional equivalence of its components (acquisition, central manager, workstation, archive, teleradiology capabilities) to these predicates. The review focuses on software development practices and general safety and effectiveness concerns rather than specific quantitative performance metrics for a diagnostic algorithm.

Ask a specific question about this device