Predicting risk of cardiovascular disease using retinal OCT imaging

• URL: http://arxiv.org/abs/2403.18873v1
• Date: Tue, 26 Mar 2024 14:42:46 GMT
• Title: Predicting risk of cardiovascular disease using retinal OCT imaging
• Authors: Cynthia Maldonado-Garcia, Rodrigo Bonazzola, Enzo Ferrante, Thomas H Julian, Panagiotis I Sergouniotis, Nishant Ravikumara, Alejandro F Frangi,
• Abstract summary: We investigated the potential of optical coherence tomography as an additional imaging technique to predict future cardiovascular disease (CVD) We utilised a self-supervised deep learning approach based on Variational Autoencoders (VAE) to learn low-dimensional representations of high-dimensional 3D OCT images. The choroidal layer visible in OCT images was identified as an important predictor of future CVD events using a novel approach to model explanability.
• Score: 40.71667870702634
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We investigated the potential of optical coherence tomography (OCT) as an additional imaging technique to predict future cardiovascular disease (CVD). We utilised a self-supervised deep learning approach based on Variational Autoencoders (VAE) to learn low-dimensional representations of high-dimensional 3D OCT images and to capture distinct characteristics of different retinal layers within the OCT image. A Random Forest (RF) classifier was subsequently trained using the learned latent features and participant demographic and clinical data, to differentiate between patients at risk of CVD events (MI or stroke) and non-CVD cases. Our predictive model, trained on multimodal data, was assessed based on its ability to correctly identify individuals likely to suffer from a CVD event(MI or stroke), within a 5-year interval after image acquisition. Our self-supervised VAE feature selection and multimodal Random Forest classifier differentiate between patients at risk of future CVD events and the control group with an AUC of 0.75, outperforming the clinically established QRISK3 score (AUC= 0.597). The choroidal layer visible in OCT images was identified as an important predictor of future CVD events using a novel approach to model explanability. Retinal OCT imaging provides a cost-effective and non-invasive alternative to predict the risk of cardiovascular disease and is readily accessible in optometry practices and hospitals.

Related papers

• A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans [12.652540031719571]
  We propose a novel joint representation that integrates discrete quantitative biomarkers and continuous deep features extracted from chest CT scans. Our method substantially improves CVD risk predictive performance and offers individual contribution analysis of each biomarker.
  arXiv  Detail & Related papers  (2024-10-24T10:06:45Z)
• Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction [47.7045293755736]
  Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography ( OCT) imaging combined with fundus photographs for identifying future adverse cardiac events. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not.
  arXiv  Detail & Related papers  (2024-10-18T12:37:51Z)
• Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data [3.790959613880792]
  Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction.
  arXiv  Detail & Related papers  (2023-07-07T13:09:07Z)
• Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence [20.415917092103033]
  Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD) The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing.
  arXiv  Detail & Related papers  (2022-10-26T10:34:20Z)
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction [68.8204255655161]
  We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data. Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
  arXiv  Detail & Related papers  (2022-04-20T17:28:08Z)
• Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review [17.021934273900182]
  Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods to examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS) and lacunes of presumed vascular origin.
  arXiv  Detail & Related papers  (2022-04-04T12:01:39Z)
• Application of Homomorphic Encryption in Medical Imaging [60.51436886110803]
  We show how HE can be used to make predictions over medical images while preventing unauthorized secondary use of data. We report some experiments using 3D chest CT-Scans for a nodule detection task.
  arXiv  Detail & Related papers  (2021-10-12T19:57:12Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)

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.