(183 days)
AVIEW CAC provides quantitative analysis of calcified plaques in the coronary arteries using non-contrast non-gated Chest CT scans. It enables the calculation of the Agatston score for coronary artery calcification, segmenting and evaluating the right coronary artery and left coronary artery. Also provide risk stratification based on calcium score, gender, and age, offering percentile-based risk categories by established guidelines. Designed for healthcare professionals, including radiologists and cardiologists, AVIEW CAC supports storing, inquiring, and displaying CT data sets on-premises, facilitating access through mobile devices and Chrome browsers. AVIEW CAC analyzes existing noncontrast/non-gated Chest CT studies that include the heart of adult patients above the age of 40. Also, the device's use should be limited to CT scans acquired on General Electric (GE) or its subsidiaries (e.g., GE Healthcare) equipment. Use of the device with CT scans from other manufacturers has not been validated or recommended.
The AVIEW CAC is a software product that can be installed on a PC. It shows images taken with the interface from various storage devices using DICOM 3.0, the digital image and communication standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-processing, saving, and sending images by using software tools. And is intended for use as a quantitative analysis of CT scanning. It also provides a calcium score by automatically analyzing coronary arteries from the segmented arteries.
The provided text describes the acceptance criteria and the study conducted for the AVIEW CAC device.
Here's the breakdown of the information requested:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria for the quantitative analysis of calcified plaques is primarily based on the Intraclass Correlation Coefficient (ICC) of the Agatston score against a ground truth and a predicate device.
| Acceptance Criteria | Reported Device Performance (AVIEW CAC vs. Ground Truth) | Reported Device Performance (AVIEW CAC vs. Predicate Device) |
|---|---|---|
| P-value > 0.8 for ICC (implied target for strong agreement) | Agatston Score ICC (95% CI): | Agatston Score ICC (95% CI): |
| Total: 0.896 (0.857, 0.925) | Total: 0.939 (0.916, 0.956) | |
| LCA: 0.927 (0.899, 0.947) | LCA: 0.955 (0.938, 0.968) | |
| RCA: 0.840 (0.778, 0.884) | RCA: 0.887 (0.844, 0.918) | |
| **All p-values 0.8", which usually signifies a strong correlation. The reported ICC values are all above this implied threshold. |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size:
- 150 CSCT (gated) cases
- 150 Chest CT (non-gated) cases
- Additionally, 280 datasets collected from multiple institutions were used for a separate "MI functionality test report" which also evaluated correlation.
- Data Provenance: The document does not explicitly state the country of origin. The test cases were derived from "multiple institutions". It is implied to be retrospective as the device analyzes "existing" non-contrast/non-gated chest CT studies.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not specify the number of experts used or their detailed qualifications (e.g., "radiologist with 10 years of experience") for establishing the ground truth.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method (such as 2+1, 3+1) for establishing the ground truth. It simply refers to "ground truth" without detailing its consensus process.
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 Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study focuses on the standalone performance of the algorithm against a defined ground truth and comparison against a predicate device, not on human reader performance with or without AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, a standalone performance study was done. The performance data section explicitly states, "we evaluated the agreement in A coronary calcium scoring between the subject device and the ground truth" and "the correlation coefficient A between the AVIEW CAC automatic analysis results of the chest CT based on the heart CT and the Agatston scores was over 90%". This indicates the algorithm's performance without human intervention.
7. The Type of Ground Truth Used
The ground truth used was Agatston scores for coronary artery calcification. The document does not specify if this ground truth was established by expert consensus of human readers, pathology, or outcomes data. However, the comparison is made to "Ground Truth" for Agatston Score measurements, which implies a highly reliable, perhaps manually derived or reference Agatston score.
8. The Sample Size for the Training Set
The document does not provide the sample size for the training set. It only mentions the test set sizes.
9. How the Ground Truth for the Training Set Was Established
The document does not describe how the ground truth for the training set was established. It only refers to deep learning for automatic segmentation but does not detail the process for creating the ground truth data used to train the deep learning model.
§ 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).