(41 days)
The Medical Insight Easyl/iz™ PACS System is a Diagnostic Softcopy Reading software package to be used for primary diagnosis and clinical review of digital radiology images.
The EasyViz(tm) system is designed as a diagnostic reading workstation system, which will be packaged with standard PC hardware and software, and is intended for use by radiologists and referring physicians.
EasyViz(tm) is capable of receiving and displaying DICOM images.
Images sent to EasyViz(tm) are converted into formats suitable for viewing in its framework, and are temporarily stored in a local cache (memory). The algorithms used to create JPEG and Wavelet compressed images are based on standard and accepted protocols.
Images sent to EasyViz(tm) can be viewed using an executable program on a standard PC, laptop, newspaper or other devices equipped with the appropriate hardware.
EasyViz(tm) uses standard "off-the-shelf" PC hardware and communicates using the standard 100 Mbit Ethernet TCP/IP stack. The network stack is superfluous to EasyViz(tm).
The provided FDA 510(k) summary for the EasyViz software (K051809) does not contain explicit acceptance criteria or a dedicated study demonstrating the device meets performance criteria.
Instead, the submission focuses on establishing substantial equivalence to predicate devices. This means that instead of presenting a new performance study with acceptance criteria, the manufacturer asserts that their device performs similarly to devices already cleared by the FDA.
Therefore, many of the requested details about acceptance criteria and study methodology are not available in this document.
Here's an analysis based on the available information:
1. Table of acceptance criteria and the reported device performance
No explicit acceptance criteria or reported device performance metrics are provided in the document. The substantial equivalence argument relies on functional similarities rather than quantifiable performance thresholds.
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
Not applicable. No specific test set data or provenance is mentioned for a standalone performance study. The "extensive testing of the software and hardware" mentioned in Section 1.8 appears to refer to internal testing for functionality and safety, not a formal clinical performance study with a test set.
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)
Not applicable. No ground truth establishment is described for a performance study.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
Not applicable. No 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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done or reported in this document. The EasyViz is a PACS soft-copy reading system, primarily for displaying images and providing tools, not necessarily an AI-powered diagnostic algorithm that assists human readers in a measurable way with an effect size.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
The EasyViz is described as a "diagnostic reading workstation system" and "Diagnostic Softcopy Reading software package," meaning it is intended for use by a human in the loop for primary diagnosis and clinical review. It is not an algorithm designed for standalone diagnostic performance. Therefore, a standalone performance study as typically understood for AI algorithms would not be applicable or reported here.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable. No ground truth is discussed in the context of a performance study for the EasyViz system.
8. The sample size for the training set
Not applicable. The EasyViz is described as a PACS system for displaying and manipulating DICOM images, not an AI/ML algorithm that requires a training set in the conventional sense.
9. How the ground truth for the training set was established
Not applicable. As there's no mention of a training set for an AI/ML algorithm, the establishment of ground truth for a training set is not relevant here.
Summary of the document's approach to demonstrating safety and effectiveness:
The document primarily relies on substantial equivalence to legally marketed predicate devices (Stentor iSite Radiology Workstation and Aquariusnet). The "Validation and Effectiveness" section 1.8 states: "Extensive testing of the software and hardware have been performed Extensive testing of the Sonward and no halfty control staff, and by potential customers." This suggests internal verification and validation activities were conducted, but not a formal clinical performance study with specific acceptance criteria that would be reported in this manner for a new AI/diagnostic algorithm. The "Substantial Equivalence Chart" (Section 1.9) details largely functional and technical similarities between EasyViz and its predicates.
§ 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).