(154 days)
EchoSolv AS is a machine learning (ML) and artificial intelligence (AI) based decision support software indicated for use as an adjunct to echocardiography for assessment of severe aortic stenosis (AS).
When utilized by an interpreting physician, this device provides information to facilitate rendering an accurate diagnosis of AS. Patient management decisions should not be made solely on the results of the EchoSolv AS analysis.
EchoSolv AS includes both the algorithm based AS phenotype analysis, and the application of recognized AS clinical practice quidelines.
Limitations: EchoSolv AS is not intended for patients under the age of 18 years or those who have previously undergone aortic valve replacement surgery
EchoSolv AS is a standalone, cloud-based decision support software which is intended to be used certified cardiologist to aid in the diagnosis of Severe Aortic Stenosis. EchoSolv AS analyzes basic patient demographic data and measurements obtained from a transthoracic echo examination to provide a categorical assessment as to whether the data are suggestive of a high, medium or low probability of Severe AS. EchoSolv AS is intended for patients who 18 years or older who have an echocardiogram performed as part of routine clinical care (i.e., for the evaluation of structural heart disease).
Patient demographic and echo measurement data is automatically processed through the artificial intelligence algorithm which provides an output regarding the probability of a Severe AS phenotype to aid in the clinical diagnosis of Severe AS during the review of the patient echo study and generation of the final study report, according to current clinical practice guidelines. The software provides an output on the following assessments:
- Severe AS Phenotype Probability
Whether the patient has a high, medium, or low probability of exhibiting a Severe AS phenotype, based on analysis by the EchoSolv AS proprietary Al algorithm, that the determined predicted AVA is ≤1.0cm². The Al probability score requires a minimum set of data inputs to provide a valid output but is based on all available echocardiographic measurement data and does not rely on the traditional LVOT measurements used to in the continuity equation.
- Severe AS Guideline Assessment
Whether the patient meets the definition for Severe AS based on direct evaluation of provided echocardiogram data measurements (AV Peak Velocity, AV Mean Gradient and AV Area) with current clinical practice guidelines (2020 ACC/AHA Guideline for the Management of Patients with Valvular Heart Disease).
EchoSolv AS is intended to be used by board-certified cardiologists who review echocardiograms during the evaluation and diagnosis of structural heart disease, namely aortic stenosis. EchoSolv AS is intended to be used in conjunction with current clinical practices and workflows to improve the identification of Severe AS cases.
Here's an analysis of the acceptance criteria and study detailed in the provided document for the EchoSolv AS device:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state "acceptance criteria" in a tabulated format. However, based on the performance data presented, the implicit acceptance criteria can be inferred from the reported performance and comparison to a predicate device. The performance metrics reported are AUROC, Sensitivity, Specificity, Diagnostic Likelihood Ratios (DLR), and improvement in reader AUROC and concordance in the MRMC study.
| Performance Metric | Implicit Acceptance Criterion (Based on context/predicate) | Reported Device Performance (EchoSolv AS) |
|---|---|---|
| Standalone Performance | ||
| AUROC (Overall) | Expected to be high, comparable to or better than predicate (Predicate: 0.927 AUROC) | 0.948 (95% CI: 0.943-0.952) |
| Sensitivity (at high probability) | High (No specific threshold given, but expected to detect a good proportion of true positive cases) | 0.801 (95% CI: 0.786-0.818) |
| Specificity (at high probability) | High (No specific threshold given, but expected to correctly identify true negative cases) | 0.923 (95% CI: 0.915-0.932) |
| DLR (Low Probability) | Low (Indicative of low probability of disease) | 0.067 (95% CI: 0.057-0.080) |
| DLR (Medium Probability) | Close to 1 (Weakly indicative) | 0.935 (95% CI: 0.829-1.05) |
| DLR (High Probability) | High (Strongly indicative of disease) | 10.3 (95% CI: 9.22-11.50) |
| Cochran-Armitage Trend Test (p-value) | Statistically significant trend (p |
§ 892.2060 Radiological computer-assisted diagnostic software for lesions suspicious of cancer.
(a)
Identification. A radiological computer-assisted diagnostic software for lesions suspicious of cancer is an image processing prescription device intended to aid in the characterization of lesions as suspicious for cancer identified on acquired medical images such as magnetic resonance, mammography, radiography, or computed tomography. The device characterizes lesions based on features or information extracted from the images and provides information about the lesion(s) to the user. Diagnostic and patient management decisions are made by the clinical user.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended.
(iii) Results from performance testing protocols that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Standalone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; and description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity).(2) Labeling must include:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the intended reading protocol.
(iii) A detailed description of the intended user and recommended user training.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations, including situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) Detailed instructions for use.
(viii) A detailed summary of the performance testing, including: Test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders (
e.g., lesion and organ characteristics, disease stages, and imaging equipment).