Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms
Open accessPublished version of a preprint
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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Hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension AI-ECG applications remain earlier or pathway-incomplete.
“hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension remain earlier or pathway-incomplete applications.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [30]
The Discussion says the cardiac amyloidosis ECG and echocardiography models would ideally be coupled with facilitated confirmatory testing, supporting the pathway-incomplete characterization for that application. It names hypertrophic cardiomyopathy and pulmonary arterial hypertension as other rare, difficult-to-detect diseases, but does not assess the maturity or clinical pathways of AI-ECG applications for either. (LLM, conf=0.84)
Experimental — accuracy not yet measuredGoto et al. reported an ECG-echocardiography pipeline with C-statistics of 0.85-0.91 for ECG and 0.89-1.00 for echocardiography across multicenter cohorts, and in deployment simulation ECG prescreening improved the positive predictive value of downstream echocardiographic screening from 33% to 74-77% at 67% recall.
“Goto et al subsequently reported a complementary ECG–echocardiography pipeline with C-statistics of 0.85–0.91 for ECG and 0.89–1.00 for echocardiography across multicenter cohorts. In deployment simulation, ECG prescreening improved the positive predictive value of downstream echocardiographic screening from 33% to 74–77% at 67% recall.30”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [30]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe available evidence for cardiac amyloidosis remains constrained by case-enriched designs, limited external validation, and the low-prevalence challenge intrinsic to real-world deployment.
“However, the available evidence remains constrained by case-enriched designs, limited external validation, and the low-prevalence challenge intrinsic to real-world deployment.30,43”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [30]
The Methods describe cases matched to controls, and the Introduction says low disease prevalence places high demands on model performance. However, the paper does not support the claim of limited external validation: the Methods report external ECG validation at two institutions and echocardiography validation at four. Our copy provides section labels but no page numbers. (LLM, conf=0.88)
Experimental — accuracy not yet measuredHCM and cardiac amyloidosis should be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.
“Taken together, HCM and CA should therefore be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.26–30,43”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [30]
The Discussion describes HCM and cardiac amyloidosis as potentially treatable but difficult to detect, and says effective deployment of amyloidosis detection should be coupled with confirmatory diagnostic processes. The Results report a deployment simulation, not an established screening pathway. The paper does not compare either condition with low-LVEF AI-ECG or establish the broader conclusion that neither is a deployable screening indication. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAI-ECG may help enrich the population selected for further evaluation with echocardiography, cardiac magnetic resonance imaging, nuclear scintigraphy, or serum and urine testing when clinical suspicion is present.
“AI-ECG may therefore help enrich the population selected for further evaluation with echocardiography, cardiac magnetic resonance imaging, nuclear scintigraphy, or serum and urine testing when clinical suspicion is present.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [33]
The Discussion (page unavailable) proposes AI-based detection followed by echocardiography and confirmatory testing with free-light-chain measurement and scintigraphy. The Methods (page unavailable) also identifies cardiac MRI as a diagnostic modality. The paper does not specify urine testing in this pathway, and its proposed approach deliberately limits the need for prior clinical suspicion rather than making suspicion a condition for selection. (LLM, conf=0.84)
Experimental — accuracy not yet measured
A fully automated approach using ECGs and echocardiograms detected cardiac amyloidosis with strong internal and external performance.
“Goto et al. subsequently demonstrated a fully automated approach using ECGs and echocardiograms to detect cardiac amyloidosis, with strong internal and external performance [33].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [33]
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“30”
InfluentialDeep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease
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- Authors
- 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.
- Affiliations
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Keio University (JP)
- Northwestern University (US)
- Corresponding author
- Deo, Rahul C.
- ORCID
- Venue
- Nature Communications
- Published
- 2021-05-11
- Volume
- 12
- Issue
- 1
- Pages
- 2726-2726
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8113484
- OpenAlex
- W3163032277
- Cited by
- 245
- References
- 4
- Citations by year
- 2026: 52 · 2025: 68 · 2024: 49 · 2023: 34 · 2022: 29 · 2021: 12 · 2020: 1
- FWCI
- 9.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- American Heart Association
- AstraZeneca
- Uehara Memorial Foundation
- Quest Diagnostics
- Verily Life Sciences
- SENSHIN Medical Research Foundation
- Kanae Foundation for the Promotion of Medical Science
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R01HL140731)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Amyloidosis: Diagnosis, Treatment, Outcomes
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- cardiac amyloidosis, artificial intelligence, automated disease detection, electrocardiogram, echocardiography, prescreening, diagnostic accuracy, positive predictive value
- MeSH terms
- Amyloidosis, Artificial Intelligence, Echocardiography, Electrocardiography
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