(291 days)
AI-Rad Companion (Cardiovascular) is image processing software that provides quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice in the evaluation and assessment of cardiovascular diseases.
It provides the following functionality:
- · Segmentation and volume measurement of the heart
- · Quantification of the total calcium volume in the coronary arteries
- Segmentation of the aorta
- · Measurement of maximum diameters of the aorta at typical landmarks
- · Threshold-based highlighting of enlarged diameters
The software has been validated for non-cardiac chest CT data with filtered backprojection reconstruction from Siemens Healthineers, GE Healthcare, Philips, and Toshiba/Canon. Additionally, the calcium detection feature has been validated on non-cardiac chest CT data with iterative reconstruction from Siemens Healthineers.
Only DICOM images of adult patients are considered to be valid input.
In general, AI-Rad Companion (Cardiovascular) is a software only image post-processing application that uses deep learning algorithms to post-process CT data of the thorax.
The subject device AI-Rad Companion (Cardiovascular) is an image processing software that utilizes deep learning algorithms to provide quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice in the evaluation and assessment of disease of the thorax. The subject device supports the following device specific functionality:
- Segmentation and volume measurement of heart
- . Identification and measurement of volume with high Hounsfield values -- related to coronary calcification
- . Segmentation of the aorta and determination of 9 Landmarks
- . Computation of cross-sectional MPRs at the 9 landmarks and their maximum diameter
- Measurement of maximum diameters of the aorta at typical landmarks ●
- Threshold-based classification of diameters into different categories ●
Here's an analysis of the acceptance criteria and study details for the AI-Rad Companion (Cardiovascular) device, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document doesn't explicitly present a formal "acceptance criteria" table with specific pass/fail thresholds for each metric. Instead, it reports performance results (sometimes implicitly as "equivalent" or "consistent") that are presumably within acceptable limits for FDA clearance given the substantial equivalence claim.
| Feature / Metric | Acceptance Criteria (Implicitly Met) | Reported Device Performance and Confidence Intervals |
|---|---|---|
| Coronary Calcium Volume Quantification | Performance equivalent to predicate device | Logarithmic correlation coefficient of total coronary calcium volume between subject and predicate device was 0.96 (N=381). |
| Aorta Diameter Measurements (Average Absolute Error) | Performance consistent across critical subgroups and within acceptable limits | Average absolute error in aorta diameters was 1.6 mm (95% confidence interval: [1.5 mm, 1.7 mm]) across all nine measurement locations. |
| Aorta Diameter Measurements (Per Location) | Performance consistent across critical subgroups and within acceptable limits | Varied between 0.9 mm and 2.4 mm per location (N=193). |
| Consistency across Subgroups | Performance consistent for critical subgroups (vendors, slice thickness) | Performance was consistent for all critical subgroups, such as vendors or slice thickness. |
| Software Functionality | All software specifications met acceptance criteria | All testable requirements in the Engineering Requirements Specifications keys, Subsystem Requirements Specifications keys, and the Risk Management Hazard keys have been successfully verified and traced. Testing results support that all software specifications have met the acceptance criteria. |
| Risk Management | Identified hazards are mitigated | Risk analysis completed and risk control implemented to mitigate identified hazards. |
| Human Factors Usability | Human factors addressed and acceptable for safe and effective use | Human Factor Usability Validation showed that Human factors are addressed in the system test and in clinical use tests with customer reports and feedback. |
2. Sample Sizes and Data Provenance
- Test Set Sample Sizes:
- Coronary Calcium Volume: N = 381 data sets.
- Aorta Diameter Measurements: N = 193 data sets.
- Data Provenance: Retrospective performance studies on non-cardiac chest CT data from multiple clinical sites across the United States.
3. Number of Experts and Qualifications
The document does not explicitly state the "number of experts" or their specific "qualifications" used to establish ground truth. However, for the training set, it mentions "Description of ground truth / annotations generation," implying expert involvement. For the validation set, the comparison is primarily against a "predicate device," suggesting that the ground truth for that comparison would have been established previously for the predicate, not necessarily by new experts for this study.
4. Adjudication Method for the Test Set
The document does not specify any adjudication method (e.g., 2+1, 3+1) for the test set's ground truth. The comparison seems to be against the predicate device's output, which would have its own established ground truth based on its clearance.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No, an MRMC comparative effectiveness study was not explicitly described in the provided text. The study focuses on the standalone performance of the AI device in comparison to a predicate device, not on how human readers improve with or without AI assistance.
6. Standalone Performance Study (Algorithm Only)
- Yes, a standalone performance study was done. The document states: "The performance of the AI-Rad Companion (Cardiovascular) device has been validated in retrospective performance studies..." and details the algorithm's performance metrics (correlation coefficient for calcium, absolute error for aorta diameters) against "predicate device" or implied ground truth, indicating algorithm-only performance.
7. Type of Ground Truth Used
The ground truth for the validation studies appears to be based on:
- Comparison to a predicate device's output: For coronary calcium volume quantification, the performance is reported as a correlation between the subject device and the predicate device.
- Likely expert-derived measurements previously established or derived from the predicate device's method: For aorta diameter measurements, the "average absolute error" suggests comparison against a reference "true" measurement, which would typically be derived by experts using the predicate's methodology, or a gold standard measurement. The mention of "AHA standard" for diameter categorization also points to established clinical guidelines as a reference.
8. Sample Size for the Training Set
The document states: "Training cohort: size and properties of data used for training O". However, it does not explicitly provide the numerical sample size for the training set.
9. How the Ground Truth for the Training Set Was Established
The document briefly mentions under "Data" for each algorithm analysis: "Description of ground truth / annotations generation O". This implies that ground truth was established, likely through expert annotation or a similar process, but does not provide specific details on the methodology (e.g., number of annotators, their qualifications, consensus process) for the training set's ground truth.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.