AI Algorithm for Detecting Cardiac Amyloidosis
Author Information
Author(s): Duffy Grant BS, Oikonomou Evan MD, DPhil, Hourmozdi Jonathan MD, Usuku Hiroki MD, PhD, Patel Jigesh MD, Stern Lily MD, Goto Shinichi MD, PhD, Tsujita Kenichi MD, PhD, Khera Rohan MD, MS, Ahmad Faraz S. MD, Ouyang David MD
Primary Institution: Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Hypothesis
Can a computer vision detection algorithm accurately identify cardiac amyloidosis in echocardiogram studies across multiple international sites?
Conclusion
The EchoNet-LVH algorithm can assist with earlier and accurate diagnosis of cardiac amyloidosis.
Supporting Evidence
- EchoNet-LVH had an AUC of 0.896.
- At the pre-specified threshold, EchoNet-LVH had a sensitivity of 0.644 and specificity of 0.988.
- The positive predictive value was 0.968 and negative predictive value was 0.828.
- Performance was consistent across different sites and patient demographics.
Takeaway
A smart computer program can help doctors find a rare heart disease called cardiac amyloidosis more quickly and accurately.
Methodology
A multi-site retrospective case-control study evaluating the performance of the EchoNet-LVH algorithm using echocardiogram videos.
Potential Biases
There is potential observer variability in echocardiographic measurements.
Limitations
The study is retrospective and lacks prospective trialing of CA screening approaches.
Participant Demographics
Mean age of participants was 78.2 years, with 77.7% male.
Statistical Information
Confidence Interval
95% CI 0.875 – 0.916
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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