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

• URL: http://arxiv.org/abs/2211.04234v1
• Date: Tue, 8 Nov 2022 13:28:26 GMT
• Title: Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data
• Authors: Dmitrii Lachinov, Arunava Chakravarty, Christoph Grechenig, Ursula Schmidt-Erfurth, Hrvoje Bogunovic
• Abstract summary: We develop a deep learning method that models the evolution of age-related disease by processing a single medical scan. For Geographic Atrophy, the proposed method outperformed the related baseline models in the atrophy growth prediction. For Alzheimer's Disease, the proposed method demonstrated remarkable performance in predicting the brain ventricle changes induced by the disease.
• Score: 4.998875488622879
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare professionals. Such a capability, however, is of great importance since it can improve patient management by providing information on the speed of disease progression already at the admission stage, or it can enrich the clinical trials with fast progressors and avoid the need for control arms by the means of digital twins. In this work, we develop a deep learning method that models the evolution of age-related disease by processing a single medical scan and providing a segmentation of the target anatomy at a requested future point in time. Our method represents a time-invariant physical process and solves a large-scale problem of modeling temporal pixel-level changes utilizing NeuralODEs. In addition, we demonstrate the approaches to incorporate the prior domain-specific constraints into our method and define temporal Dice loss for learning temporal objectives. To evaluate the applicability of our approach across different age-related diseases and imaging modalities, we developed and tested the proposed method on the datasets with 967 retinal OCT volumes of 100 patients with Geographic Atrophy, and 2823 brain MRI volumes of 633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposed method outperformed the related baseline models in the atrophy growth prediction. For Alzheimer's Disease, the proposed method demonstrated remarkable performance in predicting the brain ventricle changes induced by the disease, achieving the state-of-the-art result on TADPOLE challenge.

Related papers

• Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs [6.5527554277858275]
  We present the first causal temporal framework to model the continuous temporal evolution of disease progression via Neural Differential Equations (NSDE) Our results present the first successful uncertainty-based causal Deep Learning (DL) model to accurately predict future patient MS disability (e.g. EDSS) and treatment effects.
  arXiv  Detail & Related papers  (2024-06-18T17:22:55Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Predicting Parkinson's disease evolution using deep learning [1.4610685586329806]
  Parkinson's disease is a neurological condition that occurs in nearly 1% of the world's population. There is not a single blood test or biomarker available to diagnose Parkinson's disease. No AI tools have been designed to identify the stage of progression.
  arXiv  Detail & Related papers  (2023-12-28T10:30:54Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones [75.23250968928578]
  We present a method for predicting the medication status of Parkinson's disease patients using the public mPower dataset. The proposed method shows promising results in predicting three medication statuses objectively.
  arXiv  Detail & Related papers  (2022-07-26T02:08:08Z)
• Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction [0.5735035463793008]
  This article overviews various categories of models used for Alzheimer's disease prediction with their respective learning methods. It establishes a comparative study of early prediction and detection Alzheimer's disease progression. Finally, a robust and precise detection model is proposed.
  arXiv  Detail & Related papers  (2021-10-10T11:44:16Z)
• An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization [3.9281410693767036]
  We propose an end-to-end deep learning approach for automated diagnosis of Alzheimer's disease (AD) and localization of important brain regions related to the disease from sMRI data. Our approach has been evaluated on two publicly accessible datasets for two classification tasks of AD vs. cognitively normal (CN) and progressive MCI (pMCI) vs. stable MCI (sMCI) The experimental results indicate that our approach outperforms the state-of-the-art approaches, including those using multi-model and 3D CNN methods.
  arXiv  Detail & Related papers  (2021-07-28T07:19:00Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression [4.034948808542701]
  Alzheimer's disease (AD) is one of the major causes of dementia and is characterized by slow progression over several years. We propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status.
  arXiv  Detail & Related papers  (2020-05-06T08:08:00Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.