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 and Rahul C. Deo ()
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Shinichi Goto: Brigham and Women’s Hospital
Keitaro Mahara: Harvard T.H. Chan School of Public Health
Lauren Beussink-Nelson: Northwestern University Feinberg School of Medicine
Hidehiko Ikura: Keio University School of Medicine
Yoshinori Katsumata: Keio University School of Medicine
Jin Endo: Keio University School of Medicine
Hanna K. Gaggin: Harvard Medical School
Sanjiv J. Shah: Northwestern University Feinberg School of Medicine
Yuji Itabashi: Keio University School of Medicine
Calum A. MacRae: Brigham and Women’s Hospital
Rahul C. Deo: Brigham and Women’s Hospital

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-22877-8

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