Riemannian Geometry Learning for Disease Progression Modelling

Louis, Maxime; Couronné, Raphaël; Koval, Igor; Charlier, Benjamin; Durrleman, Stanley

doi:10.1007/978-3-030-20351-1_42

Cited by 21 publications

(22 citation statements)

References 14 publications

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“…Possible application scenarios include the use of brain MRI to monitor and predict the onset or progression of Alzheimer's disease or other neuro-degenerative disorders to act early and to prevent further progression [22]. Indeed, it has been shown that prediction of the progression in patients with Alzheimer's disease based on variables extracted through deep neural networks might be a promising approach [23][24][25]. Similar needs arise in the progression of patients with liver fibrosis towards liver cirrhosis [26], the progression of osteoporosis [27], the course of cardiovascular diseases [28], or diabetes and its complications [29].…”

Section: Discussionmentioning

confidence: 99%

Image prediction of disease progression by style-based manifold extrapolation

Han¹,

Kather²,

Pedersoli³

et al. 2021

Preprint

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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% to 51.6%, from 72.3% to 88.6%, from 28.4% to 57.6%, and from 83.9% to 88.4%, respectively, 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.

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Section: Discussionmentioning

confidence: 99%

Image prediction of disease progression by style-based manifold extrapolation

Han¹,

Kather²,

Pedersoli³

et al. 2021

Preprint

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“…NLMEM include univariate models with time-reparameterizing functions [12]. Multivariate approaches include DIVE [4], a voxel-based model which also clusters disease trajectories, and disease course mapping which combines variations in progression dynamics with phenotypic differences [5,[13][14][15]. In this approach, the observations belong to a Riemannian manifold and each individual trajectory in time is a parallel to a geodesic curve representing the population trajectory.…”

Section: Related Workmentioning

confidence: 99%

Mixture Modeling for Identifying Subtypes in Disease Course Mapping

Poulet

Durrleman

2021

Lecture Notes in Computer Science

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Disease modeling techniques summarize the possible trajectories of progression from multimodal and longitudinal data. These techniques often assume that individuals form a homogeneous cluster, thus ignoring possible disease subtypes within the population. We extend a non-linear mixed-effect model used for disease course mapping with a mixture framework. We jointly estimate model parameters and subtypes with a tempered version of a stochastic approximation of the Expectation Maximisation algorithm. We show that our model recovers the ground truth parameters from synthetic data, in contrast to the naive solution consisting in post hoc clustering of individual parameters from a one-class model. Applications to Alzheimer's disease data allows the unsupervised identification of disease subtypes associated with distinct relationship between cognitive decline and progression of imaging and biological biomarkers.

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“…More recently, deep learning techniques have been proposed for modeling disease progression. Louis et al [207] proposed to use a recurrent neural network to model trajectories of the evolution of cognitive scores and anatomical MRI in patients with AD. Fisher et al [208] used conditional restricted boltzmann machines to generate trajectories of change for different clinical measures in AD.…”

Section: Disease Progression Modelingmentioning

confidence: 99%

Deep learning for brain disorders: from data processing to disease treatment

Burgos

Bottani

Faouzi

et al. 2020

Briefings in Bioinformatics

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In order to reach precision medicine and improve patients’ quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

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Riemannian Geometry Learning for Disease Progression Modelling

Cited by 21 publications

References 14 publications

Image prediction of disease progression by style-based manifold extrapolation

Image prediction of disease progression by style-based manifold extrapolation

Mixture Modeling for Identifying Subtypes in Disease Course Mapping

Deep learning for brain disorders: from data processing to disease treatment

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