Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms

Shinichi Goto; Keitaro Mahara; Lauren Beussink-Nelson; Hidehiko Ikura; Yoshinori Katsumata; Jin Endo; Hanna K. Gaggin; Sanjiv J. Shah; Yuji Itabashi; Calum A. MacRae; Rahul C. Deo

doi:10.1038/s41467-021-22877-8

Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms

Shinichi Goto, Keitaro Mahara, Lauren Beussink-Nelson, Hidehiko Ikura, Yoshinori Katsumata, Jin Endo, Hanna K. Gaggin, Sanjiv J. Shah, Yuji Itabashi, Calum A. MacRae, Rahul C. Deo^*

^*Corresponding author for this work

Medicine, Cardiology Division

Research output: Contribution to journal › Article › peer-review

97 Scopus citations

Abstract

Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85–0.91 for ECG and 0.89–1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3–4% at 52–71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74–77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.

Original language	English (US)
Article number	2726
Journal	Nature communications
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41467-021-22877-8
State	Published - Dec 1 2021

Funding

This work was supported by One Brave Idea, co-founded by the American Heart Association and Verily with significant support from AstraZeneca and pillar support from Quest Diagnostics (to C.A.M. and R.C.D.), NIH/NHLBI HL140731 (to R.C.D.) SENSHIN Medical Research Foundation (to S.G.), the Kanae foundation for the promotion of medical science (to S.G.), the Uehara Memorial Foundation and the Vehicle Racing Commemorative Foundation. R.C.D is supported by grants from the National Institute of Health, the American Heart Association (One Brave Idea, Apple Heart and Movement Study) and GE Healthcare, has received consulting fees from Novartis and Pfizer, and is co-founder of Atman Health. S.J.S. is supported by grants from the National Institutes of Health (R01 HL140731, R01 HL120728, R01 HL107577, and R01 HL149423); the American Heart Association (#16SFRN28780016, #15CVGPSD27260148); Actelion, AstraZeneca, Corvia, and Novartis; and has received consulting fees from Actelion, Amgen, AstraZeneca, Bayer, Boehringer-Ingelheim, Cardiora, Eisai, Ionis, Ironwood, Merck, Novartis, Pfizer, Sanofi, and United Therapeutics. C.A.M. is a consultant for Pfizer and co-founder of Atman Health. All other authors declare no competing interests.

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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Goto, S., Mahara, K., Beussink-Nelson, L., Ikura, H., Katsumata, Y., Endo, J., Gaggin, H. K., Shah, S. J., Itabashi, Y., MacRae, C. A., & Deo, R. C. (2021). Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms. Nature communications, 12(1), Article 2726. https://doi.org/10.1038/s41467-021-22877-8

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abstract = "Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85–0.91 for ECG and 0.89–1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3–4% at 52–71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74–77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.",

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Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms

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