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Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images

Hongyu Zhou1,2,3, Lu Li4, Zhenyu Liu2,3

  • 1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, 518055, China.

European Radiology
|November 26, 2020
PubMed

Insights

A deep learning model using cardiovascular magnetic resonance (CMR) images improved hypertrophic cardiomyopathy (HCM) genetic mutation prediction. Combining this deep learning (DL) model with the Toronto score further enhanced diagnostic performance for HCM genotypes.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Hypertrophic cardiomyopathy (HCM) presents diverse genetic phenotypes, necessitating accurate risk stratification.
  • Existing risk-stratification systems rely on clinical and echocardiographic data, with limitations in predicting genetic mutations.
  • Cardiovascular magnetic resonance (CMR) offers detailed morphological insights valuable for HCM assessment.

Purpose of the Study:

  • To enhance mutation-risk prediction in HCM by extracting morphological features using a deep learning (DL) algorithm.
  • To evaluate the performance of a DL model in classifying HCM genotypes based on CMR cine images.
  • To compare the DL model's predictive accuracy against established genotype scores.

Main Methods:

  • Recruited 198 HCM patients, divided into training (147) and test (51) sets.
  • Developed a DL model to classify HCM genotypes using nonenhanced four-chamber cine CMR images.
  • Assessed established genotype scores (Mayo Clinic I, Mayo Clinic II, Toronto score) and the DL model's performance using Area Under the Curve (AUC).

Main Results:

  • The DL model achieved an AUC of 0.80, outperforming Mayo Clinic scores (AUC 0.64-0.70) and the Toronto score (AUC 0.74).
  • The DL model demonstrated higher sensitivity (85.71%) and specificity (69.57%) compared to established scores.
  • Combining the DL model with the Toronto score yielded the highest predictive performance (AUC = 0.84).

Conclusions:

  • Deep learning effectively extracts image features from cine CMR images for HCM genotype classification.
  • The DL model based on cine images shows superior performance over established scores in identifying HCM patients with positive genotypes.
  • Integrating the DL model with the Toronto score significantly improves the identification of HCM patients with positive genetic mutations.
Abstract

Keywords:
Cardiomyopathy, hypertrophicDeep learningGenotypeMagnetic resonance imaging

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