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CAAS Workstation is a modular software product intended to be used by or under supervision of a cardiologist or radiologist in order to aid in reading and interpreting cardiovascular X-Ray images to support diagnoses and for assistance during intervention of cardiovascular conditions.

CAAS Workstation features segmentation of cardiovascular structures, 3D reconstruction of vessel segments based on multiple angiographic images, measurement and reporting tools to facilitate the following use:

• Calculate the dimensions of cardiovascular structures;
• Quantify stenosis in coronary and peripheral vessels;
• Quantify the motion of the left and right ventricular wall;
• Perform density measurements;
• Determine C-arm position for optimal imaging of cardiovascular structures;
• Enhance stent visualization and measure stent dimensions.

CAAS Workstation is intended to be used by or under supervision of a cardiologist or radiologist. When the results provided by CAAS Workstation are used in a clinical setting to support diagnoses and 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 Workstation is designed as a stand-alone modular software product for viewing and quantification of X-ray angiographic images intended to run on a PC with a Windows operating system. CAAS Workstation contains the analysis modules QCA, QCA3D, QVA, LVA, RVA and StentEnhancer.
The analysis modules QCA, QCA3D, QVA, LVA and RVA contain functionality of the previously cleared predicate devices CAAS (K052988) and CAAS QxA3D (K100292) for calculating dimensions of coronary and peripheral vessels and the left and right ventricles, quantification of stenosis, performing density measurements and determination of optimal C-arm position for imaging of vessel segments. Semi-automatic contour detection forms the basis for the analyses.
Functionality to enhance the visualization of a stent and to measure stent dimension is added by means of the analysis module StentEnhancer. This functionality is based on the StentOptimizer module of the IC-PRO System (K110256).
The quantitative results CAAS Workstation support diagnosis and intervention of cardiovascular conditions.
The analysis results are available on screen, and can be exported in various electronic formats.
The functionality is independent of the type of vendor acquisition equipment.

Here's a breakdown of the acceptance criteria and study information for the CAAS Workstation, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state numerical acceptance criteria with corresponding device performance metrics in a clear, tabular format. Instead, it relies on demonstrating substantial equivalence to predicate devices. The performance data section broadly states:

• "System requirements - derived from the intended use and indications for use - as well as risk control measures are verified by system testing."
• "For each analysis module a validation approach is created and the proper functioning of the algorithms is validated."
• "For analysis modules already implemented in earlier versions of CAAS regression testing is performed to verify equivalence in numerical results."
• "The test results demonstrate safety and effectiveness of CAAS Workstation in relation to its intended use and that CAAS Workstation is considered as safe and effective as the predicate devices."

Therefore, the acceptance criterion is substantial equivalence to previously cleared predicate devices (CAAS K052988, CAAS QxA3D K100292, and IC-PRO System K110256) in terms of intended use, indications for use, technological characteristics, measurements, and operating environment. The "reported device performance" is that the device meets this equivalence through system testing, algorithm validation, and regression testing, ensuring comparable safety and effectiveness.

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

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It generally refers to "system testing," "algorithm validation," and "regression testing" without specifying the number of cases or images used in these tests.

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

The document does not specify the number or qualifications of experts used to establish ground truth for any test sets. The intended users are "cardiologist or radiologist," suggesting their expertise would be relevant, but details about ground truth establishment are not provided.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) used for the test set.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not specifically described in the provided text. The submission focuses on demonstrating substantial equivalence to predicate devices, rather than a comparative effectiveness study showing improvement with AI assistance.

6. Standalone Performance Study (Algorithm Only)

The testing performed includes "the proper functioning of the algorithms is validated," which implies a standalone (algorithm only) performance evaluation. However, specific results or detailed methodologies of such a standalone study are not provided beyond the general statement of validation. The device is a "stand-alone modular software product," suggesting its algorithms function independently to produce results that aid clinicians.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for testing (e.g., expert consensus, pathology, outcomes data). Given the nature of the device (quantification of cardiovascular structures from angiographic images), it is highly probable that expert consensus (e.g., manual measurements by cardiologists/radiologists) would have been used as a reference for validation and regression testing, but this is not explicitly stated.

8. Sample Size for the Training Set

The document does not specify a sample size for any training set. Given the date of the submission (2014) and the focus on substantial equivalence to predicate devices, it's possible that traditional rule-based algorithms or earlier machine learning approaches were used that might not involve large-scale "training sets" in the modern deep learning sense. The device is presented as offering "semi-automatic" contour detection, which might rely on image processing algorithms rather than extensive machine learning training data.

9. How Ground Truth for the Training Set Was Established

Since no training set details are provided, the method for establishing its ground truth is also not mentioned.

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