Predicting Osteoarthritis Progression in Radiographs via Unsupervised Representation Learning

by   Tianyu Han, et al.

Osteoarthritis (OA) is the most common joint disorder affecting substantial proportions of the global population, primarily the elderly. Despite its individual and socioeconomic burden, the onset and progression of OA can still not be reliably predicted. Aiming to fill this diagnostic gap, we introduce an unsupervised learning scheme based on generative models to predict the future development of OA based on knee joint radiographs. Using longitudinal data from osteoarthritis studies, we explore the latent temporal trajectory to predict a patient's future radiographs up to the eight-year follow-up visit. Our model predicts the risk of progression towards OA and surpasses its supervised counterpart whose input was provided by seven experienced radiologists. With the support of the model, sensitivity, specificity, positive predictive value, and negative predictive value increased significantly from 42.1 72.3 while without such support, radiologists performed only slightly better than random guessing. Our predictive model improves predictions on OA onset and progression, despite requiring no human annotation in the training phase.



There are no comments yet.


page 3

page 4

page 5

page 7

page 20

page 21

page 22

page 23


Modeling sepsis progression using hidden Markov models

Characterizing a patient's progression through stages of sepsis is criti...

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...

Representing Alzheimer's Disease Progression via Deep Prototype Tree

For decades, a variety of predictive approaches have been proposed and e...

Personalized Decision Making for Biopsies in Prostate Cancer Active Surveillance Programs

Background: Low-risk prostate cancer patients enrolled in active surveil...

Joint longitudinal and time-to-event models for multilevel hierarchical data

Joint modelling of longitudinal and time-to-event data has received much...

Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects

The ability to predict the progression of biomarkers, notably in NDD, is...

Reinforcement Learning based Disease Progression Model for Alzheimer's Disease

We model Alzheimer's disease (AD) progression by combining differential ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.