(41 days)
Synergy is a comprehensive software platform intended for use in acquisition or importing, processing, measurement, analysis and storage of clinical images and videos of the eye as well as in management of patient data, diagnostic data, clinical information, reports from ophthalmic diagnostic instruments through either a direct connection with the instruments or through computerized networks.
Synergy is a software platform that collects, processes, measures, analyzes, stores, and manages patient data and clinical information. Synergy is used together with a number of computerized digital imaging devices. In addition, Synergy software collects and manages patient demographics, image data, and clinical reports from a range of approved medical devices. Synergy enables a real-time review of diagnostic patient information at a PC workstation. In addition to the desktop application, Synergy also includes an internet-browser-based user interface to allow authorized users to access, view, create reports, and analyze patient and examination data saved in a centralized database. The system utilizes dual level authentication and 128-bit encryption to ensure secure networking environment.
This 510(k) summary provides information for the Topcon Medical Systems, Inc. Synergy, an ophthalmic image management system.
Here's a breakdown of the requested information based on the provided text:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified | No performance data was required or provided. Software validation and verification demonstrate that the Synergy performs as intended and meets its specifications. |
2. Sample size used for the test set and the data provenance
The document explicitly states: "No performance data was required or provided." Therefore, there is no test set sample size and no data provenance mentioned for a clinical performance study. The evaluation focused on software validation and verification.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
As no performance data was provided, there was no test set and therefore no experts used to establish ground truth for a clinical performance evaluation.
4. Adjudication method for the test set
Not applicable, as no performance study with a test set was conducted.
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 MRMC comparative effectiveness study was done. The device description indicates it is a software platform for image management and analysis, not an AI-assisted diagnostic tool that would typically involve human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
No standalone performance study was done in the context of clinical accuracy or diagnostic capability, as explicitly stated: "No performance data was required or provided." The "standalone" aspect described is the software itself performing its intended functions (acquisition, processing, measurement, analysis, storage, management) rather than a diagnostic algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Since no performance data or clinical study was conducted, no ground truth was established or used in the context of diagnostic accuracy. The "ground truth" for the device's functionality would have been its own specifications, verified through software validation and verification.
8. The sample size for the training set
Not applicable, as this device is an image management system and not an AI/ML diagnostic algorithm that typically requires a training set for model development.
9. How the ground truth for the training set was established
Not applicable, as no training set was used for an AI/ML model.
§ 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).