(265 days)
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."
| Criterion Type | Acceptance Criterion (as described) | Reported Device Performance (as described) |
|---|---|---|
| Numerical Results | Comparability to predicate device (K180019) for StentEnhancer and vFFR workflows. | "Numerical results...were comparable" to the predicate. |
| Safety & Effectiveness | As safe and effective as predicate device (K180019). | Demonstrated through verification and validation results. |
| Usability | Conformance to IEC 62366-1 standard. | User is able to use CAAS Workstation for its purpose. |
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.
§ 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.