(129 days)
Synapse 3D Blood Flow Analysis is medical imaging software used with Synapse 3D Base Tools that is intended to provide trained medical professionals, with tools to aid them in reading, interpreting, and treatment planning. Addition to the tools in Synapse 3D Base Tools, Synapse 3D Blood Flow Analysis provides the imaging and assessment tools of blood flow velocity and directions based on multi-slice, multi-phase and velocity encoded MR images.
Synapse 3D Blood Flow Analysis is a software application that is used to work with Synapse 3D Base Tools (cleared by via K120361 on 04/06/2012). Synapse 3D Base Tools (K120361) is connected through DICOM standard to medical devices such as CT, MR, CR, US, NM, PT, XA, etc. and to a PACS system storing data generated by these medical devices, and retrieves image data via network communication based on the DICOM standard. The retrieved image data are stored on the local disk managed by Synapse 3D Base Tools (K120361), and the associated image-related information of the image data is registered in the database and used for display, image processing, analysis, etc.
Synapse 3D Blood Flow Analysis developed to calculate the blood flow volume and velocity for an arranged ROI. The software can display the images on a display monitor, or printed them on a hardcopy using a DICOM printer or a Windows printer. The main functions of Synapse 3D Blood Flow Analysis are shown below.
- Display vessel images.
- Display the parameters such as flow velocity and volume calculated from the selected ROI.
- Display the time-intensity curve and the analysis result of the flow volume analysis and the flow velocity analysis.
- Display blood flow by flow velocity vector, streamlines and pathlines.
- Print or save reports.
Synapse 3D Blood Flow Analysis runs on Windows standalone and server/client configuration installed on a commercial general-purpose Windows-compatible computer. It offers software tools which can be used by trained professionals, such as radiologists, clinicians or general practitioners to interpret medical images obtained from various medical devices to create reports or develop treatment plans.
The provided document is a 510(k) summary for the Synapse 3D Blood Flow Analysis software. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study proving performance against specific acceptance criteria for a new clinical claim. Therefore, much of the requested information regarding acceptance criteria, study design, ground truth, and expert involvement is not present in this document.
Here's a breakdown of what can be extracted based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not provide a table of quantitative acceptance criteria for clinical performance (e.g., accuracy, sensitivity, specificity for specific diagnostic tasks) or directly report performance against such criteria. The "Testing and Performance Information" section describes the software development process and verification/validation activities, stating:
"benchmark performance testing was conducted using actual clinical images to help demonstrate that the semi-automatic or automatic segmentation, detection functions implemented in Synapse 3D Blood Flow Analysis achieved the expected accuracy performance. Pass/Fail criteria were based on the requirements and intended use of the product. Test results showed that all tests passed successfully according to the design specifications."
However, the specific "expected accuracy performance," the actual "test results," or the "Pass/Fail criteria" are not quantified or presented. The document focuses on functional and technical equivalence.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: Not specified. The document mentions "actual clinical images" were used for benchmark performance testing but does not provide the number of cases or subjects.
- Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). It only states "actual clinical images."
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
- This information is not provided in the document. The document describes the software's tools to "aid them [trained medical professionals] in reading, interpreting, and treatment planning," but it does not detail how ground truth was established for performance testing.
4. Adjudication Method for the Test Set
- This information is not provided in the document.
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 was not explicitly mentioned or described. The document states: "The subject of this 510(k) notification, Synapse 3D Blood Flow Analysis does not require clinical studies to support safety and effectiveness of the software." This indicates that the regulatory pathway did not necessitate a clinical efficacy study comparing human performance with and without the AI.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
- The document implies that "benchmark performance testing" was conducted for "semi-automatic or automatic segmentation, detection functions," suggesting an assessment of the algorithm's performance on its own. However, the exact methodology and metrics of this "standalone" assessment are not detailed. It's generally assumed that software like this would have undergone internal standalone validation.
7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)
- This information is not provided. While "actual clinical images" were used, the method for establishing the "ground truth" or reference standard for those images (e.g., expert consensus, comparison to another gold standard) is not described.
8. The Sample Size for the Training Set
- This information is not provided. The document focuses on verification and validation testing, not the training of a machine learning model, even though the device includes "semi-automatic or automatic segmentation, detection functions."
9. How the Ground Truth for the Training Set Was Established
- This information is not provided, as the document does not discuss the training set or its ground truth.
Summary of what is present and what is missing:
The 510(k) summary primarily focuses on:
- Describing the device's functions and intended use.
- Demonstrating technical and functional equivalence to a predicate device (CAAS MR 4D Flow).
- Outlining the software development process, risk management, and cybersecurity measures.
- Stating that verification and validation activities were performed and passed "according to the design specifications" but without providing quantitative results or detailed study methodologies.
The document does not contain the detailed clinical study information (acceptance criteria, specific performance metrics, sample sizes for training/test sets, ground truth methodology, expert qualifications, or MRMC study results) that would be expected for a device making new clinical claims requiring such evidence. The statement "does not require clinical studies to support safety and effectiveness of the software" reinforces that the 510(k) pathway for this device relied on substantial equivalence rather than a new clinical performance study.
§ 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).