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June 12, 2026BMC MedicineOpen Access

AI integrating ECG and echo distinguishes cardiac amyloidosis from other LVH causes with ~0.97 AUC.

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Why the study?

Cardiac amyloidosis is an under-recognized cause of left-ventricular hypertrophy often misclassified as hypertrophic cardiomyopathy or hypertensive heart disease, driving the need for an AI model to distinguish these etiologies.

Does an AI model integrating ECG and echocardiography accurately diagnose cardiac amyloidosis among patients with left-ventricular hypertrophy?

Comparison

AI model (Super Learner) and simplified scoring system vs clinical diagnoses

Design

Retrospective multicenter diagnostic model development and external validation study

Key result

A Super Learner AI model integrating electrocardiogram and echocardiography features accurately distinguished cardiac amyloidosis from other causes of left-ventricular hypertrophy with an AUC of 0.97.

Authors

SZShuyuan ZhangSZShuyuan ZhangZWZhiqiang Wan

Discussion

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Member takes

Overview

High AUC supports ML ensembles for CA discrimination in cohorts; leaves open prospective validation before clinical use.

Key Points

  • To develop and validate an AI model that distinguishes cardiac amyloidosis from other causes of left-ventricular hypertrophy.
  • Conducted a multicenter retrospective study in China.
  • Used a derivation cohort of 290 CA patients, 215 HCM patients, and 160 HHD patients.
  • Evaluated model performance using metrics such as macro-AUC, accuracy, and precision.
  • The Super Learner model achieved the highest AUC of 0.97 (95% CI: 0.95–0.98).
  • In external validation, the model achieved AUCs of 0.96 for CA and 0.91 for HHD.
  • The simplified scoring system showed robust diagnostic performance with an AUC of 0.90 (95% CI 0.86–0.93).

Study Design

Type

Cross-Sectional (n=1,221)

Multicenter

Yes

Structured PICO

Does an AI model integrating ECG and echocardiography accurately diagnose cardiac amyloidosis among patients with left-ventricular hypertrophy?

P
Population
1,221 patients with left-ventricular hypertrophy (LVH) including cardiac amyloidosis (CA), hypertrophic cardiomyopathy (HCM), and hypertensive heart disease (HHD). Derivation cohort (n=665): 290 CA, 215 HCM, 160 HHD; mean age 55.8, 66.8% male. External validation cohort (n=556): 126 CA, 240 HCM, 190 HHD; mean age 63.1, 63.3% male. Multicenter in China.
I
Intervention
AI model (Super Learner) and a simplified scoring system integrating 7 features from ECG and echocardiography (Sokolow-Lyon index, interventricular septal thickness, systolic blood pressure, left-ventricular posterior wall thickness, tricuspid annular plane systolic excursion, average E/e′, and left-ventricular ejection fraction) for CA screening and diagnosis.
O
Outcome
Diagnostic accuracy (AUC) for distinguishing cardiac amyloidosis from hypertrophic cardiomyopathy and hypertensive heart disease

Main Result

Effect estimate: AUC 0.97 (95% CI 0.95-0.98)

An AI model and simplified scoring system using routine ECG and echocardiography parameters can accurately distinguish cardiac amyloidosis from other causes of left ventricular hypertrophy.

Limitations

  • Retrospective design
  • Requires prospective validation in real-world clinical settings
  • Pseudo-infarction pattern specificity needs further verification in larger Chinese populations
  • performance and generalizability should be further validated in larger prospective multicenter studies

Cite This Study

Zhang et al. (2026) conducted a cross-sectional in Cardiac amyloidosis (n=1,221). Super Learner AI model was evaluated on Diagnostic accuracy (AUC) for cardiac amyloidosis (AUC 0.97, 95% CI 0.95-0.98). A Super Learner AI model integrating electrocardiogram and echocardiography features accurately distinguished cardiac amyloidosis from other causes of left-ventricular hypertrophy with an AUC of 0.97.

synapsesocial.com/papers/6a2bd1386550ea4541ffe9aehttps://doi.org/10.1186/s12916-026-04987-6
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence2026 · 6 citations
  2. 2Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements2024 · 9 citations
  3. 3Multimodal Artificial Intelligence for Cardiac Amyloidosis Diagnosis: Integrating Echocardiography With Clinical and Laboratory Data for Improved Detection2026 · 4 citations
  4. 4International Validation of Echocardiographic AI Amyloid Detection Algorithm2024 · 2 citations
  5. 5International Validation of Echocardiographic Artificial Intelligence Amyloid Detection Algorithm2025 · 2 citations