(76 days)
Synapse 3D Liver Analysis is medical imaging software used with Synapse 3D Basic Tools that is intended to provide trained medical imaging professionals, including Physicians and Radiologists, with tools to aid them in reading, interpreting, reporting, and treatment planning. Synapse 3D Liver Analysis accepts DICOM compliant medical images acquired from CT.
This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.
Addition to Synapse 3D Basic Tools, Synapse 3D Liver Analysis uses contrast enhanced computed tomographic images of the body and provides custom workflows and UI, and reporting functions for liver analysis including, liver segmentation, tumor segmentation, segmentation of intrahepatic vessels as well as the approximation of vascular territories.
Synapse 3D Liver Analysis is an application which uses the intravenous contrasted CT study of a liver to segment the liver and various blood vessels and to perform 3D display of the results. Using the information of segmented liver, hepatic vessels, tumors, and the morphological structure of vessel system and blood supply volume of each vessel, the user can analyze the liver, vessels and tumors and plan the treatment.
Synapse 3D Liver Analysis is used in addition to the previously-cleared features available from Synapse 3D Basic Tools (K101662) to analyze the images acquired from CT. Synapse 3D Liver Analysis is intended to provide trained medical imaging professionals, including Physicians and Radiologists, with tools to aid them in reading, interpreting, and treatment planning and accepts DICOM compliant medical images.
Synapse 3D Liver Analysis with Synapse 3D Basic Tools can be integrated with our cleared Fujifilm's Synapse Workstation, version 3.2.1 and above, and can be used as a part of a Synapse system. Synapse 3D Liver Analysis also can be integrated with Fujifilm's Synapse Cardiovascular for cardiology purposes.
This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.
The provided text does not contain detailed information about specific acceptance criteria or a comprehensive study report to prove the device meets these criteria. The document is a 510(k) summary and FDA decision letter for the Synapse 3D Liver Analysis software. It primarily focuses on the regulatory submission, device description, indications for use, and substantial equivalence to a predicate device, rather than a detailed performance study.
However, based on the information available and the general type of device (medical image processing software), we can infer certain aspects and identify what is missing.
Here's an attempt to answer the questions, highlighting what is present and what is not:
Acceptance Criteria and Study Details for Synapse 3D Liver Analysis
1. A table of acceptance criteria and the reported device performance
The provided text does not include a table of acceptance criteria or reported device performance metrics. For a device like Synapse 3D Liver Analysis, which segments structures and provides analysis tools, typical acceptance criteria would involve:
- Accuracy of segmentation: e.g., Dice Similarity Coefficient (DSC), volumetric overlap, surface distance metrics for liver, vessels, and tumors.
- Precision/Reproducibility: Consistency of results across different users or repeated analyses.
- Performance/Speed: Time taken for segmentation and analysis.
- Usability: User satisfaction and efficiency with the interface.
2. Sample size used for the test set and the data provenance
The document does not specify the sample size used for any test set or the data provenance (e.g., country of origin, retrospective/prospective).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not provide information on the number or qualifications of experts used to establish ground truth. For a device performing segmentation, ground truth would typically be established by radiologists, surgeons, or other specialists manually segmenting structures on images.
4. Adjudication method for the test set
The document does not mention any adjudication method (e.g., 2+1, 3+1) for a test set.
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
The document does not indicate that a MRMC comparative effectiveness study was done. The device is described as providing "tools to aid them [medical professionals] in reading, interpreting, and treatment planning," suggesting an assistive role. However, no study demonstrating an improvement in human reader performance with the aid of the software is reported.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document does not explicitly describe a standalone performance study for the algorithm. While it performs liver and vessel segmentation automatically (as implied by "segment the liver and various blood vessels"), no specific metrics for this standalone performance are provided.
7. The type of ground truth used
The document does not explicitly state the type of ground truth used. For segmentation tasks in medical imaging, ground truth is most commonly established through expert consensus (manual delineation by medical professionals, sometimes with pathology correlation if available).
8. The sample size for the training set
The document does not mention the sample size used for the training set. Given the date (2011), the use of deep learning (which requires large training sets) was not as prevalent as it is today, but some form of training data would have been necessary for segmentation algorithms.
9. How the ground truth for the training set was established
The document does not describe how the ground truth for the training set was established. Similar to the test set, it would likely involve manual annotation by experts.
§ 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).