(150 days)
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
| Performance Metric | Acceptance Criteria (Derived from "current standards of care and existing relevant publications") | Reported Device Performance (95% CI) |
|---|---|---|
| Sensitivity | Implicitly met by reported performance within clinical relevance | 86.9% (84.2%-89.7%) |
| Specificity | Implicitly met by reported performance within clinical relevance | 87.4% (85.2%-89.7%) |
| Overall Yield | Sufficiently high | 87.1% |
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.
§ 870.2200 Adjunctive cardiovascular status indicator.
(a)
Identification. The adjunctive cardiovascular status indicator is a prescription device based on sensor technology for the measurement of a physical parameter(s). This device is intended for adjunctive use with other physical vital sign parameters and patient information and is not intended to independently direct therapy.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Software description, verification, and validation based on comprehensive hazard analysis must be provided, including:
(i) Full characterization of technical parameters of the software, including any proprietary algorithm(s);
(ii) Description of the expected impact of all applicable sensor acquisition hardware characteristics on performance and any associated hardware specifications;
(iii) Specification of acceptable incoming sensor data quality control measures; and
(iv) Mitigation of impact of user error or failure of any subsystem components (signal detection and analysis, data display, and storage) on accuracy of patient reports.
(2) Scientific justification for the validity of the status indicator algorithm(s) must be provided. Verification of algorithm calculations and validation testing of the algorithm using a data set separate from the training data must demonstrate the validity of modeling.
(3) Usability assessment must be provided to demonstrate that risk of misinterpretation of the status indicator is appropriately mitigated.
(4) Clinical data must be provided in support of the intended use and include the following:
(i) Output measure(s) must be compared to an acceptable reference method to demonstrate that the output measure(s) represent(s) the predictive measure(s) that the device provides in an accurate and reproducible manner;
(ii) The data set must be representative of the intended use population for the device. Any selection criteria or limitations of the samples must be fully described and justified;
(iii) Agreement of the measure(s) with the reference measure(s) must be assessed across the full measurement range; and
(iv) Data must be provided within the clinical validation study or using equivalent datasets to demonstrate the consistency of the output and be representative of the range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment.
(5) Labeling must include the following:
(i) The type of sensor data used, including specification of compatible sensors for data acquisition;
(ii) A description of what the device measures and outputs to the user;
(iii) Warnings identifying sensor reading acquisition factors that may impact measurement results;
(iv) Guidance for interpretation of the measurements, including warning(s) specifying adjunctive use of the measurements;
(v) Key assumptions made in the calculation and determination of measurements;
(vi) The measurement performance of the device for all presented parameters, with appropriate confidence intervals, and the supporting evidence for this performance; and
(vii) A detailed description of the patients studied in the clinical validation (
e.g., age, gender, race/ethnicity, clinical stability) as well as procedural details of the clinical study.