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KSC 2025

Development and Validation of an AI-ECG model for Detecting Transthyretin Amyloid Cardiomyopathy in Multi-Hospital Data

  • Oct. 2025
  • by Taehyun Joo et. al.

KSC 2025 Oral presentation

 

Background
Transthyretin amyloid cardiomyopathy (ATTR-CM) is an underdiagnosed, progressive disorder characterized by pathological deposition of misfolded transthyretin protein in the myocardium. Despite therapeutic options such as tafamidis, early detection remains difficult due to nonspecific clinical presentations. Recent machine learning models using electrocardiogram (ECG) have shown promising performance in detecting cardiac amyloidosis, highlighting the feasibility of AI-assisted detection for related cardiac conditions.

 

Purpose
This study aims to develop and validate a deep learning model for detecting ATTR-CM from 12-lead ECGs using multi-institutional data from an Asian population.

 

Methods
12-lead ECGs were retrospectively collected from five university hospitals, including patients with ATTR-CM confirmed by nuclear scintigraphy (99mTc-DPD or 99mTc-PYP) or endomyocardial biopsy. For each positive patient, we selected ECGs recorded within six months prior to diagnosis. Negative controls were drawn from one hospital (Hospital A), with a whole-body bone scan showing no cardiac uptake and no ICD-10 codes for amyloidosis or infiltrative disease, with ECGs constrained to a 3-month window around the scan date. Vision Transformer models were pretrained via masked autoencoding and then fine-tuned on labeled ECG data. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC) on predefined hold-out test sets.

 

Results
A total of 59 patients with ATTR-CM (779 ECGs) and 46,275 controls (178,274 ECGs) were included. Five distinct hold-out test sets were constructed, each containing positive cases from a specific hospital and matched at a 1:10 ratio to negative controls based on age and sex. Analysis was performed using index ECGs (the first ECG within the six-month time window for positive cases, or the ECG closest to the WBBS date for negative cases). Across the five test sets, the model’s AUROC ranged from 0.916 (95% CI: 0.829–1.000) to 0.990 (95% CI: 0.970–1.000). This performance indicates robust generalizability despite negative samples being drawn from a single institution.

 

Conclusions
An ECG-based deep learning model demonstrated strong performance in detecting ATTR-CM across multiple hospitals, suggesting potential utility for earlier and more accessible identification of this underdiagnosed condition.

 

References
-Vrudhula, Amey, et al. "Impact of case and control selection on training artificial intelligence screening of cardiac amyloidosis." JACC: Advances (2024): 100998.
-Harmon, David M., et al. "Postdevelopment performance and validation of the artificial intelligence-enhanced electrocardiogram for detection of cardiac amyloidosis." JACC: Advances 2.8 (2023): 100612.
-Na, Yeongyeon, et al. "Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram." arXiv preprint arXiv:2402.09450 (2024).

 

Link

https://ksc2025.or.kr/program/at/popup/case_preview.php?sid=386

Author

Taehyun Joo, MD1, Jaewon Oh, MD, PhD2, Yeongyeon Na, MSc1, Jiwon Seo, MD3, Ju-Hee Lee, MD, PhD4, Jung-Woo Son, MD, PhD5, Jae Yeong Cho, MD, PhD6, Jung-Hyun Choi MD, PhD7, Taehyung Yu, MSc1, Hyunjin Ahn, MD1, Gihyeon Seo, MD1, Sunghoon Joo, PhD1, Seok-Min Kang, MD, PhD2

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