(63 days)
Synapse 3D Colon Analysis is medical imaging software used with Synapse 3D Base Tools to accept, display, and process DICOM compliant 2D and 3D medical images acquired from CT for the purpose of viewing of a colon to detect polyps, masses, cancers, and other lesions. It is intended to be used by trained medical professionals in reading, interpreting, reporting, and screening.
Synapse 3D is medical application software running on Windows server/client configuration installed on a commercial general-purpose Windows-compatible computer. It offers software tools which can be used by trained medical professionals to aid them in reading, interpreting, reporting, and treatment planning.
Synapse 3D Colon Analysis is supporting virtual colonoscopy using CT data. Device descriptions described in this section discuss Synapse 3D Colon Analysis operating with Synapse 3D Base Tools (K120361). Some features in Synapse 3D Base Tools (K120361) are noted so the reviewer knows that Synapse 3D Base Tools (K120361) is not the focus of this submission. The device name, Synapse 3D Colon Analysis, is used in this document where necessary to specify the device of this submission.
Synapse 3D Base Tools (K120361) is connected to various DICOM compatible medical devices, such as CT, MR, CR, US, NM, PT, XA, etc. and to a PACS system storing data generated by these medical devices. It retrieves image data via network communication based on the DICOM standard and the retrieved image data are stored on the local disk managed by Synapse 3D Base Tools (K120361). The associated information of the image data is registered in the database and is used for display, image processing, analysis, etc.
Synapse 3D Colon Analysis can handle images of CT. Images newly created by Synapse 3D Colon Analysis not only can be displayed on a display, but also can be printed on a hardcopy using a DICOM printer or a Windows printer.
Synapse 3D Colon Analysis with Synapse 3D Basic Tools (K120361) and above can be integrated with Synapse PACS V3.2.1 and above and with Synapse Cardiovascular system.
In summary, this 510(k) submission focuses on the Synapse 3D Colon Analysis with the capability of performing analysis on the CT images of the colon and supporting the trained medical professionals in reading, interpreting, reporting, and screening.
The provided text does not contain specific acceptance criteria or a detailed study proving the device meets said criteria in the format requested. The document is a 510(k) summary for the FUJIFILM Synapse 3D Colon Analysis, outlining its substantial equivalence to a predicate device, not a performance study report with detailed acceptance criteria and results.
However, based on the information provided, here's what can be extracted and inferred regarding testing:
1. Table of Acceptance Criteria and Reported Device Performance:
The document states "Pass/Fail criteria were based on the requirements and intended use of the product." However, it does not list the specific quantitative acceptance criteria for segmentation accuracy, measurement accuracy, or other performance metrics. It only concludes that "Test results showed that all tests successfully passed."
Therefore, a table cannot be fully populated from the given text.
2. Sample size used for the test set and the data provenance:
- Sample Size: Not explicitly stated. The document mentions "performance comparison testing on retrospective images," but does not quantify the number of images or cases used in this test set.
- Data Provenance: Retrospective. The text indicates "performance comparison testing on retrospective images." The country of origin is not mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not provided in the document. The text does not detail how ground truth was established for the retrospective images used in testing.
4. Adjudication method (e.g. 2+1, 3+1, none) 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:
- MRMC Study: No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not performed or at least not described in this 510(k) summary. The testing mentioned was a "performance comparison testing on retrospective images to help demonstrate that the proposed device is substantially equivalent to the predicate device," implying a technical or functional comparison, not a reader study.
- Effect Size: Not applicable as no such study was conducted or reported.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Yes, the document states "Testing involved system level functionality test, segmentation accuracy test, measurement accuracy test, interfacing test, usability test, serviceability test, labeling test... In addition, we conducted the performance comparison testing on retrospective images." This implies various standalone tests of the algorithm's performance (e.g., segmentation accuracy, measurement accuracy are metrics for the algorithm itself).
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc):
The specific type of ground truth used for the "segmentation accuracy" and "measurement accuracy" tests is not explicitly stated. It can be inferred that for accuracy tests, some form of reference standard (e.g., expert-drawn contours or measurements, or potentially pathology if the comparison was to actual polyp presence) would have been used, but this is not detailed.
8. The sample size for the training set:
The document describes pre-market testing and comparison to a predicate device. It does not provide any information about a training set size, as this type of 510(k) summary typically focuses on validation or verification, not development or training data.
9. How the ground truth for the training set was established:
Not applicable, as information on a training set is not provided.
§ 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).