(121 days)
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.
| Acceptance Criteria (Inferred) | Reported Device Performance (Quantified/Qualitative) |
|---|---|
| For existing functions (from K151780): Equivalence in numerical results as demonstrated by regression testing. | Demonstrated with regression testing for equivalence in numerical results. |
| For new "quantification of pressure drop in coronary vessels" (from K052988 with 3D reconstruction): Agreement between calculated pressure drops and known pressure drops. | Differences (mean and standard deviation) of the calculated pressure drops with respect to known pressure drops of the used datasets were calculated. A Pearson correlation between the known and calculated pressure drop values was also performed. |
| For new "quantification of pressure drop in coronary vessels": Improvement compared to predicate device K052988. | "This demonstrated that the quantification of pressure drop in coronary vessels in the new CAAS Workstation is improved compared to the predicate device K052988." (No specific quantitative metric for improvement is provided in the document). |
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.
§ 892.1600 Angiographic x-ray system.
(a)
Identification. An angiographic x-ray system is a device intended for radiologic visualization of the heart, blood vessels, or lymphatic system during or after injection of a contrast medium. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.