(22 days)
Detect the contour of the coronary vessel from a set of angiographic X-ray images – Generate absolute measurements about the dimensions of the coronary arterial segment in 3D space to improve accuracy by elimination of out-of-plane and foreshortening errors.
The CAAS OCA 3D is one of the software modules intended to run on the Cardiovascular Angiography Analysis System mark. CAAS. It functions in the same manner as other vascular analysis software packages. The OCA-3D module allows accurate and reproducible quantification of the coronary arteries from a set of angiographic X-ray images. The analyst selects between 2 and 5 angiographic images obtained from different X-Ray projections. On each of the images a classic 2D arterial detection is performed, after which from all images a reconstruction of the arterial segment is obtained in 3D space. Indication of a common point in each of the images is used to obtain an exact spatial relationship between the images. 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. Three analysis methods are available: The first method is an automatic reconstruction of the diseased arterial vessel by means of computing the reference along the arterial vessel to reconstruct the healthy arterial vessel, calculation of main result the % of stenosis. The second method allows for manual definition of the reference along the arterial segment by means of selecting one or more reference positions in the arterial segment, calculation of main result the % of stenosis. The third analysis method enables the user to define one or more subsegments, within each user defined subsegment the minimum, maximum and mean area are calculated. Besides area information also diameter results for each image used to reconstruct the vessel into 3D space are calculated over the arterial positions of interest. These diameter results will be corrected for out of plane calibration and length measurements will be corrected for foreshortening errors.
I am sorry, but the provided text does not contain sufficiently detailed information to complete all sections of your request. The document is primarily a 510(k) summary for the CAAS QCA 3D software package, establishing its substantial equivalence to a predicate device. It describes the device's functionality and intended use but does not present a specific study with acceptance criteria and detailed performance metrics in the format you've requested.
Here's an attempt to answer the questions based on the available information:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state quantitative acceptance criteria or report specific performance metrics for the CAAS QCA 3D device. It mentions that "The CAAS QCA 3D software produces similar results as the predicate device" (K052988), implying that its performance is implicitly accepted if it matches the predicate. The focus is on its ability to detect contours and generate measurements for coronary arterial segments, correcting for out-of-plane and foreshortening errors.
Since specific criteria and performance values are not provided, I cannot generate this table.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not explicitly stated in the provided text. The document refers to its functionality with "a set of angiographic X-ray images" but does not detail any specific test set used for performance evaluation or its provenance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
This information is not provided in the document.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided in the document.
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
A MRMC comparative effectiveness study is not mentioned. The document describes a software package for quantitative analysis, not an AI-assisted interpretation tool for human readers in the context of improving their performance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document describes the CAAS QCA 3D as "one of the software modules intended to run on the Cardiovascular Angiography Analysis System mark. CAAS." It performs "automatic detection" and "automatic reconstruction," implying standalone algorithmic processing. However, it also mentions the "analyst selects" images and arterial segments, suggesting a human-in-the-loop workflow where the software assists the user in quantification. No specific "standalone" performance study is detailed with metrics.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
This information is not provided. The document focuses on "automatic reconstruction of the diseased arterial vessel by means of computing the reference along the arterial vessel to reconstruct the healthy arterial vessel" and contour detection. The implicitly accepted ground truth would likely be established through validated methods for quantitative coronary analysis, potentially comparing against other established QCA systems or phantom studies, but this is not detailed.
8. The sample size for the training set
The document does not mention a training set, as it emphasizes that the device consists of "reused algorithms with the addition of several improvements that do not influence the indications for use." This suggests the algorithms were developed and validated prior to this submission, possibly without a distinct "training set" in the context of this specific 510(k).
9. How the ground truth for the training set was established
As no training set is mentioned for this specific submission, its ground truth establishment is not discussed.
§ 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).