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Synapse 3D Base Tools is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, and treatment planning. Synapse 3D Base Tools accepts DICOM compliant medical images acquired from a variety of imaging devices including, CT, MR, CR, NM, PT, and XA, etc.

This product is not intended for use with or for the primary diagnostic interpretation of Mammography images. Synapse 3D Base Tools provides several levels of tools to the user:

Basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing, orthogonal / oblique / curved Multi-Planar Reconstructions (MPR), Average (RaySum) and Minimum (MinIP) Intensity Projection. 4D volume viewing, image subtraction, surface rendering, sector and rectangular shape MPR image viewing, MPR for dental images, creating and displaying multiple MPR images along an object, time-density distribution, basic image processing, CINE, measurements, annotations, reporting, storing, distribution, and general image management and administration tools, etc.

-Tools for regional segmentation of anatomical structures within the image data, path definition through vascular and other tubular structures, and boundary detection.

-Image viewing tools for modality specific images, including CT PET fusion and ADC image viewing for MR studies. -Imaging tools for CT images including virtual endoscopic viewing.

-Imaging tools for MR images including delayed enhancement image viewing, diffusion-weighted MRI data analysis.

The 3D image analysis software Synapse 3D Base Tools (V6.1) is medical application software running on Windows server/client configuration installed on commercial general-purpose Windows-compatible computers. It offers software tools which can be used by trained professionals to interpret medical images obtained from various medical devices, to create reports, or to develop treatment plans.

Synapse 3D Base Tools 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 it retrieves image data via network communications based on the DICOM standard. The retrieved image data are stored on the local disk managed by Synapse 3D Base Tools (V6.1), and the associated image-related information of the image data is registered in its database and is used for display, image processing, analysis, etc. Images newly created by Synapse 3D Base Tools (V6.1) 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 Base Tools (V6.1) is a basic software module that works with other cleared clinical applications, including Synapse 3D Cardiac Tools (K200973), Synapse 3D Perfusion Analysis (K162287), Synapse 3D Lung and Abdomen Analysis (K130542), Synapse 3D Liver and Kidney Analysis (K142521), Synapse 3D Nodule Analysis (K120679), Synapse 3D Colon Analysis (K123566), Synapse 3D Tensor Analysis (K141514) and Synapse 3D Blood Flow Analysis (K191544). All these software modules consist of the Synapse 3D product family.

Synapse 3D Base Tools can be integrated with Fujifilm's Synapse PACS, and can be used as a part of a Synapse system. Synapse 3D Base Tools also can be integrated with Fujifilm's Synapse Cardiovascular for cardiology purposes.

The provided text describes Synapse 3D Base Tools v6.1, a medical imaging software. However, it does not include specific acceptance criteria or a detailed study proving the device meets particular performance metrics. Instead, the document focuses on regulatory compliance, substantial equivalence to a predicate device, and general software development and testing procedures.

Here's an analysis of what can be extracted and what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

No specific acceptance criteria or quantitative performance metrics are provided in the document. The text generally states that "Test results showed that all tests passed successfully according to the design specifications," but it does not detail what those design specifications or acceptance criteria were.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: Not specified. The document mentions "benchmark performance testing was conducted using actual clinical images," but the number of images or cases used is not provided.
• Data Provenance: Not specified. It only mentions "actual clinical images" without details on country of origin, whether the data was retrospective or prospective, or other demographic information.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

Not specified. The document does not describe the establishment of a ground truth for a test set, nor does it mention the involvement or qualifications of experts for this purpose.

4. Adjudication Method for the Test Set

Not applicable/Not specified. Since the document doesn't detail a test set with ground truth established by experts, an adjudication method is not mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No MRMC comparative effectiveness study is mentioned. The submission focuses on device functionality and equivalence, not human reader performance with or without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop) Performance Study Was Done

The document states that "benchmark performance testing was conducted using actual clinical images to help demonstrate that the semi-automatic or automatic segmentation, detection, and registration functions implemented in Synapse 3D Base Tools achieved the expected accuracy performance." This implies some form of standalone evaluation of the algorithm's performance for these specific functions. However, no quantitative results or specific metrics for this standalone performance are provided.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

Not specified. While "benchmark performance testing" is mentioned for segmentation, detection, and registration, the method for establishing the "ground truth" against which these algorithms were benchmarked is not detailed.

8. The Sample Size for the Training Set

Not specified. The document mentions that some segmentation applications use a "Fully Convolutional Network" (a deep learning method), which implies a training set. However, the size of this training set is not provided.

9. How the Ground Truth for the Training Set Was Established

Not specified. For the deep learning segmentation features, the method of establishing ground truth for the training data is not described.

Summary of what is present in the document:

• Synapse 3D Base Tools v6.1 is an updated version of previously cleared software.
• It provides various image viewing, processing, and analysis tools for trained medical professionals.
• It accepts DICOM images from multiple modalities (CT, MR, CR, US, NM, PT, XA).
• It is not for primary diagnostic interpretation of mammography images.
• Some segmentation features are implemented using deep learning (Fully Convolutional Network).
• Nonclinical testing included standard software development processes (hazard analysis, risk management, requirements analysis, design, integration testing, system testing, etc.).
• "Benchmark performance testing was conducted using actual clinical images to help demonstrate that the semi-automatic or automatic segmentation, detection, and registration functions implemented in Synapse 3D Base Tools achieved the expected accuracy performance."
• All tests passed according to design specifications.
• Cybersecurity measures are in place.

Summary of what is missing/not specified in the document regarding acceptance criteria and performance study details:

• Quantitative acceptance criteria for any specific function.
• Detailed quantitative performance results (e.g., accuracy, precision, recall, Dice score for segmentation).
• Specific sample sizes for test sets or training sets.
• Details on data provenance (e.g., demographics, disease prevalence, acquisition parameters).
• Information on expert involvement in ground truth establishment (number or qualifications).
• Details on ground truth methodology (e.g., expert consensus, pathology reports).
• Results from any MRMC comparative effectiveness studies.
• Specific metrics or results for standalone algorithm performance.

The document appears to be a 510(k) summary focused on demonstrating substantial equivalence primarily through technical comparison and general software validation, rather than a detailed performance study with explicit acceptance criteria and corresponding results for specific AI/ML components.

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