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.

This work is funded by NIH NHLBI grants R00HL157421, R01HL173526, and R01HL173487 to DO, and a grant from Alexion AstraZeneca Rare Disease. GD is currently an employee of Meta. D.O. reports consulting fees and/or equity in Ultromics, InVision, EchoIQ, and Pfizer. National Heart, Lung, And Blood Institute of the National Institutes of Health (under award numbers R01HL167858 and K23HL153775 to R.K., and F32HL170592 to E.K.O.), National Institute on Aging of the National Institutes of Health (under award number R01AG089981 to R.K.), and the Doris Duke Charitable Foundation (under award number 2022060 to R.K.). FSA has received research support from Pfizer and Atman Health. All other authors declare no competing interests.

This work is funded by NIH NHLBI grants R00HL157421, R01HL173526, and R01HL173487 to DO, and a grant from Alexion AstraZeneca Rare Disease.

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