Open AccessPredicting Age-Related Macular Degeneration Progression with Contrastive Attention and Time-Aware LSTM
Author(s) -
Changchang Yin,
Sayoko E. Moroi,
Ping Zhang
Publication year - 2022
Publication title -
proceedings of the 28th acm sigkdd conference on knowledge discovery and data mining
Language(s) - English
Resource type - Conference proceedings
pISSN - 2154-817X
DOI - 10.1145/3534678.3539163
Subject(s) - macular degeneration , computer science , artificial intelligence , deep learning , leverage (statistics) , recurrent neural network , convolutional neural network , machine learning , medicine , artificial neural network , ophthalmology
Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in developed countries. Identifying patients at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Recently, deep-learning-based models have been developed and achieved superior performance for late AMD prediction. However, most existing methods are limited to the color fundus photography (CFP) from the last ophthalmic visit and do not include the longitudinal CFP history and AMD progression during the previous years' visits. Patients in different AMD subphenotypes might have various speeds of progression in different stages of AMD disease. Capturing the progression information during the previous years' visits might be useful for the prediction of AMD progression. In this work, we propose a C ontrastive- A ttention-based T ime-aware L ong S hort- T erm M emory network ( CAT-LSTM ) to predict AMD progression. First, we adopt a convolutional neural network (CNN) model with a contrastive attention module (CA) to extract abnormal features from CFPs. Then we utilize a time-aware LSTM (T-LSTM) to model the patients' history and consider the AMD progression information. The combination of disease progression, genotype information, demographics, and CFP features are sent to T-LSTM. Moreover, we leverage an auto-encoder to represent temporal CFP sequences as fixed-size vectors and adopt k-means to cluster them into subphenotypes. We evaluate the proposed model based on real-world datasets, and the results show that the proposed model could achieve 0.925 on area under the receiver operating characteristic (AUROC) for 5-year late-AMD prediction and outperforms the state-of-the-art methods by more than 3%, which demonstrates the effectiveness of the proposed CAT-LSTM. After analyzing patient representation learned by an auto-encoder, we identify 3 novel subphenotypes of AMD patients with different characteristics and progression rates to late AMD, paving the way for improved personalization of AMD management. The code of CAT-LSTM can be found at GitHub.