Disease Forecast via Progression Learning

by   Botong Wu, et al.

Forecasting Parapapillary atrophy (PPA), i.e., a symptom related to most irreversible eye diseases, provides an alarm for implementing an intervention to slow down the disease progression at early stage. A key question for this forecast is: how to fully utilize the historical data (e.g., retinal image) up to the current stage for future disease prediction? In this paper, we provide an answer with a novel framework, namely Disease Forecast via Progression Learning (DFPL), which exploits the irreversibility prior (i.e., cannot be reversed once diagnosed). Specifically, based on this prior, we decompose two factors that contribute to the prediction of the future disease: i) the current disease label given the data (retinal image, clinical attributes) at present and ii) the future disease label given the progression of the retinal images that from the current to the future. To model these two factors, we introduce the current and progression predictors in DFPL, respectively. In order to account for the degree of progression of the disease, we propose a temporal generative model to accurately generate the future image and compare it with the current one to get a residual image. The generative model is implemented by a recurrent neural network, in order to exploit the dependency of the historical data. To verify our approach, we apply it to a PPA in-house dataset and it yields a significant improvement (e.g., 4.48% of accuracy; 3.45% of AUC) over others. Besides, our generative model can accurately localize the disease-related regions.


page 3

page 8


Development and Validation of a Novel Prognostic Model for Predicting AMD Progression Using Longitudinal Fundus Images

Prognostic models aim to predict the future course of a disease or condi...

Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction

Recent studies on modelling the progression of Alzheimer's disease use a...

A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome

In this work, we consider the problem of predicting the course of a prog...

Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data

Robust forecasting of the future anatomical changes inflicted by an ongo...

Linear mixed model vs two-stage methods: Developing prognostic models of diabetic kidney disease progression

Identifying prognostic factors for disease progression is a cornerstone ...

RADNet: Ensemble Model for Robust Glaucoma Classification in Color Fundus Images

Glaucoma is one of the most severe eye diseases, characterized by rapid ...

DaTscan SPECT Image Classification for Parkinson's Disease

Parkinson's Disease (PD) is a neurodegenerative disease that currently d...

Please sign up or login with your details

Forgot password? Click here to reset