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cvi42 Coronary Plaque Software Application is intended to be used for viewing, post-processing, qualitative and quantitative evaluation of cardiovascular computed tomography (CT) images in a Digital Imaging and Communications in Medicine (DICOM) Standard format.

It enables a set of tools to assist physicians in qualitative and quantitative assessment of cardiac CT images to determine the presence and extent of coronary plaques and stenoses, in patients who underwent Coronary Computed Tomography Angiography (CCTA) for evaluation of CAD or suspected CAD.

cvi42 Coronary Plaque's semi-automated machine learning algorithms are intended for an adult population.

cvi42 Coronary Plaque shall be used only for cardiac images acquired from a CT scanner. It shall be used by qualified medical professionals, experienced in examining cardiovascular CT images, for the purpose of obtaining diagnostic information as part of a comprehensive diagnostic decision-making process.

Device Description

Circle's cvi42 Coronary Plaque Software Application ('cvi42 Coronary Plaque' or 'Coronary Plaque Module', for short) is a Software as a Medical Device (SaMD) that enables the analysis of CT Angiography scans of the coronary arteries of the heart. It is designed to support physicians in the visualization, evaluation, and analysis of coronary vessel plaques through manual or semi-automatic segmentation of vessel lumen and wall to determine the presence and extent of coronary plaques and luminal stenoses, in patients who underwent Coronary Computed Tomography Angiography (CCTA) for the evaluation of coronary artery disease (CAD) or suspected CAD. The device is intended to be used as an aid to the existing standard of care and does not replace existing software applications that physicians use. The Coronary Plaque Module can be integrated into an image viewing software intended for visualization of cardiac images, such as Circle's FDA-cleared cvi42 Software Application. The Coronary Plaque Module does not interface directly with any data collection equipment, and its functionality is independent of the type of vendor acquisition equipment. The analysis results are available on-screen, can be sent to report or saved for future review.

The Coronary Plaque Module consists of multiplanar reconstruction (MPR) views, curved planar reformation (CPR) and straightened views, and 3D rendering of the original CT data. The Module displays three orthogonal MPR views that the user can freely adjust to any position and orientation. Lines and regions of interest (ROIs) can be manually drawn on these MPR images for quantitative measurements.

The Coronary Plaque Module implements an Artificial Intelligence/Machine Learning (AI/ML) algorithm to detect lumen and vessel wall structures. Further, the module implements post-processing methods to convert coronary artery lumen and vessel wall structures to editable surfaces and detect the presence and type of coronary plaque in the region between the lumen and vessel wall. All surfaces generated by the system are editable and users are advised to verify and correct any errors.

The device allows users to perform the measurements listed in Table 1.

AI/ML Overview

Here's a summary of the acceptance criteria and study details based on the provided FDA 510(k) Clearance Letter for the cvi42 Coronary Plaque Software Application:

1. Table of Acceptance Criteria and Reported Device Performance

Endpoint	Acceptance Criteria (Implied)	Reported Device Performance	Pass / Fail
Lumen Mean Dice Similarity Coefficient (DSC)	Not explicitly stated but inferred as >= 0.76 with positive result	0.76	Pass
Wall Mean Dice Similarity Coefficient (DSC)	Not explicitly stated but inferred as >= 0.80 with positive result	0.80	Pass
Lumen Mean Hausdorff Distance (HD)	Not explicitly stated but inferred as <= 0.77 mm with positive result	0.77 mm	Pass
Wall Mean Hausdorff Distance (HD)	Not explicitly stated but inferred as <= 0.87 mm with positive result	0.87 mm	Pass
Total Plaque (TP) Pearson Correlation Coefficient (PCC)	Not explicitly stated but inferred as >= 0.97 with positive result	0.97	Pass
Calcified Plaque (CP) Pearson Correlation Coefficient (PCC)	Not explicitly stated but inferred as >= 0.99 with positive result	0.99	Pass
Non-Calcified Plaque (NCP) Pearson Correlation Coefficient (PCC)	Not explicitly stated but inferred as >= 0.93 with positive result	0.93	Pass
Low-Attenuation Plaque (LAP) Pearson Correlation Coefficient (PCC)	Not explicitly stated but inferred as >= 0.74 with positive result	0.74	Pass

Note: The acceptance criteria for each endpoint are not explicitly numerical in the provided text. They are inferred to be "met Circle's pre-defined acceptance criteria" and are presented here as the numeric value reported, implying that the reported value met or exceeded the internal acceptance threshold for a 'Pass'.

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

Sample Size for Test Set: Not explicitly stated. The document mentions "All data used for validation were not used during the development of the ML algorithms" and "Image information for all samples was anonymized and limited to ePHI-free DICOM headers." However, the specific number of cases or images in the test set is not provided.
Data Provenance: Sourced from multiple sites, with 100% of the data sampled from US sources. The data consisted of images acquired from major vendors of CT imaging devices.

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

Number of Experts: Three expert annotators were used.
Qualifications of Experts: Not explicitly stated beyond "expert annotators." The document implies they are experts in coronary vessel and lumen wall segmentation within cardiac CT images.

4. Adjudication Method for the Test Set

The ground truth was established "from three expert annotators." While it doesn't explicitly state "2+1" or "3+1", the use of three annotators suggests a consensus-based adjudication, likely majority vote (e.g., if two out of three agreed, that constituted the ground truth, or a more complex consensus process). The specific method is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No. The document states, "No clinical studies were necessary to support substantial equivalence." The evaluation was primarily based on the performance of the ML algorithms against a reference standard established by experts, not on how human readers improved their performance with AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

Yes. The performance evaluation focused on the "performance of the ML-based coronary vessel and lumen wall segmentation algorithm... evaluated against pre-defined acceptance criteria and compared to a reference standard established from three expert annotators." This indicates a standalone performance assessment of the algorithm's output. The device is also described as having "semi-automated machine learning algorithms", implying the user can verify and correct.

7. The Type of Ground Truth Used

Expert Consensus. The ground truth was established "from three expert annotators," indicating that human experts' annotations formed the reference standard against which the algorithm's performance was measured.

8. The Sample Size for the Training Set

Not explicitly stated. The document mentions the ML algorithms "have been trained and tested on images acquired from major vendors of CT imaging devices," but it does not provide the specific sample size for the training set. It only clarifies that the validation data was not used for training.

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

Not explicitly stated. The document describes how the ground truth for the validation/test set was established (three expert annotators). It does not provide details on how the ground truth for the training set was generated.

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