The Eye as a Window to Systemic Health: A Survey of Retinal Imaging from Classical Techniques to Oculomics

• URL: http://arxiv.org/abs/2505.04006v1
• Date: Tue, 06 May 2025 22:35:54 GMT
• Title: The Eye as a Window to Systemic Health: A Survey of Retinal Imaging from Classical Techniques to Oculomics
• Authors: Inamullah, Imran Razzak, Shoaib Jameel,
• Abstract summary: The retinal structure assists in assessing the early detection, monitoring of disease progression and intervention for both ocular and non-ocular diseases.<n>The advancement in imaging technology leveraging Artificial Intelligence has seized this opportunity to bridge the gap between the eye and human health.<n>The new frontiers of oculomics in ophthalmology cover both ocular and systemic diseases, and getting more attention to explore them.
• Score: 14.998873360919879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The unique vascularized anatomy of the human eye, encased in the retina, provides an opportunity to act as a window for human health. The retinal structure assists in assessing the early detection, monitoring of disease progression and intervention for both ocular and non-ocular diseases. The advancement in imaging technology leveraging Artificial Intelligence has seized this opportunity to bridge the gap between the eye and human health. This track paves the way for unveiling systemic health insight from the ocular system and surrogating non-invasive markers for timely intervention and identification. The new frontiers of oculomics in ophthalmology cover both ocular and systemic diseases, and getting more attention to explore them. In this survey paper, we explore the evolution of retinal imaging techniques, the dire need for the integration of AI-driven analysis, and the shift of retinal imaging from classical techniques to oculomics. We also discuss some hurdles that may be faced in the progression of oculomics, highlighting the research gaps and future directions.

Related papers

• A System for Accurate Tracking and Video Recordings of Rodent Eye Movements using Convolutional Neural Networks for Biomedical Image Segmentation [1.961086321336988]
  We present a flexible, robust, and highly accurate model for pupil and corneal reflection identification in rodent gaze determination.<n>This is the first paper that demonstrates a highly accurate and practical biomedical image segmentation based convolutional neural network architecture.
  arXiv  Detail & Related papers  (2025-06-09T19:48:32Z)
• AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review [0.09320657506524149]
  Computer-Aided Diagnosis (CADx) systems have emerged as promising tools to assist clinicians in accurately diagnosing glaucoma early. This paper aims to provide a comprehensive overview of AI techniques utilized in CADx systems for glaucoma diagnosis.
  arXiv  Detail & Related papers  (2024-10-21T12:26:53Z)
• Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling [49.52787013516891]
  Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.
  arXiv  Detail & Related papers  (2024-05-14T17:15:28Z)
• Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases [0.2686968510141288]
  Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements.
  arXiv  Detail & Related papers  (2024-04-19T16:34:15Z)
• Periocular biometrics: databases, algorithms and directions [69.35569554213679]
  Periocular biometrics has been established as an independent modality due to concerns on the performance of iris or face systems in uncontrolled conditions. This paper presents a review of the state of the art in periocular biometric research.
  arXiv  Detail & Related papers  (2023-07-26T11:14:36Z)
• Periocular Biometrics: A Modality for Unconstrained Scenarios [66.93179447621188]
  Periocular biometrics includes the externally visible region of the face that surrounds the eye socket. The COVID-19 pandemic has highlighted its importance, as the ocular region remained the only visible facial area even in controlled settings.
  arXiv  Detail & Related papers  (2022-12-28T12:08:27Z)
• A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications [56.458448869572294]
  We introduce DETRtime, a novel framework for time-series segmentation of EEG data. Our end-to-end deep learning-based framework brings advances in Computer Vision to the forefront. Our model generalizes well in the task of EEG sleep stage segmentation.
  arXiv  Detail & Related papers  (2022-06-17T10:17:24Z)
• Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review [0.4215938932388722]
  Glaucoma is a leading cause of irreversible vision impairment globally. Fundus imaging is non-invasive and low-cost. Can artificial intelligence automatically find the boundaries of the optic cup and disc.
  arXiv  Detail & Related papers  (2022-04-12T07:47:13Z)
• MTCD: Cataract Detection via Near Infrared Eye Images [69.62768493464053]
  cataract is a common eye disease and one of the leading causes of blindness and vision impairment. We present a novel algorithm for cataract detection using near-infrared eye images. Deep learning-based eye segmentation and multitask network classification networks are presented.
  arXiv  Detail & Related papers  (2021-10-06T08:10:28Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)

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.