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

• Calculate the dimensions of cardiovascular structures;
• Quantify stenosis in coronary vessels;
• Determine C-arm position for optimal imaging of cardiovascular structures;
• Quantify pressure drop in coronary vessels;
• Enhance stent visualization and measure stent dimensions;
  CAAS Workstation is intended to be used by or under supervision of a cardiologist.

CAAS Workstation is an image post-processing software package for advanced visualization and ysis in the field of cardiology or radiology and offers functionality to view X-Ray angiographic images, to segment cardiovascular structures in these images, to analyze and quantify these cardiovascular structures and to present the results in different formats.
CAAS Workstation is a client-server solution intended for usage in a network environment or standalone usage and runs on a PC with a Windows operating system. It can read DICOM X-ray images from a directory, or receive DICOM images from the X-ray or PACS system.
CAAS Workstation is composed out of the following analysis workflows: StentEnhancer and vFFR for calculating dimensions of coronary vessels, quantification of stenosis and calculating the pressure drop and vFFR value based on two 2D X-Ray angiographic images. Semi-automatic contour detection forms the basis for the analyses.
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. The functionality is independent of the type of vendor acquisition equipment.

The provided text describes a 510(k) premarket notification for the CAAS Workstation, a software package for advanced visualization and analysis in cardiology and radiology. However, it does not contain specific details about acceptance criteria or a study proving the device meets those criteria with quantitative performance metrics for AI/ML components.

The document states: "Performance testing demonstrated that the numerical results for the analysis workflows StentEnhancer and vFFR, as already available in predicate device K180019, were comparable." This is a qualitative statement of comparability to a predicate device, not a detailed presentation of acceptance criteria and the results of a study designed to meet them.

Therefore, I cannot fulfill all parts of your request with the provided input. I will outline what can be extracted and note what information is missing.

Summary of Device and Approval:

• Device Name: CAAS Workstation
• Applicant: Pie Medical Imaging B.V.
• FDA K-Number: K232147
• Regulation Name: Angiographic X-Ray System
• Regulatory Class: Class II
• Product Codes: QHA, LLZ
• Predicate Device: CAAS Workstation (K180019) – an earlier version of the same product.
• Basis for Clearance: Substantial Equivalence to the predicate device.

Indications for Use (Key Features):
CAAS Workstation features segmentation of cardiovascular structures, 3D reconstruction of vessel segments and catheter path based on multiple angiographic images, measurement and reporting tools to facilitate the following use:

• Calculate the dimensions of cardiovascular structures;
• Quantify stenosis in coronary vessels;
• Determine C-arm position for optimal imaging of cardiovascular structures;
• Quantify pressure drop in coronary vessels;
• Enhance stent visualization and measure stent dimensions;

Missing Information:

The provided text focuses on the regulatory clearance process through 510(k) substantial equivalence. This pathway often relies on demonstrating that a new device is as safe and effective as a legally marketed predicate device, rather than requiring extensive de novo clinical performance studies with specific acceptance criteria as you've requested for an AI/ML component. The document mentions "Performance testing," but it does not provide the details required to answer your specific questions about acceptance criteria, study design, sample sizes, ground truth establishment, or expert involvement for a new AI/ML model's performance.

The "AI" mentioned appears to refer more to automated image processing algorithms (semi-automatic contour detection, vFFR workflow involving pressure drop quantification, StentEnhancer workflow) rather than a novel, deep learning-based AI/ML algorithm that would typically necessitate the detailed performance study described in your prompt. The emphasis is on comparability of "numerical results" to the predicate, implying validation of existing algorithms, possibly with minor improvements, not a new AI/ML model with distinct performance criteria.

Based on the provided text, here's what can be inferred or explicitly stated, and what remains unknown:

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

• Acceptance Criteria: Not explicitly stated in quantitative terms in the provided text. The document broadly indicates that "numerical results for the analysis workflows StentEnhancer and vFFR...were comparable" to the predicate. This implies the acceptance criterion was "comparability" to the predicate's performance, but no specific thresholds (e.g., accuracy > X%, ROC AUC > Y) are given.
• Reported Device Performance: No quantitative performance metrics (e.g., sensitivity, specificity, accuracy, precision, recall) are provided in the text. The statement is qualitative: "numerical results...were comparable."

2. Sample size used for the test set and the data provenance:

• Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The document mentions reading DICOM X-ray images, but not the source of the test data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The device is intended for use by or under the supervision of a cardiologist, suggesting expert cardiac imaging knowledge would be relevant, but details about ground truth establishment are absent.

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

• Not specified.

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:

• Not described. The focus is on the device's standalone performance compared to a predicate, not on a human-in-the-loop MRMC study.

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

• Yes, implicitly. The "Performance testing demonstrated that the numerical results for the analysis workflows StentEnhancer and vFFR...were comparable" indicates an evaluation of the algorithm's output. This is consistent with a standalone performance assessment, as the comparison is about the output of the software itself.

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

• Not explicitly stated. Given the functionalities (quantifying stenosis, dimensions, pressure drop), the ground truth for these "numerical results" would likely involve comparison against a gold standard derived from established imaging methods, potentially quantitative measurements from calibrated imaging devices, or expert consensus measurements, but the document does not elaborate.

8. The sample size for the training set:

• Not specified. The document mentions "semi-automatic contour detection forms the basis for the analyses" for the vFFR workflow, which could imply a training process, but no details are given.

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

• Not specified.

In conclusion, the K232147 FDA clearance document for the CAAS Workstation confirms its regulatory pathway via substantial equivalence to a predicate device. While it mentions "Performance testing" and "comparable numerical results," it does not provide the detailed quantitative acceptance criteria, study methodology, or specific performance metrics that would typically be found in an in-depth clinical validation study for a novel AI/ML device. The information provided is sufficient for a 510(k) submission based on predicate equivalence but lacks the granularity for the specific technical and clinical performance questions asked.

Ask a specific question about this device

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, co-registering 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 and catheter path 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;
• Quantify pressure drop in coronary vessels;
• Co-registration of angiographic X-Ray images with IVUS and OCT images.
  CAAS Workstation is intended to be used by or under supervision of a cardiologist or radiologist.

The CAAS Workstation is designed as a stand-alone software package to run on a PC with a Windows operating system. It can read DICOM X-ray images from an directory, or received from the X-ray or PACS system. Intravascular images (such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT) in DICOM format can be read from a directory, or received from the intravascular imaging console or PACS system. IVUS images can also be received realtime as a video stream from an intravascular imaging console via a DVI streamer. The CAAS Workstation product has a moderate level of concern.

CAAS Workstation is composed out of the following analysis workflows: QCA, QCA3D, QVA, LVA, RVA, StentEnhancer and IV-LINQ of the previously cleared predicate device CAAS Workstation (K151780) for calculating dimensions of coronary and peripheral vessels and the left and right ventricles, quantification of stenosis, performing density measurements, determination of optimal C-arm position for imaging of vessel segments and functionality to enhance the visualization of a stent and to measure stent dimension. Semi-automatic contour detection forms the basis for the analyses. Functionality to co-register X-ray angiographic imaging techniques (such as IVUS and OCT) is added by means of the analysis module IV-LINQ.

In the newly added vFFR workflow the user can calculate the pressure drop and a vFFR value on coronary vessels. To obtain these values for a specific lesion in a coronary vessel, the user has to start with a QCA3D detection using two angiographic images. In each of these images a classic 2D coronary detection is performed after which a reconstruction of the coronary segment is obtained in 3D space. Based on the 3D reconstruction and the user input of systolic and diastolic aortic root blood pressure drop and the vFFR values can be assessed. The functionality is based on a combination of the QCA3D workflow available in predicate device CAAS Workstation (K151780) and technology available in the predicate device CAAS (K052988).

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. The functionality is independent of the type of vendor acquisition equipment.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state acceptance criteria in a quantitative table format for the new "quantification of pressure drop in coronary vessels" functionality. Instead, it describes a validation approach and comparative analysis.

However, based on the performance data section, we can infer the acceptance criteria for the new "quantification of pressure drop in coronary vessels" module were related to agreement with known pressure drops and improvement compared to the predicate device K052988.

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

• Test set for existing functions (regression testing): Not explicitly stated, but it's implied that a comprehensive set of test cases was used for regression testing to demonstrate equivalence.
• Test set for "quantification of pressure drop in coronary vessels": A "series of X-ray angiographic datasets with known pressure drops" was used. The exact number of datasets is not specified.
• Data Provenance: Not explicitly stated, but the mention of "known pressure drops" suggests these were either simulated datasets or pre-adjudicated clinical cases where pressure drops were definitively measured (e.g., using a reference standard). The document does not specify country of origin or whether it was retrospective or prospective.

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

• For existing functions: Not stated, as regression testing was used to compare against the previous version of the software.
• For "quantification of pressure drop in coronary vessels": The ground truth was based on "known pressure drops." The document does not specify if experts were involved in establishing these "known pressure drops" or what their qualifications would be. It's possible these were derived from a separate reference standard (e.g., invasive pressure wire measurements) or simulated data.

4. Adjudication Method

• For existing functions: Not applicable, as regression testing compared against the predicate device's output.
• For "quantification of pressure drop in coronary vessels": Not applicable, as the comparison was against "known pressure drops" rather than expert consensus on unknown cases.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, the document does not describe an MRMC comparative effectiveness study involving human readers with and without AI assistance. The performance evaluation focused on the standalone algorithm's accuracy and comparison to a previous device version.

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

• Yes, the performance evaluation for the "quantification of pressure drop in coronary vessels" was a standalone evaluation of the algorithm. The device calculated pressure drops, which were then compared to "known pressure drops."

7. The Type of Ground Truth Used

• For existing functions: The ground truth for regression testing would be the output of the predicate device (K151780).
• For "quantification of pressure drop in coronary vessels": The ground truth was "known pressure drops." As mentioned, this could refer to measurements from a highly accurate reference standard (e.g., invasive physiological measurements) or carefully constructed simulated data. It does not explicitly state "expert consensus," "pathology," or "outcomes data."

8. The Sample Size for the Training Set

• The document does not explicitly state the sample size used for the training set. The description in the "Performance Data" section refers to validation and verification, implying a test set, rather than a training set for model development. The device is described as an "Angiographic X-ray system," implying traditional software rather than a deep learning AI, though modern software often incorporates machine learning components that require training. Given the context, the "known pressure drops" dataset mentioned is very likely the test set used for validation.

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

• Since a training set size is not provided, the method for establishing its ground truth is also not elaborated upon in the provided text. If the device uses machine learning, information on its training set and ground truth establishment would typically be found in a more detailed technical report.

Ask a specific question about this device

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, co-registering 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 and catheter path 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;
• Co-registration of angiographic X-Ray images with IVUS and OCT images.

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 of the previously cleared predicate device CAAS Workstation (K133993) for calculating dimensions of coronary and peripheral vessels and the left and right ventricles, quantification of stenosis, performing density measurements, determination of optimal C-arm position for imaging of vessel segments and functionality to enhance the visualization of a stent and to measure stent dimension. Semi-automatic contour detection forms the basis for the analyses.

Functionality to co-register X-ray angiographic images and intravascular imaging techniques (such as intravascular ultrasound and optical coherence tomography) is added by means of the analysis module IV-LINQ. With co-registration a common frame of intravascular imaging techniques with X-ray angiographic images is provided using a three-dimensional model. This functionality is based on the Volcano Angio-IVUS Mapping system (K060483).

In the IV LINQ workflow the user has to select two angiographic X-ray images in DICOM format. The user indicates a catheter path starting at the imaging tip. This path can be optimized manually by adding, deleting or moving control point on the drawn path. After the catheter path is drawn in both angiographic X-ray images, a 3D reconstruction of the catheter path is calculated.

The user then has to select one IVUS or OCT dataset in DICOM format or the data is streamed from the intravascular imaging console with a DVI streamer. The IVUS or OCT pullback must be acquired using a motorized pullback device. After the 3D catheter path from X-ray angiographic images is calculated and the IVUS or OCT pullback is loaded, IV-LINQ co-registers each IVUS or OCT frame with a position on the 3D catheter path using a distance mapping algorithm. On intravascular images diameter and area measurements can be performed.

The quantitative results of 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.

The provided text describes the CAAS Workstation and its regulatory submission. It mentions performance data and validation efforts but does not provide explicit acceptance criteria in a table format, nor does it detail a specific study with quantitative results proving adherence to such criteria.

However, I can extract the information provided regarding the device's validation and testing:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not provide a table of acceptance criteria with corresponding performance metrics. Instead, it offers a general statement about performance:

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

The document does not specify the sample size used for any test sets, nor does it provide information on the data provenance (e.g., country of origin, retrospective or prospective) for training or testing.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

The document does not specify the number of experts used to establish ground truth or their qualifications for any part of the testing.

4. Adjudication Method for the Test Set:

The document does not mention any adjudication method used for a 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 being done, nor does it provide any effect size for human reader improvement with or without AI assistance.

6. Standalone Performance Study:

The document implies a standalone performance for the algorithm through statements like "System testing showed that the system requirements were implemented correctly" and "proper functioning of the algorithms is validated." However, it does not explicitly detail a dedicated standalone study with specific metrics. The focus is on the software's functionality and accuracy of its calculations.

7. Type of Ground Truth Used:

The document describes "validation approaches" and "proper functioning of the algorithms," and "accuracy and reproducibility requirements" for length measurements. This suggests the ground truth was likely established through:

• Reference measurements or calculations for quantitative aspects (e.g., vessel dimensions, stenosis quantification).
• Comparison to accepted standards or methods for qualitative aspects or algorithmic outputs.

However, the document does not explicitly state the specific type of ground truth used (e.g., expert consensus, pathology, outcomes data).

8. Sample Size for the Training Set:

The document does not provide any information regarding the sample size used for the training set.

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

As no training set size is provided, the document does not explain how ground truth for a training set was established.

Ask a specific question about this device

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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