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Synapse 3D Lung and Abdomen 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, reporting, and treatment planning. Synapse 3D Lung and Abdomen 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 Base Tools, Synapse 3D Lung and Abdomen Analysis is intended to;

• use non-contrast and contrast enhanced computed tomographic images of the lung, provide custom workflows and UI, and reporting for lung analysis including boundary detection and volume calculation for pulmonary nodules in the lung based on the location specified by the user, segmentation of bronchial tubes in the lung, approximation of air supply region by the user specified bronchial tube, identifying, displaying and processing low absorption regions in the lung.
• use non-contrasted CT images and calculate subcutaneous fat and visceral fat areas in 2D and both volumes in 3D.

Synapse 3D Lung and Abdomen Analysis (V3.0) is the updated version of previously-cleared Synapse 3D Lung and Abdomen Analysis software (cleared by CDRH via K103720 on 03/16/2011).

Synapse 3D Lung and Abdomen Analysis is used in addition to Synapse 3D Base Tools (K120361, cleared on April 6, 2012) to analyze the images acquired from CT. Synapse 3D Lung and Abdomen Analysis is intended to provide trained medical professionals with tools to aid them in reading, interpreting, reporting, and treatment planning of DICOM compliant medical images. This product is not intended for use with or for the primary diagnostic interpretation of mammography images.

Synapse 3D Lung and Abdomen Analysis (V3.0) is an application that performs the CT Jung analysis/airway and abdomen 2D and 3D fat analysis.

• Lung analysis/Airway .
  Lung analysis/Airway is an application using non-contrast and contrast enhanced computed tomographic images of the lung which provides custom workflows and UI, and reporting functions including boundary detection and volume calculation for pulmonary nodules in the lung based on the location specified by the user, segmentation of bronchial tubes in the lung, approximation of air supply region by the user specified bronchial tube, identifying, displaying and processing low absorption regions in the lung,

• 2D Fat Analysis (Unchanged from the cleared version K103720) . 2D Fat Analysis is an application which can use single slice (2-dimensional) noncontrasted CT images to calculate subcutaneous fat and visceral fat areas.

• 3D Fat Analysis (Unchanged from the cleared version K103720) . 3D Fat Analysis is an application which can use volume (3-dimensional) non-contrasted CT images to calculate subcutaneous fat and visceral fat areas and volumes.

The following common image processing functions are available to support the analysis of the lung and abdomen CT images. These functions belong to and are provided by Synapse 3D Base Tools (K120361) that is used with Synapse 3D Lung and Abdomen Analysis (V3.0).

• Window width and window level. .
• Zooming, panning, flip, rotate. .
• Measurement of lengths, areas, etc. .
• Adding annotations on an image. .
• Extraction and Deletion of 3D objects: Editing of mask areas using the smart out feature. .
• 3D clipping: The display area can be specified for 3D display. .
• Creation of video files: Video files with 2D or 3D display can be created. .

The provided text describes the acceptance criteria and the study conducted for the Synapse 3D Lung and Abdomen Analysis (V3.0) device, specifically for its new lung volume analysis feature (approximation of air supply region).

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Simulated Test: "a simulated image data set for air supply region approximation with known air supply volume for each bronchus." (Specific number of cases or volumes not given, but implies a designed set.)
• Real Human Body CT Image Test: "five lung CT image data sets of real human body."
• Data Provenance: Not explicitly stated for either test in terms of country of origin or whether it was retrospective or prospective. The term "real human body" suggests clinical data.

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

• Simulated Test: Ground truth was established by "known air supply volume for each bronchus," implying a computational or theoretical basis rather than expert consensus on images. Experts were not used to establish this ground truth.
• Real Human Body CT Image Test: The ground truth for the "reference volumes of five pulmonary lobes for each test data set" was established manually "using the validated segmentation tools." The number of experts is not specified, nor are their particular qualifications (e.g., radiologist with X years of experience). The phrasing "manually using the validated segmentation tools" suggests a skilled user performing the segmentation, rather than a multidisciplinary expert panel.

4. Adjudication Method for the Test Set

• The document implies that the "validated segmentation tools" were used to establish the reference volumes for the real human body CT images. No formal adjudication method like "2+1" or "3+1" is described. The simulated test had "known values," so no adjudication was needed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No MRMC comparative effectiveness study is mentioned for the new lung volume analysis feature. The testing described focuses on the accuracy of the algorithm's measurements against defined ground truths.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Yes, the studies described are essentially standalone performance evaluations of the algorithm for the new lung volume analysis feature. In the "Test using lung CT image data sets of real human body," the device's output (air supply region approximation) is compared against "reference volumes" established prior to or independently of the device's main function being tested. The user's role is primarily to specify the root of each bronchus, which is an input for the algorithm, but the calculation and approximation itself are done by the device.

7. Type of Ground Truth Used

• Simulated Test: "Known air supply volume" (presumably computationally derived or theoretically defined).
• Real Human Body CT Image Test: Expert consensus via "manual... segmentation and volume calculation using validated segmentation tools" for pulmonary lobes. This serves as a reference standard for comparison with the device's approximation of air supply regions.

8. Sample Size for the Training Set

• The document does not provide information on the sample size used for the training set for the lung volume analysis feature. The studies described are verification/validation tests, not details of model training.

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

• Since the document does not provide information on the training set, it also does not describe how its ground truth was established.

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