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International Validation of Echocardiographic Artificial Intelligence Amyloid Detection Algorithm
Grant Duffy1, Evangelos K Oikonomou2, Nicholas Easton3
1Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Insights
A new computer vision algorithm, EchoNet-left ventricular hypertrophy (LVH), shows high accuracy in diagnosing cardiac amyloidosis (CA). This AI tool aids in earlier and more precise CA detection, improving patient outcomes.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Cardiac amyloidosis (CA) diagnosis is often delayed due to its similarity to other conditions causing left ventricular wall thickening.
- Conventional echocardiography has limitations in specificity for CA detection.
Purpose of the Study:
- To evaluate the performance of a computer vision algorithm, EchoNet-left ventricular hypertrophy (LVH), for identifying cardiac amyloidosis (CA).
- To assess the algorithm's diagnostic accuracy across multiple international sites in a case-control study.
Main Methods:
- A retrospective case-control study involving 574 CA patients and 979 controls.
- Utilized EchoNet-LVH, a deep learning algorithm analyzing echocardiogram videos (parasternal long axis and apical-4-chamber views).
- Evaluated diagnostic performance using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value.
Main Results:
- EchoNet-LVH achieved an AUC of 0.896.
- At a threshold optimized for specificity, the algorithm demonstrated a sensitivity of 0.644 and a high specificity of 0.988.
- Positive predictive value was 0.968, and negative predictive value was 0.828, with consistent performance across various patient demographics and study sites.
Conclusions:
- EchoNet-LVH shows potential to assist in earlier and more accurate diagnosis of cardiac amyloidosis.
- The algorithm's high specificity is crucial for maximizing the positive predictive value of confirmatory tests in rare diseases like CA.
Background:
Diagnosis of cardiac amyloidosis (CA) is often missed or delayed due to confusion with other causes of increased left ventricular wall thickness. Conventional transthoracic echocardiographic measurements like global longitudinal strain have shown promise in distinguishing CA, but with limited specificity.
Objectives:
We conducted a multisite retrospective case-control study to investigate the performance of a computer vision algorithm for CA identification across multiple international sites.
Methods:
EchoNet-left ventricular hypertrophy (LVH) is a computer vision deep learning algorithm for the detection of CA based on parasternal long axis and apical-4-chamber view videos. We evaluated EchoNet-LVH's ability to distinguish between the echocardiogram studies of 574 CA patients and 979 controls. We reported discrimination performance with an area under the receiver operating characteristic curve and associated sensitivity, specificity, and positive predictive value at the prespecified threshold.
Results:
EchoNet-LVH had an area under the receiver operating characteristic curve of 0.896 (95% CI: 0.875-0.916). At the prespecified model threshold optimizing for specificity, EchoNet-LVH had a sensitivity of 0.644 (95% CI: 0.601-0.685), specificity of 0.988 (95% CI: 0.978-0.994), positive predictive value of 0.968 (95% CI: 0.944-0.984), and negative predictive value of 0.828 (95% CI: 0.804-0.850). There was no evidence of heterogeneity in performance by site, race, sex, age, body mass index, CA subtype, or ultrasound manufacturer.
Conclusions:
EchoNet-LVH can assist with earlier and accurate diagnosis of CA. EchoNet-LVH achieved development goals to be highly specific to maximize positive predictive value of downstream confirmatory testing since CA is a rare disease.
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