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The iCardio.ai CardioVision™ AI is an automated machine learning–based decision support system, indicated as a diagnostic aid for patients undergoing an echocardiographic exam consisting of a single PLAX view in an outpatient environment, such as a primary care setting.

When utilized by an interpreting clinician, this device provides information that may be useful in detecting moderate or severe aortic stenosis. iCardio.ai CardioVision™ AI is indicated in adult populations over 21 years of age. Patient management decisions should not be made solely on the results of the iCardio.ai CardioVision™ AI analysis. iCardio.ai CardioVision™ AI analyzes a single cine-loop DICOM of the parasternal long axis (PLAX).

The iCardio.ai CardioVision™ AI is a standalone image analysis software developed by iCardio.ai Corporation, designed to assist in the review of echocardiography images. It is intended for adjunctive use with other physical vital sign parameters and patient information, but it is not intended to independently direct therapy. The device facilitates determining whether an echocardiographic exam is consistent with aortic stenosis (AS), by providing classification results that support clinical decision-making.

The iCardio.ai CardioVision™ AI takes as input a DICOM-compliant, partial or full echocardiogram study, which must include at least one parasternal long-axis (PLAX) view of the heart and at least one full cardiac cycle. The device uses a set of convolutional neural networks (CNNs) to analyze the image data and estimate the likelihood of moderate or severe aortic stenosis. The output consists of a binary classification of "none/mild" or "moderate/severe," indicating whether the echocardiogram is consistent with moderate or severe aortic stenosis. In cases where the image quality is insufficient, the device may output an "indeterminate" result.

The CNNs and their thresholds are fixed prior to validation and do not continuously learn during standalone testing. These models are coupled with pre- and post-processing functionalities, allowing the device to integrate seamlessly with pre-existing medical imaging workflows, including PACS, DICOM viewers, and imaging worklists. The iCardio.ai CardioVision™ AI is intended to be used as an aid in diagnosing AS, with the final diagnosis always made by an interpreting clinician, who should consider the patient's presentation, medical history, and additional diagnostic tests.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for CardioVision™:

Acceptance Criteria and Reported Device Performance

Note: The document explicitly states that the levels of sensitivity and specificity exceed the predefined success criteria and those of the predicate device, supporting the claim of substantial equivalence. While exact numerical thresholds for the acceptance criteria aren't provided in terms of specific values, the narrative confirms they were met.

Study Details

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iCardio.ai EchoMeasure is software that is used to process previously acquired DICOM-compliant cardiac ultrasound images, and to make measurements on these images in order to provide automated estimation of several cardiac measurements. The data produced by this software is intended to be used to support qualified cardiologists, sonographers, or other licensed professional healthcare practitioners for clinical decision-decision-making.

iCardio.ai EchoMeasure is indicated for use in adult patients.

iCardio.ai EchoMeasure is a software device used to process previously acquired DICOM-compliant transthoracic cardiac ultrasound images. The software provides automated view classification and quality check of images to then provide several automated estimation of cardiac anatomical measurements and quantities.

iCardio.ai EchoMeasure is a comprehensive software application that seamlessly integrates image pre-processing and quality check of standard cardiac ultrasound views and provides automated measurements of standard cardiac parameters and measurements.

iCardio.ai EchoMeasure is designed to sort through and determine the eligibility criteria for downstream processing, including image quality, and appropriate cardiac view. The following pre-processing steps are considered in making a determination about image eligibility for processing:

• Echocardiographic view classification
• Echocardiographic view overall image quality -
• -End-Diastolic and End-Systolic frame identification

iCardio.ai EchoMeasure automatically sorts through and recognizes these key parameters to then allow an image to pass for automated processing for measurement of several cardiac parameters, including:

• 1. Left Ventricular Volume (A2C, A4C, and Biplane; Systole and Diastole)
• 2. Left Ventricular Diameter (Systole and Diastole)
• 3. Right Ventricular Diameter
• 4. Posterior Wall Thickness
• 5. Aortic Annulus Diameter
• 6. Left Ventricular Outflow Tract Diameter
• 7. Sinus of Valsalva Diameter
• 8. Sinotubular Junction Diameter
• 9. Left Atrium Dimension
• 10. Interventricular Septal Thickness

Machine learning based view detection, quality grading, key frame selection, automated keypoint detection and segmentation form the basis of the software's automated analysis.

iCardio.ai EchoMeasure output is intended for consumption by 3rd party software and hardware vendors. Additionally iCardio.ai has a native browser interface for reviewing the report summary as well as a functionality to download the available report in PDF format. The iCardio.ai EchoMeasure browser interface allows the end user to view both 2D image and cine loops determined by the software and to review the automated measurements produced. It is the option of the reviewing clinician to accept, reject, edit, or ignore the output provided by iCardio.ai EchoMeasure.

A report, automatically generated from the calculated parameters, is returned to the interpreting clinician. This software device aims to aid diagnostic review and analysis of echocardiographic data, patient record management, and reporting. It also features tools for organizing and displaying quantitative data from cardiovascular images acquired from ultrasound scanners. It is exclusively for use by qualified clinicians.

Here's an analysis of the acceptance criteria and study detailed in the provided text:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria & Reported Device Performance

The acceptance criteria for iCardio.ai EchoMeasure's performance were based on the Bi-variate Linear Regression Coefficient Slope (BLRSC). The device was designed to estimate the "worst-case" error, defined as the difference between the software output and the mean of three clinician-derived annotations. The acceptance criterion was that the estimated worst-case BLRSC (based on the 95% CI) for each endpoint must be above a certain predetermined threshold. The study's conclusion explicitly states that "In no instance did the worst-case BLRSC for a given measurement (calculated based on the 95% confidence interval) fall below the predetermined, minimum allowable BLRSC threshold."

2. Sample Size for Test Set and Data Provenance

• Sample Size: 200 comprehensive echocardiography studies from 200 distinct patients. A single DICOM was selected for each relevant view (PLAX, A2C, or A4C).
• Data Provenance: Retrospective, sampled from two independent clinical sites from two different US states. This was done to assure a wide sample of imaging data and patient demographics. No data from these sites was used for the training or tuning of the algorithm.

3. Number of Experts and Qualifications for Ground Truth (Test Set)

• Number of Experts: Three (3)
• Qualifications: Experienced US-based cardiac sonographers.

4. Adjudication Method for Test Set
The ground truth was established using the mean of three (3) clinician-derived annotations per case. This implies a consensus-based approach or averaging of independent expert measurements.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The provided text does not mention a multi-reader multi-case (MRMC) comparative effectiveness study to assess the effect of AI assistance on human reader performance. The study described is a standalone performance study.

6. Standalone Performance Study
Yes, a standalone performance study was conducted. The objective was to demonstrate successful device performance using prospectively-defined success criteria for each endpoint, specifically evaluating the "worst-case" error for linear and volumetric measurements against clinician-derived ground truth.

7. Type of Ground Truth Used
The ground truth used was expert consensus based on manual measurements and segmentations performed by experienced clinicians (the mean of three experienced US-based cardiac sonographers).

8. Sample Size for Training Set
The text does not specify the sample size for the training set. It only mentions that the sonographers used for the standalone study were independent of those used to annotate the training data, and that data from the two clinical sites used for the test set was not used for training or tuning.

9. How Ground Truth for Training Set was Established
The text does not explicitly detail how the ground truth for the training set was established, other than noting that different sonographers were involved compared to the test set ground truth establishment.

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