(64 days)
ImageQube is intended for use by a physician or other medical professionals in the acquisition of medical images and demographic detail from all institutional imaging modalities, including CT, MRI, NM, DR, US, nuclear medicine, Angio and secondary capture devices such as film digitizers or other imaging sources. The ImageQube Web system is designed for acquisition, storage, and distribution of all modalities. Device is also designed for primary interpretation of all modalities except manmography. Device is not to be used for primary imaging diagnosis in mammography and will be conspicuously labeled as such during display of mammography images. The acquired medical images and demographic information may be displayed, processed, reviewed optionally utilizing Rational Imaging PACS Multi-planar Reconstruction (MPR), Anatomic Triangulation (AT) and 3D display, sent to and retrieved by radiologists at remote sites, stored, archived or printed.
ImageQube is designed for use by a physician or other medical professionals in the acquisition of medical images and demographic detail from all institutional imaging modalities, including CT, CR, MRI, NM, DR, US, Angio, nuclear medicine, and secondary capture devices such as film digitizers or other imaging sources.. The acquired medical images and demographic information may be displayed, processed, reviewed, sent to and retrieved by radiologists at remote sites, stored, archived or printed. Multi-planar Reconstruction (MPR). Anatomic Triangulation (AT) and 3D display are optionally available.
The provided text describes the 510(k) summary for the ImageQube device, which is an Image Processing system (PACS). The submission focuses on demonstrating substantial equivalence to a predicate device, rather than providing a detailed study of its performance against specific acceptance criteria. Therefore, several of the requested sections cannot be fully populated from the provided document.
Here's a breakdown of what can be extracted and what cannot:
1. A table of acceptance criteria and the reported device performance
The document does not specify quantitative acceptance criteria or report device performance in terms of metrics like sensitivity, specificity, accuracy, or other benchmarked performance indicators. The comparison is feature-based against a predicate device.
| Feature | Acceptance Criteria (Implied) | Reported ImageQube Performance |
|---|---|---|
| Multimedia Enterprise Distribution of images and data via Internet or Intranet | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| Automatically receive DICOM images from any Imaging Acquisition Device | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| Inter-vendor communication (Receive RIS from HL7 compliant systems) | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| DICOM compliance | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| IHE compliance | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| Image Server API | Functional API | IQViewer (Equivalent to LightView) |
| Secure Web Based Administration | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| Maximum Intensity Projection (MIP) | Not present in ImageQube | N (Predicate has it) |
| Cross Sectional Viewing | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| Plain Film Studies | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| Individual User Templates | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| Image review and manipulation tools | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| Image Measurement tools | Equivalence to predicate | Y (Equivalent to Amicas Light Beam) |
| Transmission | Functional transmission | Lurawave® (Predicate uses JPEG2000) |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not provided. The document describes a feature-based comparison for substantial equivalence, not a performance study using a test set of medical images.
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 applicable. No ground truth establishment for a test set is described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. No test set or adjudication method is described.
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
Not applicable. The document describes a PACS system, not an AI-assisted diagnostic tool, and no MRMC study is mentioned.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. The ImageQube is a PACS system for image management and display; it's not a standalone diagnostic algorithm. The "standalone" performance in this context would refer to its ability to perform its core functions (acquire, process, archive, distribute images) which is implicitly covered by the "Substantial Equivalence" claim.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Not applicable. No ground truth is established as this is a comparison of technical features and functionality of a PACS system.
8. The sample size for the training set
Not applicable. The device is a PACS system that processes and displays images; it is not an AI/ML model that requires a training set in the conventional sense.
9. How the ground truth for the training set was established
Not applicable. See point 8.
§ 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).