(83 days)
The Strykerware™ PACS System is a system designed to provide licensed medical professionals access to medical images. This product is intended to be used for reviewing and storing images from a variety of modalities. Strykerware™ is the system with software installed on a computer configured with connections to image acquisition devices, image storage locations and the internet. The system facilitates the transferal of images between local and remote locations (including but not limited to a licensed medical professional's operating suite).
The Stryker Strykerware™ PACS System is a medical system that is designed to allow access to medical images and related data for physicians and other licensed professionals. The system receives images and medical data from image acquisition devices or other PACS (Picture Archive and Communication System) networks. The system's software includes an integrated web-based viewing and storage archival applications. The Strykerware™ System allows for the transfer of medical images and data between locations such as but not limited to a physicians operating suite.
This 510(k) summary document for the Strykerware™ PACS System does not contain the specific information required to complete all sections of your request regarding acceptance criteria and study details. The document is primarily a declaration of substantial equivalence to predicate devices and outlines the device's intended use and regulatory compliance.
Here's what can be extracted and what is missing:
1. A table of acceptance criteria and the reported device performance
- This information is not provided in the document. The document states that the device is "substantially equivalent in safety and efficacy as the currently marketed Remotelmage™ System and AMICAS Web/Intranet Image Server." This implies that the acceptance criteria are met by virtue of being comparable to these predicate devices, but no explicit performance metrics or criteria are listed for the Strykerware™ system itself.
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
- This information is not provided. As this is a 510(k) for a PACS system, not a diagnostic AI device, there isn't typically a "test set" in the sense of clinical images for diagnostic accuracy evaluation. The evaluation would have been focused on functionality, connectivity, and image integrity, which are not detailed here.
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)
- This information is not provided. This type of detail is typically associated with clinical performance studies for diagnostic algorithms, which is not the primary focus of a PACS system's 510(k).
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
- This information is not provided. This is not relevant for a PACS system's 510(k).
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
- This information is not provided. An MRMC study is relevant for evaluating the impact of AI on human reader performance, which is not applicable to a PACS system like the Strykerware™.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
- This information is not provided. This question is typically for AI algorithms that perform a diagnostic or analytical function. The Strykerware™ is an image management and viewing system, not a standalone diagnostic algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- This information is not provided. As explained above, for a PACS system, the "ground truth" would relate to the accuracy of image storage, retrieval, display, and transfer, rather than diagnostic accuracy. These functional tests are typically performed during product development and verification but are not usually detailed in a 510(k) summary in this manner.
8. The sample size for the training set
- This information is not provided. The Strykerware™ PACS system is not an AI/ML diagnostic algorithm that would have a "training set" of medical images in the traditional sense. Its development would involve software engineering principles, testing against functional requirements, and adherence to standards like DICOM.
9. How the ground truth for the training set was established
- This information is not provided. Similar to point 8, this question is not applicable to the type of device described.
Summary of what can be inferred or explicitly stated from the document:
- Device Name: Strykerware™ PACS System (formerly Strykerware™ Office Portal/Media Archive)
- Classification Name: System, Image Processing (21 CFR 892.2050)
- Predicate Devices: Remotelmage™ System (K994228) and AMICAS Web/Intranet Image Server (K970064).
- Acceptance Criteria (Implied): The device meets acceptance criteria by demonstrating "substantial equivalence in safety and efficacy" to the predicate devices. The specific performance metrics or thresholds for this equivalence are not listed.
- Study Proving Acceptance Criteria: The document mentions a "510(k) summary and effectiveness" but does not detail a specific study or test results. The primary "proof" is the declaration of substantial equivalence based on technological similarities and adherence to voluntary standards (ACR/NEMA DICOM, ANSI/AAMI SW68, IEC 60601). The lack of "new issues of safety and efficacy" compared to predicates is the core argument.
- Device Performance Reported: The document does not report specific performance metrics for the Strykerware™ system. It rather asserts its performance is substantially equivalent to the predicate devices. The function is described as allowing access, viewing, storage, and transfer of medical images and data.
§ 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).