(59 days)
The Imageshare Secondary Capture Acquisition Station and/or Software is a uni-directional gateway that receives digital or analog images from various image sources (including, but not limited to, film digitizers, video signal outputs or print output from imaging modalities, or other imaging sources). The incoming data formats are bitmapped, raster images or proprietary to the modality source vendor. The Imageshare Secondary Capture Acquisition Station and/or Software converts the data to DICOM, ACR-NEMA v2.0, SPI format or other proprietary data format and transmits the data to one or more user-specified nodes across a standard, general purpose computing network.
The Imageshare Secondary Capture Acquisition Station is a general purpose computer system running an image acquisition software application to receive, reformat and transmit image and demographic information. The system receives the image messages from a source (film digitizer, video source, ultrasound, print output, etc.) and routes them automatically through a conversion to a destination based on information contained in the message source, encoded data and data entered by an operator through a user interface. The Imageshare Secondary Capture Acquisition Station stores the data on its local hard disk until the destination application acknowledges the successful transmission. Images not delivered to their destination are queved for retransmission until the remote destination confirms receipt of the message. Image delivery is also guaranteed when the Imageshare Secondary Capture Acquisition Station loses power and is restarted.
The Imageshare Secondary Capture Acquisition Station is adaptable to various PACS environments. The configuration can be modified by the system administrator to adapt the Imageshare Secondary Capture Acquisition Station to perform functions specific to the site.
Here's an analysis of the provided text regarding acceptance criteria and supporting studies:
Based on the provided 510(k) summary (K963594), there is no information provided about acceptance criteria or a study proving the device meets specific performance metrics.
The document primarily focuses on:
- Device Identification: Trade name, common name, classification name.
- Predicate Devices: A list of similar devices already on the market.
- Device Description: Functionality (receiving, reformatting, transmitting image and demographic information, handling retransmission, adaptability to PACS).
- Physical and Performance Characteristics: General statements about running on off-the-shelf equipment, portability, performance primarily affected by network load.
- Intended Use: As a uni-directional gateway to convert and transmit digital or analog images in various formats.
- Statement of Substantial Equivalence: Claiming substantial equivalence to predicate devices without significant influence on safety or efficacy.
Therefore, it's not possible to populate the requested table or answer the specific questions about acceptance criteria and studies. The summary is typical for a 510(k) submission seeking equivalence to existing devices, where detailed performance studies might not be explicitly required if substantial equivalence can be demonstrated through design, function, and intended use.
If this were a more modern AI/ML device submission, a 510(k) would typically include detailed performance data, acceptance criteria, and study methodologies. However, this document from 1996 for an "Imageshare Secondary Capture Acquisition Station and/or Software" is focused on general image handling and transmission, not specific diagnostic AI performance.
In summary, for the information requested:
- A table of acceptance criteria and the reported device performance: Not provided in the document.
- Sample size used for the test set and the data provenance: Not provided in the document.
- Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided in the document.
- Adjudication method for the test set: Not provided in the document.
- 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 provided in the document. (This type of study is typically for diagnostic AI, which this device is not presented as).
- If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not provided in the document.
- The type of ground truth used: Not applicable/Provided in the document. (The device is for image capture, conversion, and transmission, not for making diagnostic interpretations requiring ground truth).
- The sample size for the training set: Not provided in the document.
- How the ground truth for the training set was established: Not applicable/Provided in the document.
§ 892.2020 Medical image communications device.
(a)
Identification. A medical image communications device provides electronic transfer of medical image data between medical devices. It may include a physical communications medium, modems, and interfaces. It may provide simple image review software functionality for medical image processing and manipulation, such as grayscale window and level, zoom and pan, user delineated geometric measurements, compression, or user added image annotations. The device does not perform advanced image processing or complex quantitative functions. This does not include electronic transfer of medical image software functions.(b)
Classification. Class I (general controls). The device is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to § 892.9.