(59 days)
The Imageshare Protocol Converting Gateway and/or Software is a uni-directional (or optionally, bi-directional) gateway that receives digital images from various image sources (including, but not limited to, CT scanners, MR scanners, Ultrasound systems, R/F units, Computed and Direct Radiography devices, secondary capture devices, imaging gateways, or other imaging sources), The incoming data formats are DICOM, ACR-NEMA v2.0, SPI (Standard Product Interconnect) or proprietary to the modality source vendor. The Imageshare Protocol Converting Gateway converts them into DICOM, ACR-NEMA v2.0, SPI format or proprietary data format and transmits the data to one or more user-specified nodes across a standard, general purpose computing network.
The Imageshare Protocol Converting Gateway is a general purpose computer system running a protocol conversion software application that uses defined configuration to receive, reformat and transmit image and demographic information. The system receives the image messages from a source and routes them automatically them through a conversion to a remote destination based on information contained in both the message source and encoded data. The system requires no user interaction when in operation. The Imageshare Protocol Converting Gateway stores the image data on its local hard disk until the destination application acknowledges the successful transmission. Images that are not delivered to their destination are queued for retransmission until the remote destination confirms receiving the message. This is also true when the Imageshare Protocol Converting Gateway loses power and is restarted.
The Imageshare Protocol Converting Gateway is adaptable to various PACS environments. The configuration can be modified by the system administrator to adapt the Imageshare Protocol Converting Gateway to perform the desired supported conversions.
Here's an analysis of the provided text regarding acceptance criteria and study information:
Based on the provided Device Summary (K963592), it is not possible to complete most sections of your request. This document is a 510(k) summary for a "Protocol Converting Gateway and/or Software" which is a system for converting and transmitting digital images, not a diagnostic or treatment device with performance metrics related to patient outcomes or disease detection.
The document focuses on its technical function of converting digital image protocols (e.g., DICOM, ACR-NEMA) and its substantial equivalence to predicate devices for this specific technical purpose. It does not describe any clinical studies, performance metrics against ground truth, or human reader effectiveness because its function is a technical data conversion and transmission, not a clinical interpretation or diagnostic aid.
Here's what can be extracted and how it relates to your requested information:
1. Table of Acceptance Criteria and Reported Device Performance
It is not possible to generate this table from the provided text.
The document describes the device's technical function, such as:
- Receiving, reformatting, and transmitting image and demographic information.
- Converting between DICOM, ACR-NEMA v2.0, SPI, or proprietary data formats.
- Storing data locally until successful transmission is acknowledged.
- Queuing undelivered images for retransmission.
- Adaptability to various PACS environments via configuration.
- Designed for "maximum portability across operating systems and hardware platforms."
- Performance "primarily a function of network load; secondarily a function of the hardware platform's computational speed."
These are functional and technical specifications, not acceptance criteria with measurable performance metrics in a clinical context (e.g., sensitivity, specificity, accuracy). The "performance" described is about technical throughput and reliability of data transfer, not clinical efficacy.
2. Sample Size Used for the Test Set and Data Provenance
Not applicable and not provided. There is no mention of a "test set" in the context of clinical data because this device is not making clinical interpretations. The "testing" would have been for its technical ability to convert and transmit data, likely using synthetic or existing image files, but this is not detailed.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
Not applicable and not provided. The device does not produce an output that requires expert ground truth. Its function is data conversion and transmission.
4. Adjudication Method for the Test Set
Not applicable and not provided. As there's no clinical test set requiring ground truth, there's no adjudication method.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No, a MRMC study was not done. This type of study is relevant for devices that assist human readers in making clinical decisions (e.g., CAD systems). This gateway device does not provide any diagnostic or interpretative assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Not applicable in the sense of clinical performance. While the device operates "without user interaction when in operation," this refers to its autonomous data conversion and transmission, not standalone performance in a clinical diagnostic context (e.g., an AI algorithm detecting a disease without human input). The entire device functions as a "standalone" technical system.
7. The Type of Ground Truth Used
Not applicable. There is no "ground truth" in the clinical sense (e.g., expert consensus, pathology, outcomes data) as the device's function is technical data handling. The "ground truth" for this device would be whether it accurately converted and transmitted data without corruption, which is a technical validation, not clinical.
8. The Sample Size for the Training Set
Not applicable and not provided. This device is a software application for protocol conversion. It does not utilize machine learning or AI that requires a "training set" of data in the typical sense for learning patterns or making inferences. Its operations are rule-based and configurable.
9. How the Ground Truth for the Training Set Was Established
Not applicable and not provided. As there's no training set for machine learning, there's no ground truth established for it.
§ 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.