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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.

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.

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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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X-RAY VVA is software intended to be used for performing calculations in X-ray angiographic images of the chambers of the heart and of blood vessels. These calculations are based on contours that are either manually drawn by the clinician or trained medical technician who is operating the software, or automatically detected by the software and subsequently presented for review and manual editing. X-RAY VVA is also intended to be used for performing caliper measurements. The results obtained are displayed on top of the images and provided in reports. The analysis results obtained with X-RAY VVA are intended for use by cardiologists and radiologists: to support clinical decisions concerning the heart and vessels to support the evaluation of interventions or drug therapy applied for conditions of the heart . and vessels. X-RAY VVA is indicated for use in clinical settings where validated and reproducible quantified results are needed to support the calculations in X-ray angiographic images of the heart and of blood vessels, for use on individual patients with cardiovascular disease. When the quantified results provided by X-RA Y VVA are used in a clinical setting on X-ray images of an individual patient, they can be used to support the clinical decisions making for the diagnoiss of the patient or the evaluation of the treatment applied. In this case, the results are explicitly not to be regarded as the sole, intefutable basis for clinical diagnosis, and they are only intended for use by the responsible clinicians.

X-RAY VVA (Vessel and Ventricular Analysis) is image post-processing software for the viewing and quantification of digital x-ray angiographic images of blood vessels and of the chambers of the heart. Semi-automatic contour detection forms the basis for the analyses. Its functionality is independent of the type of vendor acquisition equipment. The analysis results are available on screen, and can be exported in various electronic formats. X-RAY VVA has been developed as a standalone application to run on a Windows based operating system. The import of images and the export of analysis results are via CD / DVD, a PACS or network environment. X-RAY VVA has a modular structure that consists of its previously cleared predicate devices: OCA-CMS, QVA-CMS, QLV-CMS, and CMS-VIEW. X-RAY VVA comprises their respective functionalities for analyzing the blood vessels and the left ventricle. In addition, X-RAY VVA includes new functionality for the analysis of: the right ventricle, stent and sub-segments, coronary anewysms, and bifurcations.

The provided text for K112807 does not contain the detailed information necessary to complete most of the requested fields regarding acceptance criteria and study design. The document is a 510(k) summary focusing on substantial equivalence to predicate devices. It states that "Testing and validation have produced results consistent with design input requirements" but does not elaborate on what those requirements or results were.

Therefore, many of the requested fields cannot be accurately filled based on the provided text.

Here's a breakdown of what can and cannot be answered:

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample size for test set: Not specified in the document.
• Data provenance: Not specified in the document.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

• Not specified in the document.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

• Not specified in the document.

5. 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

• A MRMC study is not mentioned in the document. The device is described as "image post-processing software" that assists with quantification, suggesting it's an aid, but no comparative effectiveness study with human readers is described regarding improvement with or without AI assistance.

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

• The document implies that the software's automatic contour detection is presented for review and manual editing, indicating a human-in-the-loop workflow. A standalone performance study of the algorithm alone is not described.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Not specified in the document.

8. The sample size for the training set

• Not specified in the document.

9. How the ground truth for the training set was established

• Not specified in the document.

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Detect the contour of the coronary vessel from a set of angiographic X-ray images – Generate absolute measurements about the dimensions of the coronary arterial segment in 3D space to improve accuracy by elimination of out-of-plane and foreshortening errors.

The CAAS OCA 3D is one of the software modules intended to run on the Cardiovascular Angiography Analysis System mark. CAAS. It functions in the same manner as other vascular analysis software packages. The OCA-3D module allows accurate and reproducible quantification of the coronary arteries from a set of angiographic X-ray images. The analyst selects between 2 and 5 angiographic images obtained from different X-Ray projections. On each of the images a classic 2D arterial detection is performed, after which from all images a reconstruction of the arterial segment is obtained in 3D space. Indication of a common point in each of the images is used to obtain an exact spatial relationship between the images. After the selection of the arterial segment of interest the contour of this arterial segment is automatically detected. Based on the contour information a number of analysis results can be calculated. Three analysis methods are available: The first method is an automatic reconstruction of the diseased arterial vessel by means of computing the reference along the arterial vessel to reconstruct the healthy arterial vessel, calculation of main result the % of stenosis. The second method allows for manual definition of the reference along the arterial segment by means of selecting one or more reference positions in the arterial segment, calculation of main result the % of stenosis. The third analysis method enables the user to define one or more subsegments, within each user defined subsegment the minimum, maximum and mean area are calculated. Besides area information also diameter results for each image used to reconstruct the vessel into 3D space are calculated over the arterial positions of interest. These diameter results will be corrected for out of plane calibration and length measurements will be corrected for foreshortening errors.

I am sorry, but the provided text does not contain sufficiently detailed information to complete all sections of your request. The document is primarily a 510(k) summary for the CAAS QCA 3D software package, establishing its substantial equivalence to a predicate device. It describes the device's functionality and intended use but does not present a specific study with acceptance criteria and detailed performance metrics in the format you've requested.

Here's an attempt to answer the questions based on the available information:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria or report specific performance metrics for the CAAS QCA 3D device. It mentions that "The CAAS QCA 3D software produces similar results as the predicate device" (K052988), implying that its performance is implicitly accepted if it matches the predicate. The focus is on its ability to detect contours and generate measurements for coronary arterial segments, correcting for out-of-plane and foreshortening errors.

Since specific criteria and performance values are not provided, I cannot generate this table.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not explicitly stated in the provided text. The document refers to its functionality with "a set of angiographic X-ray images" but does not detail any specific test set used for performance evaluation or its provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the document.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not provided in the document.

5. 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

A MRMC comparative effectiveness study is not mentioned. The document describes a software package for quantitative analysis, not an AI-assisted interpretation tool for human readers in the context of improving their performance.

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

The document describes the CAAS QCA 3D as "one of the software modules intended to run on the Cardiovascular Angiography Analysis System mark. CAAS." It performs "automatic detection" and "automatic reconstruction," implying standalone algorithmic processing. However, it also mentions the "analyst selects" images and arterial segments, suggesting a human-in-the-loop workflow where the software assists the user in quantification. No specific "standalone" performance study is detailed with metrics.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

This information is not provided. The document focuses on "automatic reconstruction of the diseased arterial vessel by means of computing the reference along the arterial vessel to reconstruct the healthy arterial vessel" and contour detection. The implicitly accepted ground truth would likely be established through validated methods for quantitative coronary analysis, potentially comparing against other established QCA systems or phantom studies, but this is not detailed.

8. The sample size for the training set

The document does not mention a training set, as it emphasizes that the device consists of "reused algorithms with the addition of several improvements that do not influence the indications for use." This suggests the algorithms were developed and validated prior to this submission, possibly without a distinct "training set" in the context of this specific 510(k).

9. How the ground truth for the training set was established

As no training set is mentioned for this specific submission, its ground truth establishment is not discussed.

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