(83 days)
- Optimizing the quantitation of artery dimensions to be used in clinical trials and in clinical cath lab 1. environment
-
- Managing of data resulting of the analysis of artery dimensions
The CAAS II QVA is one of the software modules intended to run on the Cardiovascular Angiography Analysis System mark II, CAAS II. It functions in the same manner as other vascular analysis software packages. 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. Two methods for obstruction analysis are available, one with automatic reconstruction of the arterial wall to estimate the normal diameter or reference diameter for the obstruction, calculation of the MLD and % stenosis. The second method allows for manual selection of one or more reference position in the arterial segment and based on the MLD and this calculated reference for the position of the MLD the % stenosis is calculated. In the arterial segment under study one or more subsegments can be selected by the user and for all these user defined subsegments the minimum, maximum and mean diameter are calculated. Besides diameter information also cross sectional area is calculated over the arterial positions of interest. These cross sectional areas are calculated based on both circular symmetry of the artery and densitometric analysis of the contrast volume in the artery. The QVA package can be used on arteries up to 50mm in diameter.
Here's an analysis of the provided text regarding the CAAS II QVA device's acceptance criteria and studies:
Acceptance Criteria and Device Performance:
The provided document, a 510(k) summary for the CAAS II QVA, primarily focuses on establishing substantial equivalence to a predicate device (CAAS II QCA, K945540). It does not explicitly state specific quantitative acceptance criteria for clinical performance metrics (e.g., accuracy, precision) in the way a performance study report typically would. Instead, the acceptance is based on demonstrating that the device functions in a "similar manner" and produces "similar results" to the predicate.
Given this, the table below reflects what can be inferred or directly stated about the device's performance in relation to its intended function and equivalence.
| Acceptance Criteria (Inferred from Equivalence Claim) | Reported Device Performance (as stated in the 510(k) Summary) |
|---|---|
| Performs quantitative vascular analysis | Automatically detects arterial segment contours. Calculates MLD, % stenosis, minimum, maximum, and mean diameter, and cross-sectional area. |
| Produces results similar to predicate devices | "The CAAS II QVA software produces similar results as the predicate devices." (No specific quantitative metrics for "similar" are provided.) |
| Functions similarly to predicate devices | "The automatic contour detection of the CAAS II QVA software is similar to the contour detection algorithms used in the predicate devices." |
| Capable of analyzing arteries up to 50mm in diameter | "The QVA package can be used on arteries up to 50mm in diameter." |
| Optimizes quantitation of artery dimensions | Intended use: "Optimizing the quantitation of artery dimensions to be used in clinical trials and in clinical cath lab environment." (Implies effective performance) |
| Manages data from artery dimension analysis | Intended use: "Managing of data resulting of the analysis of artery dimensions." (Implies functional data management) |
It's important to note that without a detailed performance study abstract or report, such as might be found in a full 510(k) submission, it's impossible to provide specific numerical acceptance criteria or detailed performance metrics. The information here strongly indicates a reliance on the predicate device's established performance.
Study Information:
The provided 510(k) summary does not describe a detailed, standalone clinical or technical study with specific sample sizes, ground truth establishment, or expert adjudication as one might expect for a de novo device or a more complex submission. The basis for substantial equivalence is primarily through comparison to a predicate device (CAAS II QCA, K945540) based on technological characteristics and functional claims.
Therefore, many of the requested fields cannot be filled with specific information from this document.
-
Sample size used for the test set and the data provenance:
- Test set sample size: Not specified. The document states the device "produces similar results as the predicate devices," implying some form of comparison, but details of a specific test set are not provided.
- Data provenance: Not specified.
-
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not specified. There is no mention of a formal expert panel establishing ground truth for a test set.
-
Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Not specified.
-
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:
- No MRMC study is described in this summary. The device is a QVA software, implying an automated analysis tool rather than an AI-assisted interpretation tool for human readers in the context of improving human performance.
-
If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
- The document implies standalone performance comparison to the predicate device: "The automatic contour detection of the CAAS II QVA software is similar to the contour detection algorithms used in the predicate devices." and "The CAAS II QVA software produces similar results as the predicate devices." However, no formal study design for standalone performance is detailed. The device itself performs "quantitative vascular analysis" without explicitly stating a human-in-the-loop component for its primary analysis functions (though user selection of segments is mentioned).
-
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Not specified. For QVA devices, ground truth often involves phantom studies, in vitro measurements, or invasive angiography with highly experienced readers, but this document does not elaborate.
-
The sample size for the training set:
- Not applicable/Not specified. This document pertains to a 510(k) submission for a device without explicit mention of deep learning or machine learning where a distinct "training set" would be a primary focus. While algorithms are involved, the description doesn't frame it in terms of AI training.
-
How the ground truth for the training set was established:
- Not applicable/Not specified. (See point 7).
Summary of Study Information Gaps:
The provided 510(k) summary is very high-level and focused on demonstrating substantial equivalence to a predicate device. It lacks the detailed study methodology, performance metrics, and validation procedures that would typically be found in a comprehensive clinical or technical study report. The acceptance is based on the claim of similarity to a previously cleared device, rather than new, independent performance validation data detailed within this specific document.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).