Development of an Expert-Level Right Ventricular Abnormality Detection Algorithm Based on Deep Learning.

Abstract

Purpose: Studies relating to the right ventricle (RV) are inadequate, and specific diagnostic algorithms still need to be improved. This essay is designed to make exploration and verification on an algorithm of deep learning based on imaging and clinical data to detect RV abnormalities.

Methods: The Automated Cardiac Diagnosis Challenge dataset includes 20 subjects with RV abnormalities (an RV cavity volume which is higher than 110 mL/m² or RV ejection fraction which is lower than 40%) and 20 normal subjects who suffered from both cardiac MRI. The subjects were separated into training and validation sets in a ratio of 7:3 and were modeled by utilizing a nerve net of deep-learning and six machine-learning algorithms. Eight MRI specialists from multiple centers independently determined whether each subject in the validation group had RV abnormalities. Model performance was evaluated based on the AUC, accuracy, recall, sensitivity and specificity. Furthermore, a preliminary assessment of patient disease risk was performed based on clinical information using a nomogram.

Results: The deep-learning neural network outperformed the other six machine-learning algorithms, with an AUC value of 1 (95% confidence interval: 1-1) on both training group and validation group. This algorithm surpassed most human experts (87.5%). In addition, the nomogram model could evaluate a population with a disease risk of 0.2-0.8.

Conclusions: A deep-learning algorithm could effectively identify patients with RV abnormalities. This AI algorithm developed specifically for right ventricular abnormalities will improve the detection of right ventricular abnormalities at all levels of care units and facilitate the timely diagnosis and treatment of related diseases. In addition, this study is the first to validate the algorithm's ability to classify RV abnormalities by comparing it with human experts.