(70 days)
The SharpView Image Enhancement System is intended for use by a qualified/trained technologist for transfer, storage, enhancement, and viewing of MRI images.
The product is a kit containing software and hardware (PCI graphics processor board) which is intended to be installed on a personal computer. Typically the personal computer receives MRI images in DICOM 3 format over a network connection. The received image is stored locally, then accessed, then enhanced by the software/hardware combination, then stored in the enhanced format. The original file can still be accessed and is not modified.
The provided text is a 510(k) summary for the SharpView Image Enhancement System. It outlines the device's identification, intended use, description, and a comparison to a predicate device to establish substantial equivalence. However, it does not contain detailed information about specific acceptance criteria, a formal study proving the device meets those criteria, or quantitative performance metrics.
Here's an analysis based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
The document states: "The results of bench and user testing indicates that the new device is as safe and effective as the predicate devices." and "After analyzing both bench and user testing data, it is the conclusion of Contextvision AB SharpView Image Enhancement System is as safe and effective as the predicate device, has few technological differences, and has no new indications for use, thus rendering it substantially equivalent to the predicate device."
| Acceptance Criteria (Inferred from Substantial Equivalence) | Reported Device Performance | Comments |
|---|---|---|
| As safe as the predicate device (IES Image Enhancement System, K922470) | Bench and user testing indicates the device is as safe as the predicate. | No specific safety metrics or criteria are provided. The claim is qualitative and based on the substantial equivalence argument. |
| As effective as the predicate device (IES Image Enhancement System, K922470) | Bench and user testing indicates the device is as effective as the predicate. | No specific effectiveness metrics or criteria are provided. The claim is qualitative and based on the substantial equivalence argument. |
| Few technological differences compared to the predicate device | As outlined in the "Substantial Equivalence Chart," differences include: | The differences are primarily in the hardware platform (PC compatible vs. Sun computer), operating system (Windows 98, NT 4.0 vs. Unix), storage methods, and image input (DICOM 3 vs. direct from MRI console). The software core "GOP® Enhancement software" is stated as "SAME." |
| No new indications for use | The intended use is "SAME" as the predicate device: "for use by a qualified/trained technologist for transfer, storage, enhancement, and viewing of MRI images." | This criterion is met. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
The document mentions "bench and user testing data" but does not provide any details regarding the sample size of the test set, the data provenance (country of origin), or whether the data was retrospective or prospective.
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):
The document refers to "user testing" but does not specify the number of experts used, their qualifications, or how ground truth was established for any test set.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
The document does not describe any adjudication method used for a test set.
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:
The document does not mention any multi-reader multi-case (MRMC) comparative effectiveness study, nor does it discuss human reader improvement with or without AI assistance. The device is an image enhancement system, implying it modifies images, but it's not positioned as an AI-assisted diagnostic tool in the text.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
The document describes the "SharpView Image Enhancement System" as a standalone software and hardware kit for image enhancement. The "bench testing" mentioned likely refers to standalone performance, but no specific details on standalone algorithm-only performance metrics or studies are provided. The focus is on substantial equivalence to a predicate image enhancement system.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
The document does not specify the type of ground truth used for any testing. Given the nature of an "image enhancement system" and the lack of diagnostic claims, ground truth in the traditional sense (e.g., pathology for disease detection) might not have been directly applied in the same way as for a diagnostic AI. Instead, subjective evaluation of image quality or objective measures of enhancement properties might have been used, but these are not disclosed.
8. The sample size for the training set:
The document does not mention any training set size as this is an older 510(k) and the device description does not explicitly state it uses machine learning/AI models that would require a distinct training set. The "GOP® Enhancement software" is a core component, which might be a rule-based or algorithmic image processing technique rather than a data-trained AI model in the modern sense.
9. How the ground truth for the training set was established:
Since no training set is mentioned or implied for a machine learning model, there is no information on how its ground truth would have been established.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).