(83 days)
The Imation™ MODEL 9410 NETWORK INTERFACE Network Interface is intended for use as a communications gateway. It accepts input from imaging source modalities and transfers the image data to a compatible printing, viewing, archive or network system. The MODEL 9410 NETWORK INTERFACE accepts input in DICOM standard, digital or video formats and converts, if needed, to DICOM Standard or other digital formats. The system is intended for use with a variety of imaging modalities including CT, MR and CR for the transmission of image data to a variety of printing, viewing and storage devices.
The Imation™ MODEL 9410 NETWORK INTERFACE Network Interface is intended for use as a communications gateway. It accepts input from imaging source modalities and transfers the image data to a compatible printing, viewing, archive or network system. The MODEL 9410 NETWORK INTERFACE accepts input in DICOM standard, digital or video formats and converts, if needed, to DICOM Standard or other digital formats. The system is intended for use with a variety of imaging modalities including CT, MR and CR for the transmission of image data to a variety of printing, viewing and storage devices.
The provided 510(k) summary for the Imation™ MODEL 9410 NETWORK INTERFACE does not contain detailed information regarding specific acceptance criteria or a dedicated study explicitly designed to prove the device meets such criteria in terms of image quality or clinical performance.
Instead, the submission relies on the following points to establish safety and effectiveness, and thus 'acceptance':
1. Acceptance Criteria and Device Performance (Inferred):
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Safety: Compliance with voluntary safety standards. (Specifically mentioned: UL950, CSA C22.2 No. 950) | The device adheres to UL950 and CSA C22.2 No. 950. The device has no patient contact and does not control, monitor, or affect any devices directly connected to or affecting the patient. Released software undergoes testing, hazard analysis, and approval equivalent to the initial release. |
| Effectiveness (Image Quality Preservation): Maintain or improve image properties (spatial resolution, gray-scale resolution, density uniformity) compared to predicate devices. | "Images communicated by the subject device maintain the same or better image properties in the areas of spatial and gray-scale resolution and in density uniformity as the predicate. No lossy compression is used in this device." |
| Functional Equivalence: Performs as a communications gateway, accepting and transferring image data in specified formats. | The device "accepts input from imaging source modalities and transfers the image data to a compatible printing, viewing, archive or network system." It accepts "DICOM standard, digital or video formats and converts, if needed, to DICOM Standard or other digital formats." |
| Substantial Equivalence: Demonstrated similarity in technical characteristics and intended use to predicate devices (Cemax-Icon Scanlink V and Merge MVP). | The submission asserts that "The subject device and predicate devices use the same technical design base." and "The subject and predicate device(s) have all been designed to equivalent safety standards." |
2. Sample Size Used for the Test Set and Data Provenance:
The document does not specify a distinct "test set" in the context of image quality performance or clinical evaluation. The statement "Images communicated by the subject device maintain the same or better image properties..." implies an internal assessment, but details about the number of images, their origin, or whether this was a retrospective or prospective collection are not provided.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
Not applicable. The submission does not describe a study involving expert readers to establish ground truth for image interpretation or diagnosis. The phrase "Images communicated by the subject device and its predicates are reviewed by medical personnel, offering ample opportunity for competent human intervention in case of a malfunction or other failure" suggests that medical personnel ultimately review images, but this is not a ground truth establishment process for a test set.
4. Adjudication Method for the Test Set:
Not applicable. No test set adjudication is described.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
No MRMC study was conducted or reported. The submission focuses on device functionality and equivalence, not on the impact of the device on human reader performance.
6. Standalone (Algorithm Only) Performance:
The device is a network interface, a hardware/software system for data transfer and conversion. It does not perform diagnostic algorithms in the sense of an AI model that interprets images. Therefore, a standalone (algorithm only) performance study as typically understood for AI-powered diagnostic devices is not applicable. Its performance is based on its ability to accurately transmit and convert data without degradation.
7. Type of Ground Truth Used:
The concept of "ground truth" as typically used in the context of diagnostic AI algorithms (e.g., pathology, clinical outcomes) is not applicable here. The evaluation of this device is based on technical specifications and faithful transmission/conversion of image data, not on the accuracy of diagnostic findings. The implied 'ground truth' for its performance relates to the integrity of the transmitted image data compared to the original, which would be evaluated through technical measurements rather than clinical expert consensus.
8. Sample Size for the Training Set:
Not applicable. This device is a network interface and does not involve AI/machine learning models that require a "training set" in the typical sense for image interpretation. Its software converts data formats and manages communication, which is developed through standard software engineering practices rather than data-driven machine learning.
9. How the Ground Truth for the Training Set was Established:
Not applicable, as there is no training set for an AI algorithm.
§ 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.