(107 days)
CAAS A-Valve has been developed to support the interventionalist during or in preparation of treatment of the aortic root. Based on angiographic X-ray images an analysis is performed: To assist in C-arm projection selection to optimize visualization during treatment; To calculate dimensions of the aortic root corrected for out-of-plane magnification and foreshortening errors; To provide an objective and reproducible grading method for aortic regurgitation based on time versus density curves extracted from an aortogram. The software is used by or under supervision of a cardiologist. When the results provided by CAAS A-Valve are used in a clinical setting to support diagnoses or for assistance during intervention of cardiovascular conditions, the results are explicitly not to be regarded as the sole, irrefutable basis for clinical decision making.
CAAS A-Valve is a stand-alone software application, intended to run on a PC with a Windows operating system. The images for analysis can be read from a directory or from an X-ray system or PACS through a command line interface. The results can be displayed on the screen, printed or saved in a variety of formats to a hard disk, network, PACS system or CD. Results and clinical images with overlay can also be printed as a hardcopy. CAAS A-Valve consists of two separate workflows, Optimal Projection and qRA (quantitative Regurgitation Analysis). With CAAS A-Valve - Optimal Projection, angiographic images of the aortic root can be analyzed to determine a good projection for visualization of the aortic root and to perform basic measurements. As input for analysis, two angiographic images of the aortic root can be selected. On both images the contour of the aortic root is defined manually by the user. The 2D aortic root contours in each image are used to generate a 3D reconstruction of the aortic root. By indicating the right coronary cusp in both projections the software determines the recommended projection (PRL projection). This projection can be used to acquire an aortogram with the cusps in a line and all cusps visible. Additionally it is possible to perform diameter and length measurements based on the 3D reconstruction. The Optimal Projection workflow is 510(k) cleared under K113076. CAAS A-Valve - qRA is used to determine aortic regurgitation (also referred to as aortic insufficiency). This is done based on a multi-frame image showing the aortic root and the left ventricle while contrast liquid is injected in the aorta during the X-ray acquisition; also known as an aortogram. The user draws the contour of the aortic root and the left ventricle and indicates the basal plane. Next a static background, which is obtained from the images before contrast injection, is subtracted resulting in an image sequence in which the intensities correlate to the amount of contrast liquid. Based on this image sequence combined with the user input, time versus contrast density curves are calculated and visualized for both the aortic root and the left ventricle. The ratio between the area under the curve of the aortic root and the area under the curve of the left ventricle represents the amount of contrast liquid flowing from the aortic root to the left ventricle and is a measure for regurgitation. Additionally a dynamic color map is shown for the left ventricle. This color map is achieved by showing the accumulative area under the curve at each image frame as a movie with the same frame rate as used during the acquisition of the multi-frame image. CAAS A-Valve is designed for use in clinical practice to support the physician during or in preparation of treatment of the aortic root.
The acceptance criteria and study details for the CAAS A-Valve device are outlined below, focusing on the information available in the provided text.
1. Acceptance Criteria and Reported Device Performance
The document does not explicitly present a table of acceptance criteria with corresponding performance metrics in a pass/fail format. Instead, it describes what was verified and validated. The "Performance Data" section states that "System requirements - derived from the intended use and indications for use - as well as risk control measures are verified by System Testing. Additionally numerical accuracy and reproducibility is verified and validated for the following analysis results."
Based on this, the table below infers acceptance criteria from the verified and validated analysis results, and the reported performance is that these criteria were met, leading to a conclusion of safety and effectiveness.
| Acceptance Criteria (Inferred from Verified/Validated Results) | Reported Device Performance |
|---|---|
| Numerical accuracy and reproducibility of Optimal C-arm projection | Verified and validated |
| Numerical accuracy and reproducibility of Dimensions of the aortic root | Verified and validated (corrected for out-of-plane magnification and foreshortening errors) |
| Numerical accuracy and reproducibility of Time versus density curves | Verified and validated (for both the aortic root and the left ventricle, enabling calculation of a ratio for regurgitation and dynamic color map) |
| Numerical accuracy and reproducibility of Aortic regurgitation grade | Verified and validated (objective and reproducible grading method based on time versus density curves) |
| Overall System Safety and Effectiveness | The test results demonstrate safety and effectiveness of CAAS A-Valve in relation to its intended use and that CAAS A-Valve is considered as safe and effective as the predicate devices. The device complies with ISO 14971:2007, NEMA PS 2.1 3.20 (2011) DICOM Set, and IEC 62304 First edition 2006-05. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify the exact "sample size used for the test set" or the "data provenance (e.g., country of origin of the data, retrospective or prospective)". It generally refers to "System Testing," "numerical accuracy," and "reproducibility" being verified and validated but provides no specific numbers of cases or origin details.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not specify the "number of experts used to establish the ground truth for the test set" or their "qualifications". It mentions that the software is used by or under the supervision of a cardiologist, suggesting expert involvement in clinical use, but not specifically for establishing ground truth in testing. The process for defining aortic root contours is described as "defined manually by the user," implying clinician input.
4. Adjudication Method
The document does not specify any "adjudication method" (e.g., 2+1, 3+1, none) for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not mention a "multi-reader multi-case (MRMC) comparative effectiveness study" or any "effect size of how much human readers improve with AI vs without AI assistance." The device's role is described as "to support the interventionalist" and "to provide an objective and reproducible grading method," suggesting assistance rather than a comparative AI vs. human study for improvement.
6. Standalone Performance Study
Yes, a standalone performance study was implicitly done. The "Performance Data" section states that "System requirements... as well as risk control measures are verified by System Testing. Additionally numerical accuracy and reproducibility is verified and validated for the following analysis results." This indicates that the algorithm's output for Optimal C-arm projection, dimensions, time versus density curves, and aortic regurgitation grade was tested for accuracy and reproducibility on its own.
7. Type of Ground Truth Used
The type of ground truth used is primarily based on expert definition/manual input (user-defined contours) and numerical accuracy/reproducibility verification against established methods or expected values.
- For Optimal Projection: "On both images the contour of the aortic root is defined manually by the user. The 2D aortic root contours in each image are used to generate a 3D reconstruction of the aortic root. By indicating the right coronary cusp in both projections the software determines the recommended projection (PRL projection)."
- For Quantitative Regurgitation Analysis (qRA): "The user draws the contour of the aortic root and the left ventricle and indicates the basal plane." The calculated time versus density curves and the ratio for regurgitation are then compared to a "grading method for aortic regurgitation" that is stated to be "objective and reproducible."
This suggests that the ground truth for validating the device's calculations and determinations relies on initial manual inputs (expert-defined contours) and the ability of the system to consistently and accurately derive quantitative results from these inputs, aligning with established clinical understanding or other validated methods for measurement and grading.
8. Sample Size for the Training Set
The document does not specify the "sample size for the training set."
9. How the Ground Truth for the Training Set Was Established
The document does not provide information on "how the ground truth for the training set was established." Given the descriptions, it's possible that the "training set" (if any, as it's not explicitly mentioned as a machine learning model) would also rely on expert input for defining anatomical landmarks and confirming measurements, similar to how the test set's ground truth is implied to be established. However, this is speculative as no specific details are given.
§ 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).