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Us2.ca processes acquired transthoracic cardiac ultrasound images to support qualified cardiologists, sonographers, or other licensed professional healthcare practitioners in their diagnosis of cardiac amyloidosis. Us2.ca is intended for use only in adult patients with increased left ventricular wall thickness, defined as an interventricular septal thickness (IVSd) or left ventricular posterior wall thickness (LVPWd) ≥ 12mm. Us2.ca is not intended to provide a diagnosis and does not replace current standards of care. The results from Us2.ca are not intended to exclude the need for further follow-up on cardiac amyloidosis.

The Us2.ai platform is a clinical decision support tool that analyzes echocardiogram images in order to generate a series of AI-derived measurements. Fully automated, functional reporting with disease indications is also provided, in line with ASE & ESC guidelines. Echo images are sent to the Us2.ai platform where they are processed, analyzed and measured. Results that meet the confidence threshold for both image quality and measurement accuracy are passed through to a report for review by the clinical users. Report text is also generated and presented with the measurements, providing functional reporting and disease indications. The ultimate clinical decision and interpretation reside solely with the clinician. Us2.ca is an enhancement to Us2.ai existing Us2.v2 software, adding the capability to detect cardiac amyloidosis. It is an image post-processing analysis software device used for viewing and quantifying cardiovascular ultrasound images in DICOM format. The device is intended to aid diagnostic review and analysis of echocardiographic data, patient record management and reporting. The primary intended function of Us2.ca is to automatically identify patients who require additional follow-up for cardiac amyloidosis. In doing so, the primary benefit is to improve clinical echocardiographic workflow, enabling clinicians to generate and edit reports faster, with precision and with full control. The final clinical decision of the results still remains with the clinicians.

Here's a breakdown of the acceptance criteria and the study proving Us2.ca meets them, based on the provided FDA 510(k) Clearance Letter:

Us2.ca Device Performance Study Summary

Us2.ca is an AI-powered software designed to analyze transthoracic cardiac ultrasound images to support healthcare practitioners in the diagnosis of cardiac amyloidosis in adult patients with increased left ventricular wall thickness (IVSd or LVPWd ≥ 12mm). The device is not intended as a standalone diagnostic tool but as an adjunctive clinical decision support system.

1. Acceptance Criteria and Reported Device Performance

The primary performance metrics for Us2.ca were sensitivity and specificity for the detection of cardiac amyloidosis. The benchmarks for acceptance criteria were established with reference to current standards of care and existing relevant publications.

Table of Acceptance Criteria and Reported Device Performance

Note: The document states "The benchmark used in deriving the acceptance criteria of Us2.ca was made with reference to current standards of care and existing relevant publications." However, explicit numerical acceptance thresholds for sensitivity and specificity are not provided in the excerpt. The reported performance metrics are presented as the results that met the unstated acceptance criteria.

2. Sample Sizes and Data Provenance

• Training Set Sample Size: 4,371 patients (2,241 CA Cases, 2,130 Control Cases)
• Test Set (External Validation) Sample Size: 1,647 patients (664 CA Cases, 983 Control Cases)
• Data Provenance:
  • Country of Origin: The external validation cohort was sourced from six clinical sites across the United States (USA) and Japan. The training data came from "entirely separate data providers," implying diverse origins as well.
  • Retrospective or Prospective: All echocardiographic studies were retrospectively obtained from routine clinical evaluations.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") used to establish the ground truth for the test set. However, it indicates that the device "supports qualified cardiologists, sonographers, or other licensed professional healthcare practitioners in their diagnosis of cardiac amyloidosis," implying that the ground truth would have been established by such qualified professionals.

4. Adjudication Method for the Test Set

The document does not describe the specific adjudication method (e.g., 2+1, 3+1) used for establishing the ground truth of the test set. It mentions the "testing data involved two cohorts: Cardiac Amyloidosis Group (CA Group) and Control Group," but not the process for classifying patients into these groups.

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

The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to assess how human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the Us2.ca algorithm.

6. Standalone (Algorithm Only) Performance

Yes, a standalone performance study was conducted. The reported sensitivity of 86.9% and specificity of 87.4% are results of the Us2.ca algorithm's performance on the test set, without human intervention or assistance during the evaluation phase. The overall yield of 87.1% also reflects the algorithm's ability to generate confident predictions.

7. Type of Ground Truth Used

The type of ground truth used was expert consensus / clinical diagnosis implicitly. Patients were categorized into a "Cardiac Amyloidosis Group (CA Group)" and "Control Group," indicating that established clinical diagnoses of cardiac amyloidosis (or lack thereof) were used as the reference standard. The "diagnosis of cardiac amyloidosis" is the target of the device's support to "qualified cardiologists, sonographers, or other licensed professional healthcare practitioners."

8. Sample Size for the Training Set

The sample size for the training set was 4,371 patients.

9. How Ground Truth for the Training Set Was Established

The document states that the training and external validation datasets were "sourced from entirely separate data providers." While it doesn't explicitly detail the methodology for establishing ground truth for the training set, it can be inferred that it followed similar clinical diagnostic processes as the test set, leading to the classification of "CA Cases" and "Control Cases." This would typically involve clinical evaluation, imaging interpretation by experts, and potentially confirmatory tests as standard clinical practice for cardiac amyloidosis diagnosis.

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Us2.v2 software is used to process acquired transthoracic cardiac ultrasound images, to analyze and make measurements on images in order to provide automated estimation of several cardiac structural parameters, including left right atrial and ventricular linear dimensions, volumes, systolic function, measured by B mode, M mode and Doppler (PW, CW, tissue) modalities. 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-making. Us2.v2 is indicated for use in adult patients.

Us2.v2 is an image post-processing analysis software device used for viewing and quantifying cardiovascular ultrasound images in DICOM format. The device is intended to aid diagnostic review and analysis of echocardiographic data, patient record management and reporting. The primary intended function of Us2.v2 is to automatically provide clinically relevant and reproducible quantitative echocardiographic measurements, while reducing echocardiographic analysis time. In doing so, the primary benefit of Us2.v2 is to improve clinical echocardiographic workflow, enabling clinicians to generate and edit reports faster. with precision and with full control. Because Us2.v2 measurements cover the minimum echocardiographic dataset f or a standard adult echocardiogram (by European Society of Cardiovascular Imaging, British Society of Echocardiography and American Society of Echocardiography guidelines), our software is applicable to the vast majority of adult transthoracic echocardiograms. Our current sof tware aims to automate measurements of cardiac dimensions and lef t ventricular function and are applicable regardless of normal or disease states. We specifically indicate that our current product will not be reporting measurements associated with intra-cardiac lesions ( e.g. tumours, thrombi), nor complex adult congenital heart disease. The sof tware provides automated markup and analysis to generate a f ull report, on which a qualif ied sonographer/ reviewing physician could perf orm edits/ revise the markup on the echocardiographic image measurement during their approval process. The markup includes: the cardiac segments captured. measurements of distance, time, area and blood flow. quantitative analysis of cardiac function, and a summary report. The software allows the sonographer to enter their markup manually. It also provides automated markup and analysis, which the sonographer may choose to accept outright, to accept partially and modify, or to reject and ignore. Machine learning based view classification and border detection form the basis for this automated analysis. Additionally, the software has features for organizing, displaying and comparing to reference guidelines the quantitative data from cardiovascular images acquired from ultrasound scanners.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria provided for Left Ventricular Strain are based on Root Mean Square Error (RMSE). For other measurements, Intraclass Correlation Coefficient (ICC) is used, with the column indicating the lower bound of the 95% confidence interval for ICC, and the ICC itself.

Note: The document states that "Acceptance criteria were based on Root Mean Square Error against reference values generated using the comparator device" for Left Ventricular Strain, and for other measurements, ICC is used. However, specific numerical thresholds for these criteria are not explicitly stated in the provided text. The tables only show the reported performance values.

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

• Test Set Sample Sizes:
  • Dataset 1: n = 3029
  • Dataset 2: n = 260
  • Dataset 3: n = 192
• Data Provenance: The document states "US-based cohorts used in Us2.v2 testing." It also specifies that "Test datasets are strictly segregated from algorithm training datasets, as they are from completely separate cohorts." The study is described as a "bench study to validate its performance in real-world conditions" using "the same patient data and the same images" as manual analysis, implying retrospective data from clinical settings. It doesn't explicitly state if it was prospective or retrospective, but the phrasing "same patient data and the same images" for comparison with manual analysis strongly suggests retrospective use of existing data.

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

The document states that the performance of Us2.v2 measurements was compared "against manual analysis (of the same patient data and the same images) generated by trained echocardiography technicians or cardiologists, both in 'gold standard' reference echo core labs and 'real world' clinical settings."
It does not specify the exact number of experts (technicians or cardiologists) used, nor their specific qualifications (e.g., years of experience).

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1). It states that the "manual analysis...generated by trained echocardiography technicians or cardiologists" was the reference. It doesn't mention how discrepancies among multiple human readers (if any were used per case) were resolved.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly described. The study compared the device's automated measurements against a "manual analysis" reference, which was performed by "trained echocardiography technicians or cardiologists." There is no mention of human readers improving with AI vs. without AI assistance. The study focuses on the performance of the algorithm compared to human-generated measurements.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the described study appears to be a standalone performance evaluation. The device's automated analysis is compared directly against manual measurements, demonstrating the algorithm's performance without explicitly including a human-in-the-loop workflow. The description "The automated analysis generated by Us2.v2 will be compared head-to-head against manual analysis" supports this.

7. The Type of Ground Truth Used

The ground truth used was expert consensus / manual analysis. Specifically, it was established by "trained echocardiography technicians or cardiologists, both in 'gold standard' reference echo core labs and 'real world' clinical settings."

8. The Sample Size for the Training Set

The sample size for the training set is not provided in the given text. The document only states that "Test datasets are strictly segregated from algorithm training datasets, as they are from completely separate cohorts."

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

The document mentions that "Machine learning based view classification and border detection form the basis for this automated analysis" and that the test datasets are "strictly segregated from algorithm training datasets." However, it does not describe how the ground truth for the training set was established.

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Us2.v1 is a fully automated software platform that processes, analyses and makes measurements on acquired transthoracy cardiac ultrasound images, automatically producing a full report with measurements of several and functional parameters. The data produced by this software is intended to be used to support qualified cardiologists or licensed primary care providers for clinical decision-making. Us2.v1 is in adult patients. Us2.v1 has not been validated for the assessment of congenital heart disease, pericardial disease, and/or intra-cardiac lesions (e.g. tumours, thrombi).

Us2.v1 is an image post-processing analysis software device used for viewing and quantifying cardiovascular ultrasound images in DICOM format. The device is intended to aid diagnostic review and analysis of echocardiographic data, patient record management and reporting.

The software provides an interface for a skilled sonographer to perform the necessary markup on the echocardiographic image prior to review by the prescribing physician. The markup includes: the cardiac segments captured, measurements of distance, time, area and blood flow, quantitative analysis of cardiac function, and a summary report.

The software allows the sonographer to enter their markup manually. It also provides automated markup and analysis, which the sonographer may choose to accept outright, to accept partially and modify, or to reject and ignore. Machine learning based view classification and border detection form the basis for this automated analysis. Additionally, the software has features for organizing, displaying and comparing to reference guidelines the quantitative data from cardiovascular images acquired from ultrasound scanners.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The documents state a single, overarching acceptance criterion:

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