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Standalone software for medical intended for advanced visualization and quantitative analysis in adult populations to support physicians with diagnostics by means of enabling segmentation, quantification, review, and reporting of cardiac structures based on multi-dimensional echocardiographic images. Caas Qardia facilitates the following use:

· Segmentation of cardiovascular structures

· Calculation of quantitative indices describing cardiovascular function and structure

When the results provided by Caas Qardia are used in a clinical decision making, the results are explicitly not to be regarded as the sole, irrefutable basis for clinical decision making.

Caas Qardia is an image post-processing software package for diagraphy, which offers functionality to view echocardiographic images, to segment cardiovascular structures in these images, to analyze and quantify these cardiovascular structures and to present the analysis results in different formats.

Caas Qardia is a web-based solution for diagnostic echocardiography, intended for usage in a network environment or standalone usage. Caas Qardia is composed of the following key analysis features:

• -Study selection (from PACS server or local data)
• -Image viewing
• -Left ventricular ejection fraction analysis
• -Left ventricular strain analysis
• -Cardiac geometric measurements and velocity measurements
• -Reporting
• -Export of results

Results can be displayed on the screen, printed, or saved in a variety of formats to hard disk, network, PACS system or CD. Results and clinical images with overlay can also be printed as a hardcopy and exported in various electronic formats.

Here's a summary of the acceptance criteria and study details for the Caas Qardia device:

Caas Qardia Acceptance Criteria and Performance Study

• The document states these criteria were met for all values.

Here's the additional information about the study:

• Sample size used for the test set and the data provenance: The document does not explicitly state the sample size for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It mentions "clinical images" and "clinical datasets."

• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: The document does not specify the number of experts or their qualifications for establishing ground truth. It refers to "manually annotated ED and ES contours" as the comparison for volume and EF, implying expert annotation.

• Adjudication method (e.g., 2+1, 3+1, none) for the test set: The adjudication method is not mentioned.

• If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: No MRMC comparative effectiveness study involving human readers with and without AI assistance is described in this document. The performance testing focused on the device's accuracy against manual annotations and predicate device, and inter/intra-observer variability.

• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Yes, the performance testing described for ED/ES volume, EF, and GLS against manually annotated contours, and the comparison with the predicate device, represents standalone (algorithm only) performance.

• The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The ground truth for ED and ES volume and EF determination was based on manually annotated ED and ES contours. For Global Longitudinal Strain, it was evaluated by comparing results against the predicate device.

• The sample size for the training set: The document does not specify the sample size for the training set.

• How the ground truth for the training set was established: The document does not provide details on how the ground truth for the training set was established.

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