High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning
Grant Duffy1, Paul P Cheng2, Neal Yuan1
1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California.
Insights
A deep learning model accurately measures left ventricular wall thickness and differentiates causes like hypertrophic cardiomyopathy and cardiac amyloidosis. This automated approach offers precise, reproducible cardiac hypertrophy diagnosis.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Early detection of increased left ventricular (LV) wall thickness is crucial but challenged by under-recognition, measurement errors, and difficulty distinguishing causes.
- Conditions like hypertrophic cardiomyopathy and cardiac amyloidosis require accurate differentiation for effective patient management.
Purpose of the Study:
- To evaluate the accuracy of a deep learning (DL) workflow in quantifying ventricular hypertrophy.
- To assess the DL model's ability to predict the causes of increased LV wall thickness, specifically hypertrophic cardiomyopathy and cardiac amyloidosis.
Main Methods:
- A cohort study utilizing physician-curated data from specialized cardiac clinics (Stanford and Cedars-Sinai) between 2008 and 2020.
- A DL algorithm trained and validated on retrospective echocardiogram videos from multiple healthcare systems, including independent external datasets.
- Analysis focused on the DL model's accuracy in measuring LV dimensions and classifying specific causes of hypertrophy.
Main Results:
- The DL algorithm demonstrated high accuracy in measuring intraventricular wall thickness (MAE 1.2 mm) and LV diameter (MAE 2.4 mm).
- It accurately classified cardiac amyloidosis (AUC 0.83) and hypertrophic cardiomyopathy (AUC 0.98) in the primary cohort.
- External validation confirmed accurate quantification of ventricular parameters (R2 0.96 domestic, R2 0.90 international) and detection of hypertrophic cardiomyopathy (AUC 0.89) and cardiac amyloidosis (AUC 0.79).
Conclusions:
- The DL model accurately identifies subtle LV wall geometric changes and differentiates causes of hypertrophy.
- The fully automated DL workflow provides reproducible, precise measurements, surpassing human expert variability.
- This technology may form the basis for a precision diagnostic approach to cardiac hypertrophy.
Importance:
Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis.
Objective:
To assess the accuracy of a deep learning workflow in quantifying ventricular hypertrophy and predicting the cause of increased LV wall thickness.
Design, Settings, And Participants:
This cohort study included physician-curated cohorts from the Stanford Amyloid Center and Cedars-Sinai Medical Center (CSMC) Advanced Heart Disease Clinic for cardiac amyloidosis and the Stanford Center for Inherited Cardiovascular Disease and the CSMC Hypertrophic Cardiomyopathy Clinic for hypertrophic cardiomyopathy from January 1, 2008, to December 31, 2020. The deep learning algorithm was trained and tested on retrospectively obtained independent echocardiogram videos from Stanford Healthcare, CSMC, and the Unity Imaging Collaborative.
Main Outcomes And Measures:
The main outcome was the accuracy of the deep learning algorithm in measuring left ventricular dimensions and identifying patients with increased LV wall thickness diagnosed with hypertrophic cardiomyopathy and cardiac amyloidosis.
Results:
The study included 23 745 patients: 12 001 from Stanford Health Care (6509 [54.2%] female; mean [SD] age, 61.6 [17.4] years) and 1309 from CSMC (808 [61.7%] female; mean [SD] age, 62.8 [17.2] years) with parasternal long-axis videos and 8084 from Stanford Health Care (4201 [54.0%] female; mean [SD] age, 69.1 [16.8] years) and 2351 from CSMS (6509 [54.2%] female; mean [SD] age, 69.6 [14.7] years) with apical 4-chamber videos. The deep learning algorithm accurately measured intraventricular wall thickness (mean absolute error [MAE], 1.2 mm; 95% CI, 1.1-1.3 mm), LV diameter (MAE, 2.4 mm; 95% CI, 2.2-2.6 mm), and posterior wall thickness (MAE, 1.4 mm; 95% CI, 1.2-1.5 mm) and classified cardiac amyloidosis (area under the curve [AUC], 0.83) and hypertrophic cardiomyopathy (AUC, 0.98) separately from other causes of LV hypertrophy. In external data sets from independent domestic and international health care systems, the deep learning algorithm accurately quantified ventricular parameters (domestic: R2, 0.96; international: R2, 0.90). For the domestic data set, the MAE was 1.7 mm (95% CI, 1.6-1.8 mm) for intraventricular septum thickness, 3.8 mm (95% CI, 3.5-4.0 mm) for LV internal dimension, and 1.8 mm (95% CI, 1.7-2.0 mm) for LV posterior wall thickness. For the international data set, the MAE was 1.7 mm (95% CI, 1.5-2.0 mm) for intraventricular septum thickness, 2.9 mm (95% CI, 2.4-3.3 mm) for LV internal dimension, and 2.3 mm (95% CI, 1.9-2.7 mm) for LV posterior wall thickness. The deep learning algorithm accurately detected cardiac amyloidosis (AUC, 0.79) and hypertrophic cardiomyopathy (AUC, 0.89) in the domestic external validation site.
Conclusions And Relevance:
In this cohort study, the deep learning model accurately identified subtle changes in LV wall geometric measurements and the causes of hypertrophy. Unlike with human experts, the deep learning workflow is fully automated, allowing for reproducible, precise measurements, and may provide a foundation for precision diagnosis of cardiac hypertrophy.
