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510(k) Data Aggregation
(20 days)
REGIUS RS-1000, MEDICAL IMAGE PROCESSING WORKSTATION
Receive and process electronic images of patients. The REGIUS RS-1000 is NOT intended for use with digital mammography system.
REGIUS RS-1000 is Konica Minolta Image Quality Control Terminal. REGIUS RS-1000 has the hard disk for storing the digital images. REGIUS RS-1000 consists of a workstation computer with keyboard and mouse for input, LAN interface for communication, color or monochrome CRT or LCD for displaying. REGIUS RS-1000 processes the images received from a single or multiple CR (Computed Radiography) devices with the auto gradation processing function, etc. and outputs them to one or multiples storage devices, such as the host computer or a film printer.
Note) This product is designed intended for exclusive use with a radiographer, and not for the purpose of doctor's diagnosis.
The REGIUS RS-1000 has the following feature.
- The function to do an image processing to the image data received from the CR modalities.
The kinds of the image processing are as follows:
- Adjusting the Contrast:
Achieve a clearly depicted image (with clear minimum density). - Re-sampling and Resizing
The function that re samples and resizes the image data according to need. - F-processing:
Improve dill or poorly modulated (lacking in contrast) images (to give definition to details). These process doses not affect density. - E-processing:
To improve the image that is not possible to be fully expressed by the film latitude due to the wide distribution of the subject. - H-processing
Hybrid processing is frequency enhancement processing and equalization processing based on multi-resolution analysis. - Masking
Fills in black the area on the frame where the X ray is not irradiated. - Stitching
This function that manually or automatically recognizes the long body part and assists the user to stitch each body part to create a composite image. The user checks all alignments done automatically. The image can be divided into several small images before output to a storage device or film printer. Note) This feature requires cassette type CR in addition connected.
- The function that displays all information attached with the medical image, such as patient related information, study information, and so on. The ability to confirm, modify or update this information manfully or by connecting to RIS (Radiology Information System) or HIS (Hospital Information System) is also provided.
- Images belong to the same study can be separated into different studies when necessary, and the different studies also be combined into a single study.
- The function to add or modify digital marker, grid, scale, and so on.
- The function that stores images data temporary before transferring them any further. Oldest images will be erased automatically to make sure of the hard disk capacity for continuously operation.
- The function that outputs the image data to a storage device, such as PACS (Picture Archiving and Communication System), and a film printer.
- The function that retrieves past image data from a storage device. The retrieved image data may be re-processed then output to a film printer and so on.
Here's an analysis of the provided text regarding the KUS 1521 (REGIUS RS-1000) device, focusing on acceptance criteria and supporting studies.
Important Note: The provided document is a 510(k) summary, which is a premarket notification to the FDA. These summaries typically focus on demonstrating substantial equivalence to a legally marketed predicate device rather than detailing extensive clinical studies to establish novel performance metrics or human-in-the-loop improvements. As such, many of the requested details about specific acceptance criteria and study designs are not present in this type of document.
KUS 1521 (REGIUS RS-1000) - Acceptance Criteria and Study Details
1. Table of Acceptance Criteria and Reported Device Performance
Given that this is a 510(k) summary focused on substantial equivalence, explicit, quantified acceptance criteria and detailed performance metrics are not provided in the document. The device's performance is implicitly evaluated against the predicate device's capabilities, particularly regarding image processing functions, in terms of functionality and enhancement rather than specific quantitative metrics (e.g., sensitivity, specificity).
The comparison table highlights features, implying that the REGIUS RS-1000 is equivalent to or enhances upon the predicate device's capabilities.
Feature / Acceptance Criteria (Implicit) | Reported Device Performance (as per comparison table) |
---|---|
Hardware Equivalence: | Equivalent or improved (e.g., more RAM, larger display option) |
- CPU / Bus / RAM / Hard Drive | Pentium 4, PCI, 1024MB RAM, 40GB HDD (improved RAM) |
- Floppy / CD-ROM / Keyboard / Mouse | Present |
- Operating System | Microsoft Windows 2000 or XP |
- Ethernet Capability | Yes: LAN |
- Image Transfer | via DICOM 3.0 & proprietary protocol |
- Image Display | 16" color 1MP LCD (predicate: 19" 2MP) |
- Connects to Image Recorders | Yes |
Software Functionality Equivalence (Image Processing): | Equivalent or offers additional features/ enhancements |
- F-processing (Image Spatial Frequency) | YES (similar to Predicate's Gradation/Edge enhancement) |
- E-processing (Dynamic Range Conversion) | YES (similar to Predicate's DRC) |
- H-processing (Multi-resolution Analysis) | YES (similar to Predicate's MFP) |
- Masking | YES (Additional feature compared to predicate) |
- Re-sampling and Resizing | YES (Additional feature compared to predicate) |
- Stitching | YES (Additional feature compared to predicate) |
2. Sample Size Used for the Test Set and Data Provenance
Not explicitly stated. As a 510(k) for a PACS workstation with image processing capabilities, the submission focuses on functionality and safety, demonstrating equivalence through a comparison of features and capabilities rather than a clinical performance study with a 'test set' in the traditional sense of medical image analysis AI. There is no mention of a specific "test set" of patient images used to evaluate its performance against diagnostic criteria.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
Not applicable/Not stated. Given the nature of a PACS workstation (which manages and processes images, without making diagnostic claims itself), clinical ground truth studies like those for diagnostic AI are not typically part of its 510(k) submission. The document defines its intended use as "Receive and process electronic images of patients" and explicitly states, "This product is designed intended for exclusive use with a radiographer, and not for the purpose of doctor's diagnosis."
4. Adjudication Method for the Test Set
Not applicable/Not stated. No clinical test set or adjudication method is described.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No. There is no mention of an MRMC study. The device is a workstation for processing and managing images, not a diagnostic aid intended to improve human reader performance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
No. While the device contains algorithms for image processing (F-processing, E-processing, H-processing, Masking, Re-sampling, Resizing, Stitching), its regulatory pathway as a PACS workstation does not require standalone diagnostic performance studies. Its functions are assistive for radiographers in preparing images for review, not for automated diagnosis. The document explicitly states it's "not for the purpose of doctor's diagnosis."
7. The Type of Ground Truth Used
Not applicable/Not stated. For a PACS workstation, the "ground truth" relates more to the faithful and accurate execution of its processing functions and adherence to DICOM standards, rather than diagnostic outcomes. Compliance with DICOM and safety standards (UL, IEC/CISPR) is mentioned.
8. The Sample Size for the Training Set
Not applicable/Not stated. As this device is a workstation with pre-defined image processing algorithms, it is not a machine learning or AI algorithm that undergoes statistical "training" on a specific dataset in the typical sense. The algorithms are likely deterministic or rule-based, designed by engineers.
9. How the Ground Truth for the Training Set Was Established
Not applicable/Not stated. (See point 8).
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