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

• URL: http://arxiv.org/abs/2007.05120v1
• Date: Fri, 10 Jul 2020 00:33:19 GMT
• Title: Development and Validation of a Novel Prognostic Model for Predicting AMD Progression Using Longitudinal Fundus Images
• Authors: Joshua Bridge, Simon P. Harding, Yalin Zheng
• Abstract summary: We propose a novel deep learning method to predict the progression of diseases using longitudinal imaging data with uneven time intervals. We demonstrate our method on a longitudinal dataset of color fundus images from 4903 eyes with age-related macular degeneration (AMD) Our method attains a testing sensitivity of 0.878, a specificity of 0.887, and an area under the receiver operating characteristic of 0.950.
• Score: 6.258161719849178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prognostic models aim to predict the future course of a disease or condition and are a vital component of personalized medicine. Statistical models make use of longitudinal data to capture the temporal aspect of disease progression; however, these models require prior feature extraction. Deep learning avoids explicit feature extraction, meaning we can develop models for images where features are either unknown or impossible to quantify accurately. Previous prognostic models using deep learning with imaging data require annotation during training or only utilize a single time point. We propose a novel deep learning method to predict the progression of diseases using longitudinal imaging data with uneven time intervals, which requires no prior feature extraction. Given previous images from a patient, our method aims to predict whether the patient will progress onto the next stage of the disease. The proposed method uses InceptionV3 to produce feature vectors for each image. In order to account for uneven intervals, a novel interval scaling is proposed. Finally, a Recurrent Neural Network is used to prognosticate the disease. We demonstrate our method on a longitudinal dataset of color fundus images from 4903 eyes with age-related macular degeneration (AMD), taken from the Age-Related Eye Disease Study, to predict progression to late AMD. Our method attains a testing sensitivity of 0.878, a specificity of 0.887, and an area under the receiver operating characteristic of 0.950. We compare our method to previous methods, displaying superior performance in our model. Class activation maps display how the network reaches the final decision.

Related papers

• Extrapolating Prospective Glaucoma Fundus Images through Diffusion Model in Irregular Longitudinal Sequences [46.80977922491862]
  The utilization of longitudinal datasets for glaucoma progression prediction offers a compelling approach to support early therapeutic interventions. We propose a novel diffusion-based model to predict prospective images by extrapolating from existing longitudinal fundus images of patients.
  arXiv  Detail & Related papers  (2024-10-28T15:31:47Z)
• Multi-task Learning Approach for Intracranial Hemorrhage Prognosis [0.0]
  We propose a 3D multi-task image model to predict prognosis, Glasgow Coma Scale and age, improving accuracy and interpretability. Our method outperforms current state-of-the-art baseline image models, and demonstrates superior performance in ICH prognosis compared to four board-certified neuroradiologists using only CT scans as input.
  arXiv  Detail & Related papers  (2024-08-16T14:56:17Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Image [1.805673949640389]
  We introduce a new way to train time-aware models using $t_mix$, a weighted average time between two consecutive examinations. We predict whether an eye would develop a severe DR in the following visit using a single image, with an AUC of 0.798 compared to baseline results of 0.641.
  arXiv  Detail & Related papers  (2023-10-16T14:01:20Z)
• Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model [1.4922888318989764]
  Mobile sensing-based modeling of behavioral changes could predict an oncoming psychotic relapse in schizophrenia patients for timely interventions. Deep learning models could complement existing non-deep learning models for relapse prediction by modeling latent behavioral features relevant to the prediction. We propose RelapsePredNet, a neural network-based model for relapse prediction.
  arXiv  Detail & Related papers  (2022-05-24T17:34:19Z)
• Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor [55.9023096444383]
  Current deep learning models are characterised by catastrophic forgetting of old knowledge when learning new classes. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning.
  arXiv  Detail & Related papers  (2022-04-28T08:41:51Z)
• On-the-Fly Test-time Adaptation for Medical Image Segmentation [63.476899335138164]
  Adapting the source model to target data distribution at test-time is an efficient solution for the data-shift problem. We propose a new framework called Adaptive UNet where each convolutional block is equipped with an adaptive batch normalization layer. During test-time, the model takes in just the new test image and generates a domain code to adapt the features of source model according to the test data.
  arXiv  Detail & Related papers  (2022-03-10T18:51:29Z)
• 1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions [79.71065005161566]
  1-D convolutional neural network models are trained for classification of short-range sequences. Model provides prediction with high average accuracy on a hold out test set.
  arXiv  Detail & Related papers  (2020-02-06T17:25:37Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)
• CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images [0.0]
  We develop an automated model to identify stenosis severity in MPR images. The model predicts one of three classes: 'no stenosis' for normal, 'non-significant' - 1-50% of stenosis detected,'significant' - more than 50% of stenosis. For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level.
  arXiv  Detail & Related papers  (2020-01-23T15:20:22Z)

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.