A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions

• URL: http://arxiv.org/abs/2311.13710v1
• Date: Wed, 22 Nov 2023 22:10:53 GMT
• Title: A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions
• Authors: Hina Raja, Taimur Hassan, Bilal Hassan, Muhammad Usman Akram, Hira Raja, Alaa A Abd-alrazaq, Siamak Yousefi, Naoufel Werghi
• Abstract summary: This paper provides a systematic survey of retinal diseases that cause visual impairments or blindness. It covers both clinical and automated approaches for detecting retinal disease, focusing on studies from the past decade.
• Score: 6.728206045751265
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper provides a systematic survey of retinal diseases that cause visual impairments or blindness, emphasizing the importance of early detection for effective treatment. It covers both clinical and automated approaches for detecting retinal disease, focusing on studies from the past decade. The survey evaluates various algorithms for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of existing literature. This comprehensive study, which reviews both clinical and automated detection methods using different modalities, appears to be unique in its scope. Additionally, the survey serves as a helpful guide for researchers interested in digital retinopathy.

Related papers

• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  This study focuses on the clinical evaluation of medical Synthetic Data Generation using Artificial Intelligence (AI) models. The paper contributes by a) presenting a protocol for the systematic evaluation of synthetic images by medical experts and b) applying it to assess TIDE-II, a novel variational autoencoder-based model for high-resolution WCE image synthesis. The results show that TIDE-II generates clinically relevant WCE images, helping to address data scarcity and enhance diagnostic tools.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment Prediction [0.0]
  This research paper addresses the critical challenge of diabetic retinopathy (DR), a severe complication of diabetes leading to potential blindness. The proposed methodology leverages transfer learning with convolutional neural networks (CNNs) for automatic DR detection using a single fundus photograph. High evaluation scores in Jaccard, F1, recall, precision, and accuracy underscore the model's potential for enhancing diagnostic capabilities in retinal pathology assessment.
  arXiv  Detail & Related papers  (2024-01-05T11:19:24Z)
• Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review [0.0]
  Glaucoma is one of the primary causes of vision loss around the world. Recent developments in deep learning approaches demonstrate potential in automating glaucoma detection. The use of deep learning algorithms may significantly improve the efficacy, usefulness, and accuracy of glaucoma detection.
  arXiv  Detail & Related papers  (2023-11-02T17:39:40Z)
• Deep Learning and Computer Vision for Glaucoma Detection: A Review [0.8379286663107844]
  Glaucoma is the leading cause of irreversible blindness worldwide. Recent advances in computer vision and deep learning have demonstrated the potential for automated assessment. We survey recent studies on AI-based glaucoma diagnosis using fundus, optical coherence tomography, and visual field images.
  arXiv  Detail & Related papers  (2023-07-31T09:49:51Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
  The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
  arXiv  Detail & Related papers  (2022-11-07T17:20:39Z)
• A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography [0.0]
  OCT and OCTA are promising tools for the (early) diagnosis of Alzheimer's disease (AD) interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. The current literature lacks an extensive survey on the diagnosis of Alzheimer's disease or cognitive impairment using OCT or OCTA.
  arXiv  Detail & Related papers  (2022-09-07T08:27:10Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Deep Learning for Medical Anomaly Detection -- A Survey [38.32234937094937]
  This survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. We contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.
  arXiv  Detail & Related papers  (2020-12-04T02:09:37Z)
• Anomalous Example Detection in Deep Learning: A Survey [98.2295889723002]
  This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for Deep Learning applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.
  arXiv  Detail & Related papers  (2020-03-16T02:47:23Z)

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.