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Performing and Processing FNA of Anterior Fat Pad for Amyloid
Published on: October 30, 2010
International Validation of Echocardiographic AI Amyloid Detection Algorithm
Grant Duffy1, Evan Oikonomou2, Jonathan Hourmozdi3
1Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Insights
A new computer vision algorithm, EchoNet-LVH, shows high accuracy in detecting cardiac amyloidosis (CA) from echocardiograms. This tool can aid in earlier and more precise diagnosis of this often-missed condition.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Cardiac amyloidosis (CA) diagnosis is frequently delayed due to its resemblance to other conditions causing left ventricular hypertrophy.
- Conventional echocardiographic measures like global longitudinal strain (GLS) offer limited specificity for CA detection.
Purpose of the Study:
- To evaluate the diagnostic performance of EchoNet-LVH, a computer vision algorithm, for detecting cardiac amyloidosis.
- To assess the algorithm's ability to differentiate CA from other causes of increased left ventricular wall thickness using echocardiogram videos.
Main Methods:
- A multi-site retrospective case-control study was conducted using echocardiogram videos.
- EchoNet-LVH, a deep learning algorithm, analyzed parasternal long axis and apical-4-chamber views to detect CA.
- Performance was measured using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and predictive values.
Main Results:
- EchoNet-LVH achieved an AUC of 0.896, indicating strong discrimination.
- The algorithm demonstrated high specificity (0.988) and positive predictive value (0.968), crucial for diagnosing rare diseases like CA.
- Performance remained consistent across various sites, demographics, and equipment, suggesting broad applicability.
Conclusions:
- EchoNet-LVH shows significant potential to assist in the earlier and more accurate diagnosis of cardiac amyloidosis.
- The algorithm's high specificity is vital for maximizing positive predictive value in the context of CA's rarity.
- Future research will focus on whether early diagnosis facilitated by EchoNet-LVH leads to improved treatment initiation and patient outcomes.
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 (GLS) has shown promise in distinguishing CA, but with limited specificity. We conducted a study to investigate the performance of a computer vision detection algorithm in across multiple international sites.
Methods:
EchoNet-LVH is a computer vision deep learning algorithm for the detection of cardiac amyloidosis based on parasternal long axis and apical-4-chamber view videos. We conducted a multi-site retrospective case-control study evaluating EchoNet-LVH's ability to distinguish between the echocardiogram studies of CA patients and controls. We reported discrimination performance with area under the receiver operating characteristic curve (AUC) and associated sensitivity, specificity, and positive predictive value at the pre-specified threshold.
Results:
EchoNet-LVH had an AUC of 0.896 (95% CI 0.875 - 0.916). At pre-specified model threshold, EchoNet-LVH had a sensitivity of 0.644 (95% CI 0.601 - 0.685), specificity of 0.988 (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 minimal heterogeneity in performance by site, race, sex, age, BMI, CA subtype, or ultrasound manufacturer.
Conclusion:
EchoNet-LVH can assist with earlier and accurate diagnosis of CA. As CA is a rare disease, EchoNet-LVH is highly specific in order to maximize positive predictive value. Further work will assess whether early diagnosis results in earlier initiation of treatment in this underserved population.
