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ClearView cCAD is a software application designed to assist skilled physicians in analyzing breast ultrasound images. ClearView cCAD automatically classifies shape and orientation characteristics of user-selected regions of interest (ROIs).

The software allows the user to annotate, and automatically record and/or store selected views. The software also automatically generates reports from user inputs annotated during the image analysis process as well as the automatically generated characteristics. The output of this system will be a DICOM compatible (e.g. grayscale softcopy presentation state (GSPS)) and/or PDF report that can be sent along with the original image to standard film or paper printers or sent electronically to an intranet webserver or other DICOM compatible device.

cCAD includes options to annotate and describe the image based on the ACR BI-RADS® Breast Imaging Atlas. In addition. the report form has been designed to support compliance with the ACR BI-RADS @ Ultrasound Lexicon Classification Form.

When interpreted by a skilled physician, this device provides information that may be useful in screening and diagnosis. Patient management decision should not be made solely on the results of the cCAD analysis. The ultrasound images displayed on cCAD must not be used for primary diagnostic interpretation.

ClearView cCAD is a software application designed to assist skilled physicians in analyzing breast ultrasound images. ClearView cCAD automatically classifies shape and orientation characteristics of user-selected regions of interest (ROIs). The device uses multivariate pattern recognition methods to perform characterization and classification of images.

For breast ultrasound, these pattern recognition and classification methods are used by a radiologist to analyze such features as shape, orientation, and putative BI-RADS® category which can then be used to describe the lesion in the ACR BI-RADS® breast ultrasound lexicon as well as assigning an ACR BI-RADS® categorization which is intended to support compliance with the ACR BI-RADS® ultrasound lexicon classification form. Similarly, this process can be used to assist in training, evaluation, and tracking of physician performance.

The cCAD software can be run on any Windows 7 or higher or Windows Embedded platform that has network, Microsoft IIS, and Microsoft SQL support and is cleared for use in medical imaging. The software does not require any specialized hardware, but the time to process ROIs will vary depending on the hardware specifications. ClearView cCAD is based on core BI-RADS models and lesion characteristic extraction algorithms that can use novel statistical, texture, shape, orientation descriptors, and physician input to help with proper ACR BI-RADS® assessment.

The ClearView cCAD processing software is a platform agnostic web service that queries and accepts DICOM compliant digital medical files from an ultrasound device, another DICOM source, or PACS server. To initiate analysis and processing, images are queried from a compatible location and loaded for display within the application. The user then selects an ROI to analyze by clicking and dragging a bounding box around the region requiring analysis. Once selected, the user then clicks the processing button which initiates the analysis and processing sequence. The results are displayed to the user on the monitor and can then be selected for automated reporting, storage, or modification. The output of this system will be a DICOM compatible overlay (e.g. grayscale softcopy presentation state (GSPS)) and/or PDF report that can be sent along with the original image to standard film or paper printers or sent electronically to an intranet webserver or other DICOM compatible devices distributed by various OEM vendors. All fields may be modified by the user at any time during the analysis and prior to archiving.

Here's a breakdown of the acceptance criteria and study details for the ClearView cCAD device, based on the provided text:

ClearView cCAD Acceptance Criteria and Study Details

1. Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size:
  • 1204 cases for shape analysis.
  • 1227 lesions for orientation analysis.
• Data Provenance: Not explicitly stated (e.g., country of origin). The study involved skilled physicians evaluating a dataset, implying medical images, but whether these were retrospective or prospective, or from specific geographical regions, is not mentioned.

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

• Number of Experts: Three MQSA certified skilled physicians.
• Qualifications of Experts:
  • Each with over 20 years of experience.
  • Each read at least 3000 images per year.

4. Adjudication Method for the Test Set

• Adjudication Method: "Majority decision" was used to establish ground truth for shape and orientation. This implies that if at least two out of the three experts agreed on a characteristic, that was considered the ground truth.

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

• Was an MRMC study done? No, a true MRMC comparative effectiveness study was not explicitly stated as having been performed to measure human reader improvement with AI assistance. The study compared the device's standalone performance to expert performance, showing statistical equivalence, but not how human readers' performance might change with the device.
• Effect size of human reader improvement with AI vs. without AI assistance: Not measured or reported in this document.

6. Standalone Performance Study

• Was a standalone study done? Yes. The study focused on the ClearView cCAD system's "ability to discern BI-RADS® based shape and orientation parameters" independently and compared these results to the ground truth established by expert radiologists.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (majority decision by three MQSA certified skilled physicians).

8. Sample Size for the Training Set

• Training Set Sample Size: Not explicitly stated in the provided document. The document describes the "bench testing" for the device's performance but does not specify the size of the dataset used to train the underlying multivariate pattern recognition methods and algorithms.

9. How Ground Truth for the Training Set Was Established

• Ground Truth for Training Set: Not explicitly stated. While the document mentions that the device uses "multivariate pattern recognition methods to perform characterization and classification of images" and is "based on core BI-RADS models and lesion characteristic extraction algorithms," it does not describe how the ground truth for these training datasets was established.

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