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Clarius Ejection Fraction AI is intended for semi-automatic non-invasive measurement of the left ventricular ejection fraction on ultrasound data acquired by the Clarius Ultrasound Scanner (i.e., phased array and curvilinear scanners). The user shall be a healthcare professional trained and qualified in ultrasound. The user retains the responsibility of confirming the validity of the measurements based on standard practices and clinical judgment. Clarius Ejection Fraction AI is intended for use in adult patients only.

Clarius Ejection Fraction AI is a machine learning algorithm that is integrated into the Clarius App software as part of the complete Clarius Ultrasound Scanner system for use in cardiac ultrasound applications, specifically intended for use by trained healthcare practitioners for semi-automatic real-time measurement of the left ventricular (LV) ejection fraction (EF) on ultrasound image data acquired by the Clarius Ultrasound Scanner system (i.e., phased array and curvilinear scanners) using a deep learning image segmentation algorithm.

During the ultrasound imaging procedure, the anatomical site is selected through a preset software selection (i.e., Cardiac Basic, Cardiac Advanced) from the Clarius App in which Clarius Ejection Fraction AI will engage when enabled by the user to place a segmentation mask or landmark markers on the ultrasound image to identify the left ventricle (LV) in both End Diastolic (ED) and End Systolic (ES) phases. Using the segmentation volume or landmark markers in both phases, Clarius Ejection Fraction AI will calculate the EF of the cardiac images obtained in Parasternal Long Axis (PLAX), Parasternal Short Axis (PSAX), and Apical (AP4, AP2) views.

Clarius Ejection Fraction AI operates by performing the following tasks:
• Automatic capture of the ED and ES frames used to create the EF measurement
• Automatic calculations and measurements for the left ventricular ejection fraction.

The user has the option to manually adjust the measurements made by Clarius Ejection Fraction AI by moving the caliper crosshairs. Clarius Ejection Fraction AI does not perform any functions that could not be accomplished manually by a trained and qualified user.

Clarius Ejection Fraction AI is an assistive tool intended to inform clinical management and is not intended to replace clinical decision-making. The clinician retains the ultimate responsibility of ascertaining the measurements based on standard practices and clinical judgment. Clarius Ejection Fraction AI is indicated for use only in adult patients.

Clarius Ejection Fraction AI is integrated into the Clarius App software, which is compatible with iOS and Android operating systems two versions prior to the latest iOS or Android stable release build and is intended for use with the following Clarius Ultrasound Scanner system transducers (previously 510(k)-cleared in K213436 and K232704). Clarius Ejection Fraction AI is not a stand-alone software device.

Here's a breakdown of the acceptance criteria and study details for the Clarius Ejection Fraction AI device, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details

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

• Test Set Sample Size: 279 ultrasound exams (images of cardiac anatomy).
• Data Provenance: Retrospective analysis of anonymized ultrasound images obtained from a multi-center database.
  • Countries of Origin: Predominantly from the United States, but also includes data from Canada, Germany, Turkey, United Kingdom, Philippines, Australia, Italy, Sweden, Mexico, Belgium, Singapore, El Salvador, Lithuania, Norway, Venezuela, Malaysia, Switzerland, South Africa, Indonesia, Greece, Nigeria, New Zealand, Austria, Morocco, Iraq, South Korea, Jamaica, Israel, Taiwan, The Netherlands, Dominican Republic, Uganda, Ireland, Bahrain, and Vatican.
  • Retrospective/Prospective: Retrospective.

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

• Number of Experts: Not explicitly stated as a specific number, but referred to as "human experts/qualified ultrasound users" and "experienced ultrasound users." The ICC values (Table 3) compare "Reviewer1 vs. Reviewer2" and "Reviewer1 vs. Reviewer3" and "Reviewer2 vs. Reviewer3," implying at least three reviewers were involved in the ground truth establishment for the test set.
• Qualifications of Experts: "Experienced ultrasound users" and "qualified ultrasound users." No further specific details (e.g., number of years of experience, specific certifications) are provided in the excerpt.

4. Adjudication Method for the Test Set

• Adjudication Method: To aggregate measurements from different truthers, the mean of the three values was taken and was treated as one reviewer mean. This indicates a consensus approach among multiple reviewers.

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

• MRMC Comparative Effectiveness Study: Yes, a comparative study was conducted where the AI's performance was compared to that of human experts.
• Effect Size: The study focused on demonstrating non-inferiority rather than a direct improvement effect size in an MRMC setting where humans use the AI. The non-inferiority results (p-values and mean differences) indicate that the AI's measurements are statistically comparable to (not worse than) human expert measurements. The mean differences reported for the mean absolute difference are:
  • Apical: -6.27
  • PSAX: -3.87
  • PLAX: -5.92
    These values represent the mean difference between the AI's measurement and the mean reviewer measurement, within the equivalence margin of 10.

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

• Standalone Study: Yes, the primary objective of the clinical verification study was to evaluate the "Clarius Ejection Fraction AI measurements" against "mean reviewer measurements." This inherently describes the algorithm's standalone performance compared to human-derived ground truth. The human-in-the-loop aspect is described in the "Clinical Validation Study," which focused on usability and integration.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert Consensus. The ground truth measurements were established by "human experts/qualified ultrasound users" through their manual analysis and annotation of the ultrasound images, with the mean of their measurements taken as the consolidated ground truth.

8. Sample Size for the Training Set

• Training Set Sample Size: Not explicitly stated. The document mentions that the AI model was "developed and trained using three data sets: training, tuning, and internal testing" and that this data was "collected from the Clarius Cloud and/or partner clinics." However, no specific number of images or exams for the training set is provided.

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

• Ground Truth Establishment for the Training Set: The document states that the "internal test data was fully independent of the training/tuning dataset and was labelled by experts." While this specifically refers to the internal test set, it strongly implies that the training data and tuning (validation) data were also "labelled by experts." No further details on the number or qualifications of these experts, or the specific methodology for labeling, are provided for the training set.

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