(25 days)
iQ-System PACS is a software device intended for viewing of images acquired from CT, MR, CR, DR, US and other DICOM compliant medical imaging systems when installed on suitable commercial standard hardware. Images and data can be captured, stored, communicated, processed, and displayed within the system and or across computer networks at distributed locations. Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary diagnosis or image interpretation. It is the User's responsibility to ensure monitor quality, ambient light conditions, and image compression ratios are consistent with clinical application.
iQ-System PACS is a software device for network or web-based medical image viewing and manipulation, running on Windows 2000/XP. It is adapted for, storing, processing routing and report generating. iQ-System PACS fully supports the DICOM standard and has functionality for advanced DICOM viewing, Hanging Protocol, and 3D image processing (Orthogonal and Oblique Multiplanar Reconstructions (MPR), Maximum Intensity Projections (MIP), Surface Shaded Display (SSD), and Volume Rendering (VRT)). The main iQ-SYSTEM PACS modules are iQ-LITE, iQ-Print, iQ-View (including iQ-3D), and iQ-Web.
The provided text is a 510(k) summary for the iQ-System PACS. It describes the device, its intended use, and its substantial equivalence to a predicate device. However, this document does not contain information about specific acceptance criteria or a study proving the device meets them in the way typically expected for performance-based AI/device evaluations.
This 510(k) is for a Picture Archiving Communications System (PACS) from 2006, which primarily deals with image viewing, manipulation, storage, and communication. At that time, the regulatory landscape and testing methodologies for PACS systems focused more on DICOM compliance, software functionality, and safety aspects rather than quantitative, statistically significant performance metrics like accuracy, sensitivity, or specificity for diagnostic tasks.
Therefore, many of the requested sections (e.g., sample sizes for test/training sets, ground truth establishment, expert qualifications, MRMC studies, standalone performance) are not applicable or detailed in this type of submission.
Here's a breakdown based on the information available in the document:
1. Table of Acceptance Criteria and Reported Device Performance
This document does not specify quantitative acceptance criteria or performance metrics (like accuracy, sensitivity, specificity) for diagnostic tasks. The primary "acceptance criteria" for a PACS system like this, as implied by the 510(k) process, are:
| Acceptance Criteria (Implied from 510(k) focus) | Reported Device Performance (from summary) |
|---|---|
| Substantial Equivalence to Predicate Device | Determined to be substantially equivalent to predicate device (K052358) by FDA. |
| DICOM Standard Support | iQ-System PACS fully supports the DICOM standard. |
| Image Viewing & Manipulation Functionality | Functionality for advanced DICOM viewing, Hanging Protocol, Orthogonal and Oblique Multiplanar Reconstructions (MPR), Maximum Intensity Projections (MIP), Surface Shaded Display (SSD), and Volume Rendering (VRT). |
| Image Capture, Storage, Communication, Processing | Images and data can be captured, stored, communicated, processed, and displayed. |
| Safety - Hazard Analysis | Submission contains results of a hazard analysis; "Level of Concern" classified as "Minor". |
| No Primary Diagnostic Use for Lossy Mammograms | Lossy compressed mammographic images... must not be reviewed for primary diagnosis or image interpretation. (This is a limitation, not a performance claim, but implies an "acceptance criterion" of safe use). |
| Monitor/Light Conditions Responsibility | User's responsibility to ensure monitor quality, ambient light conditions, and image compression ratios are consistent with clinical application. (Another limitation/user responsibility.) |
2. Sample size used for the test set and the data provenance
- Not Applicable / Not Provided. This document does not describe a clinical validation study involving a test set of medical images evaluated for diagnostic accuracy. The testing would have focused on software functionality, DICOM compliance, and safety.
- The document implies general medical imaging data (CT, MR, CR, DR, US) but does not specify a provenance for a test set.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Not Applicable / Not Provided. As no specific diagnostic performance test set is described, there's no mention of experts establishing ground truth for such a set.
4. Adjudication method for the test set
- Not Applicable / Not Provided. No test set or ground truth 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
- No. This device is a PACS viewer, not an AI-assisted diagnostic tool. An MRMC study comparing human readers with and without AI assistance is not described and would not typically be performed for this type of general PACS system.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
- Not Applicable. The iQ-System PACS is a tool for human interpretation and management of images, not a standalone diagnostic algorithm. No standalone performance study is described.
7. The type of ground truth used
- Not Applicable / Not Provided. For a PACS system, ground truth in the diagnostic sense is not its direct function. Its "ground truth" relates to its ability to accurately display the source DICOM data. Compliance with DICOM standards and verification of image integrity are implicitly tested, but not framed as "ground truth" for diagnostic outcomes.
8. The sample size for the training set
- Not Applicable / Not Provided. This device is a software system for image management and viewing, not a machine learning or AI algorithm that requires a "training set" in the context of data-driven learning. Its development would involve software engineering and testing, not statistical training on medical images.
9. How the ground truth for the training set was established
- Not Applicable / Not Provided. As it's not an AI/ML device, there's no concept of a training set or its associated ground truth.
§ 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).