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2D Quantitative Analysis is a post processing software medical device intended to assist physicians through providing quantitative information as additional input for their comprehensive diagnosis decision making process and planning during cardiovascular procedures and for post procedural evaluation. 2D Quantitative Analysis consists of six applications:

The 2D Quantitative Coronary Analysis application is intended to be used for quantification of coronary artery dimensions (approximately 1 to 6 mm) from 2D angiographic images.

The 2D Quantitative Vascular Analysis application is intended to be used for quantification of aortic and peripheral artery dimensions (approximately 5 to 50 mm) from 2D angiographic images.

The 2D Left Ventricle Analysis and the Biplane 2D Left Ventricle Analysis applications are intended to be used for quantification of left ventricular volumes and local wall motion from biplane angiographic series. respectively.

The 2D Right Ventricle Analysis and the Biplane 2D Right Ventricle Analysis applications are intended to be used for quantification of right ventricular volumes and local wall motion from biplane angiographic series, respectively.

2D Quantitative Analysis is a software application that assists the user with quantification of

• · vessels and vessel obstructions,
• · ventricular volumes and
• ventricular wall motion

from angiographic X-ray images. The software provides semi-automatic contour detection of vessels, catheters and the left ventricle in angiographic X-ray images, where the end-user is able to edit the contours. 2D Quantitative Analysis implements computational models for the quantification of vessels, obstructions in vessels, ventricular volumes and ventricular local wall motion from 2D contours.

The proposed 2D Quantitative Analysis will be offered as an optional accessory to the Philips Interventional X-ray systems.

This looks like a 510(k) premarket notification for a medical device called "2D Quantitative Analysis" from Philips Medical Systems. Let's break down the information to answer your request.

It's important to note that this document is a 510(k) summary, which often focuses on demonstrating substantial equivalence to a predicate device rather than presenting extensive de novo clinical studies with detailed acceptance criteria and performance tables as might be expected for novel devices or PMAs.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of numerical acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) for the device's performance, nor does it provide a direct quantifiable "reported device performance" against such criteria in the context of human-level tasks. Instead, the "acceptance criteria" appear to be met through demonstration of compliance with standards and successful completion of verification and validation testing, confirming it "conforms to its specifications" and "intended use and user needs."

The primary performance claim for the 2D Quantitative Analysis device is its ability to quantify:

• Vessel and vessel obstructions
• Ventricular volumes
• Ventricular wall motion

From angiographic X-ray images, with semi-automatic contour detection and user editing capabilities.

The document states:

• "Dedicated phantom based algorithm validation testing has been performed to ensure sufficient accuracy and agreement with the predicate device. Results demonstrated that the algorithm confirms to its specifications."
• "Non-clinical software validation testing covered the intended use and commercial claims as well as usability testing with representative intended users. Results demonstrated that the 2D Quantitative Analysis conforms to its intended use and user needs."

Therefore, the acceptance criteria are implicitly satisfied by the successful completion of these tests and demonstrating substantial equivalence. Specific numerical performance metrics against defined thresholds are not provided in this summary.

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

The document mentions "Dedicated phantom based algorithm validation testing" and "Non-clinical software validation testing." However, it does not specify the sample size (number of cases/patients or phantoms) used for these test sets.

The data provenance is also not explicitly stated as retrospective or prospective clinical data from specific countries. The testing appears to be primarily phantom-based and non-clinical software validation, rather than clinical studies using patient data.

3. 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 used to establish ground truth for any test set. The validation appears to be against "specifications" and "predicate device agreement," which for phantom studies would likely involve known measurements or highly controlled scenarios rather than expert consensus on clinical images in the traditional sense.

4. Adjudication Method for the Test Set

Since the document does not describe the use of human experts to establish ground truth on clinical images, an "adjudication method" for a test set (like 2+1 or 3+1) is not applicable or mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

The document explicitly states: "2D Quantitative Analysis, did not require clinical data since substantial equivalence to the currently marketed Allura Xper FD series and Allura Xper OR Table series was demonstrated..."

Therefore, an MRMC comparative effectiveness study, particularly one measuring the effect size of human readers with vs. without AI assistance, was not performed or, at least, not presented in this 510(k) summary. The device is a "post processing software medical device intended to assist physicians through providing quantitative information," implying it's a tool for physicians, but its impact on human reader performance was not quantified in a comparative study here.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

The document states: "Dedicated phantom based algorithm validation testing has been performed to ensure sufficient accuracy and agreement with the predicate device. Results demonstrated that the algorithm confirms to its specifications."

This "phantom based algorithm validation testing" could be considered a form of standalone performance assessment, as it would evaluate the algorithm's output against known phantom measurements or the predicate's performance, without direct human interaction during the measurement process. However, the device is described as having "semi-automatic contour detection... where the end-user is able to edit the contours," indicating a human-in-the-loop design in its intended clinical use. The "standalone" testing here would likely refer to the algorithm's ability to generate measurements from specific inputs accurately.

7. The Type of Ground Truth Used

For the "dedicated phantom based algorithm validation testing," the ground truth was likely derived from known physical dimensions or precisely measured values from the phantoms. For "non-clinical software validation testing," ground truth would be based on the established "intended use and user needs" and the device's functional specifications. Pathology or outcomes data are not mentioned.

8. The Sample Size for the Training Set

The document does not provide any information about the sample size used for a training set. This is typical for 510(k) submissions focusing on substantial equivalence, especially for devices that may not rely on machine learning models trained on large datasets in the way that some modern AI devices do. The description of the device's functionality (semi-automatic contour detection, computational models) suggests a more rule-based or traditional image processing approach rather than deep learning, though the exact methodologies are not detailed.

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

Since no training set information is provided, how its ground truth was established is also not discussed.

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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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