(57 days)
CARESTREAM Image Suite is an image management system whose intended use is to receive, process, review, display, print and archive images and data from all imaging modalities. This excludes mammography applications in the United States.
CARESTREAM Image Suite is a stand-alone, self-contained radiographic imaging system designed to provide a low-cost platform to manage medical images, reports, patient/exam information and workflow in small clinics. The system performs capture, processing, review, archiving, and printing of radiographic images as well as report writing and printing and is designed to run on a PC workstation. The CARESTREAM Image Suite is designed to be simple and intuitive to both use and service.
CARESTREAM Image Suite is designed as a hardware-independent system and may be interfaced with verified and validated image acquisition devices from both Carestream Health and 3rd party vendors, Carestream Health PACS system, and other 3rd party PACS systems. The system will acquire an image from either a DirectView Classic CR or a Point-of-Care 140/145 or 360 CR and be PC and monitor independent.
The provided text is a 510(k) summary for the CARESTREAM Image Suite and does not contain the specific details required to complete all sections of your request. While it mentions "Performance testing was conducted to verify the design output met the design input requirements and to validate the device conformed to the defined user needs and intended uses," it does not provide the concrete acceptance criteria (e.g., specific metrics like accuracy, sensitivity, specificity, or image quality parameters), nor does it detail the study design, sample sizes, ground truth establishment, or expert qualifications in the way you've requested for a device that detects or diagnoses conditions.
This document describes an image management system (PACS-like functionality) rather than a device that makes a diagnostic assessment itself. Therefore, the types of performance metrics and studies you're asking for (e.g., MRMC studies, standalone performance with sensitivity/specificity, expert ground truth for diagnosis) are typically not applicable to a device of this nature. The testing would focus on system functionality, image integrity, data storage, display accuracy, and adherence to industry standards (like DICOM).
However, based on the general description, I can infer some points and explicitly state where information is missing:
Here's an attempt to answer your questions based on the provided text, highlighting where information is absent:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Inferred from text) | Reported Device Performance |
|---|---|
| Functional Equivalence to Predicates: The device should perform core functions (receive, process, review, display, print, archive) similarly to predicate devices. | "Predefined acceptance criteria was met and demonstrated that the device is as safe and as effective as the predicate devices." (Specific performance metrics are not provided.) |
| Image Processing Integrity: Image processing modules should operate without degradation or modification from the established KODAK Eclipse Image Processing Software. | "The design is based on current Carestream technology with the image processing modules using the existing KODAK Eclipse Image Processing Software product without any modifications to the software responsible for image processing." (Implies this was met.) |
| Compatibility: Interface with verified and validated image acquisition devices (CR and DR), Carestream Health PACS, and other 3rd party PACS systems. | "May be interfaced with verified and validated image acquisition devices from both Carestream Health and 3rd party vendors, Carestream Health PACS system, and other 3rd party PACS systems." (Implies this was met.) |
| Workflow Management: Manage medical images, reports, patient/exam information, and workflow in small clinics. | "The system performs capture, processing, review, archiving, and printing of radiographic images as well as report writing and printing." (Implies this was met.) |
| User Needs & Intended Uses: Conformance to defined user needs and intended uses (receiving, processing, reviewing, displaying, printing, and archiving images and data from CR and DR modalities, excluding mammography). | "Performance testing was conducted to verify the design output met the design input requirements and to validate the device conformed to the defined user needs and intended uses." (Implies this was met.) |
| Safety & Effectiveness: Demonstrated to be as safe and as effective as the predicate devices. | "Demonstrated that the device is as safe and as effective as the predicate devices." (Specific safety and effectiveness metrics are not detailed, as this refers more to overall system compliance rather than diagnostic performance.) |
2. Sample Size Used for the Test Set and the Data Provenance
- Test Set Sample Size: Not specified. The document only states "Performance testing was conducted" and "Nonclinical testing was conducted under simulated use conditions." It does not mention a specific 'test set' in the context of diagnostic performance evaluation.
- Data Provenance: Not specified. Given the nature of an image management system, the "data" would likely be images acquired from various CR/DR modalities. No specific country of origin or whether it was retrospective/prospective is mentioned.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts
- Not Applicable in the traditional sense for this device. As an image management system, its "ground truth" relates to its functional performance (e.g., does it display images correctly? does it store them reliably? does image processing apply as intended?). It's not evaluating a diagnostic outcome that would require expert consensus on medical findings. There is no mention of experts establishing ground truth for diagnostic accuracy.
4. Adjudication Method for the Test Set
- Not Applicable. Since the product is an image management system and not a diagnostic AI, there is no adjudication method described in the context of diagnostic findings. Adjudication methods like 2+1 or 3+1 are used to resolve disagreements among human readers on clinical cases, which is not the primary function of this device's evaluation documentation.
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. An MRMC comparative effectiveness study is not mentioned and would not be relevant for an image management system. Such studies apply to AI algorithms designed to assist in diagnosis or detection.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not Applicable in the context of diagnostic performance. The device itself is standalone in its function as an image management system. However, it doesn't perform diagnostic "algorithms" that would have a standalone diagnostic performance measured in metrics like sensitivity or specificity.
7. The Type of Ground Truth Used
- Functional/Technical Validation: The "ground truth" for this device would be its adherence to technical specifications, proper image display, integrity of stored data, correct application of image processing parameters (as per the KODAK Eclipse software), and successful data transfer/interoperability. This would be verified through engineering and software testing rather than expert medical consensus on a diagnosis.
8. The Sample Size for the Training Set
- Not Applicable in the AI/machine learning sense. This device is described as using "existing KODAK Eclipse Image Processing Software product without any modifications to the software responsible for image processing" and is based on "current Carestream technology." This indicates a software product built on established algorithms and engineering principles, not a machine learning model that requires a "training set" of labeled data.
9. How the Ground Truth for the Training Set was Established
- Not Applicable. As there's no mention of a machine learning training set, this question is not relevant to the provided 510(k) summary.
§ 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).