Search Results

AI4CMR software is designed to report cardiac function measurements (ventricle volumes, ejection fraction, indices, etc.) from 1.5T and 3T magnetic resonance (MR) scanners. AI4CMR uses artificial intelligence to automatically segment and quantify the different cardiac measurements. Its results are not intended to be used on a stand-alone basis for clinical decision-making. The user incorporating AI4CMR into their DICOM application of choice is responsible for implementing a user interface.

AI4CMR also supports clinical diagnostics by calculating flow measurements of vascular structures in 2D phase-encoded cardiac MR images.

AI4CMR v2.0 is a cloud-based solution designed to integrate to any third-party DICOM viewer application where the DICOM viewer serves as the user interface and the interface to a PACS or scanner for AI4CMR. AI4CMR is implemented as a plug-in to the DICOM viewer by the user and automatically processes and analyses cardiac MR images received by the DICOM viewer to quantify relevant cardiac function metrics as well as analytical flow quantification and makes the information available to the user at the user's discretion.

The following are the cardiac function metrics quantified and reported by the software:

Quantitative Analysis

The subject device performs the following:

Cardiac function measurements in cine sequences

• Anatomy and tissue segmentation
• LV/RV stroke volume
• LV/RV cardiac output
• LV/RV ejection fraction
• LV/RV end-diastolic volume
• LV/RV end-systolic volume

Analytical flow quantification in 2D Phase-Contrast Sequences (2DFlow)

• Total Forward/Backward Net Volumes
• Regurgitation Fraction
• Effective Volume per Minute
• Maximum Velocity
• Pressure Gradient
• Flow values

Reporting

The subject device enables the following metrics to be reported as desired by the user:

Cardiac function measurements

• LV/RV stroke volume
• LV/RV cardiac output
• LV/RV ejection fraction
• LV/RV end-diastolic volume
• LV/RV end-systolic volume
• LV myocardial mass
• LV/RV end-systolic frame
• LV/RV end-diastolic frame
• LV/RV end-systolic volume index
• LV/RV end-diastolic volume index
• LV/RV stroke volume index
• Myocardium mass index
• Cardiac index

Analytical flow measurements (2DFlow)

• Total Forward Volume
• Total Backward Volume
• Total Volume
• Total Net Positive Volume
• Total Net Negative Volume
• Regurgitation Fraction
• Volume per Minute
• Effective Volume per Minute
• Maximum Pressure Gradient
• Maximum Velocity
• Minimum Velocity
• Maximum Mean Velocity
• Maximum Flow
• Minimum Flow

Here's an analysis of the acceptance criteria and study detailed in the provided FDA 510(k) clearance letter for AI4CMR v2.0, structured according to your requirements:

Acceptance Criteria and Study Details for AI4CMR v2.0

1. Table of Acceptance Criteria and Reported Device Performance

The primary acceptance criterion mentioned explicitly for the AI-based 2D Flow Segmentation model is based on the Dice Similarity Coefficient (DSC) for vessel segmentation.

Note on ICC values: While high ICC values (e.g., 0.95) typically indicate excellent agreement, and 0.82 indicates substantial agreement, the document does not explicitly state acceptance thresholds for these metrics with the reference device. The statement "demonstrated consistent agreement" and supporting the claim of substantial equivalence implies these values met an internal, unstated acceptance level.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for AI-based 2D Flow Segmentation (Independent Test Set): Approximately 15% of the subjects from the development dataset. The total development dataset comprised 167 cardiac MR cases from 61 adult subjects. While not directly stated, applying 15% to 61 subjects would yield roughly 9-10 subjects in the independent test set. The document also states that 296 vessel samples (ascending aorta, descending aorta, and pulmonary artery) were included in total, and these were stratified by vessel type and split at the subject level.
• Data Provenance: Retrospective clinical data from a tertiary Western European hospital.
• Data Acquisition: Images were acquired using standard cardiac MR 2D phase-contrast flow imaging protocols on 1.5T and 3T scanners. The dataset included both normal and pathological cases.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: One expert reader.
• Qualifications: EACVI level III. (The European Association of Cardiovascular Imaging - EACVI - provides certification levels for expertise in cardiovascular imaging, with Level III indicating high-level expertise).

4. Adjudication Method for the Test Set

The document states, "An expert reader (EACVI level III) independently annotated all cases." This implies a single reader ground truth with no explicit adjudication method (e.g., 2+1 or 3+1). The text mentions "intra-reader reliability maintained by following established standards," suggesting the expert's consistency was ensured, but not through external adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study was mentioned. The study focused on the standalone performance of the AI model and its agreement with a legally marketed reference device, not on how human readers improve with AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone performance study was done for the AI-based 2D Flow Segmentation model. The reported Dice Similarity Coefficient (DSC), Precision, and Recall values are all measures of the algorithm's standalone performance compared to the expert-derived ground truth.

Separately, the agreement of the device's flow measurements (TFV, TBV, Vmax) with a legally marketed reference device (cvi42) also reflects standalone algorithm performance against another established algorithm.

7. Type of Ground Truth Used

The ground truth used for the AI-based 2D Flow Segmentation was expert consensus / expert manual annotations. Specifically, one EACVI level III expert independently annotated all cases using standard segmentation guidelines.

8. Sample Size for the Training Set

The development dataset comprised 167 cardiac MR cases from 61 adult subjects. This dataset was split into training, validation, and independent test sets at a 70%/15%/15% ratio (of subjects). Therefore, the training set would include approximately 70% of 61 subjects, which is about 42-43 subjects. All vessels and repeated acquisitions from these subjects were assigned to the training set.

9. How the Ground Truth for the Training Set Was Established

The ground truth for the training set was established in the same manner as the ground truth for the test set: through the independent annotations of a single EACVI level III expert. The document states, "An expert reader (EACVI level III) independently annotated all cases using standard segmentation guidelines to ensure consistency and algorithm generalizability, with intra-reader reliability maintained by following established standards." This process applied to the entire dataset before it was split into training, validation, and test sets.

Ask a specific question about this device