40 research outputs found
Current and future roles of artificial intelligence in retinopathy of prematurity
Retinopathy of prematurity (ROP) is a severe condition affecting premature
infants, leading to abnormal retinal blood vessel growth, retinal detachment,
and potential blindness. While semi-automated systems have been used in the
past to diagnose ROP-related plus disease by quantifying retinal vessel
features, traditional machine learning (ML) models face challenges like
accuracy and overfitting. Recent advancements in deep learning (DL), especially
convolutional neural networks (CNNs), have significantly improved ROP detection
and classification. The i-ROP deep learning (i-ROP-DL) system also shows
promise in detecting plus disease, offering reliable ROP diagnosis potential.
This research comprehensively examines the contemporary progress and challenges
associated with using retinal imaging and artificial intelligence (AI) to
detect ROP, offering valuable insights that can guide further investigation in
this domain. Based on 89 original studies in this field (out of 1487 studies
that were comprehensively reviewed), we concluded that traditional methods for
ROP diagnosis suffer from subjectivity and manual analysis, leading to
inconsistent clinical decisions. AI holds great promise for improving ROP
management. This review explores AI's potential in ROP detection,
classification, diagnosis, and prognosis.Comment: 28 pages, 8 figures, 2 tables, 235 references, 1 supplementary tabl
Deep learning in ophthalmology: The technical and clinical considerations
The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally
Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity
Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder
because of damage to the eye's retina which can affect babies born prematurely.
Screening of ROP is essential for early detection and treatment. This is a
laborious and manual process which requires trained physician performing
dilated ophthalmological examination which can be subjective resulting in lower
diagnosis success for clinically significant disease. Automated diagnostic
methods can assist ophthalmologists increase diagnosis accuracy using deep
learning. Several research groups have highlighted various approaches. This
paper proposes the use of new novel fundus preprocessing methods using
pretrained transfer learning frameworks to create hybrid models to give higher
diagnosis accuracy. The evaluations show that these novel methods in comparison
to traditional imaging processing contribute to higher accuracy in classifying
Plus disease, Stages of ROP and Zones. We achieve accuracy of 97.65% for Plus
disease, 89.44% for Stage, 90.24% for Zones with limited training dataset.Comment: 10 pages, 4 figures, 7 tables. arXiv admin note: text overlap with
arXiv:1904.08796 by other author
Artificial intelligence in retinal disease: clinical application, challenges, and future directions
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans
The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review
Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology
A Review of the Management of Eye Diseases Using Artificial Intelligence, Machine Learning, and Deep Learning in Conjunction with Recent Research on Eye Health Problems: Eye Microbiome
In the field of computer science, Artificial Intelligence can be considered one of the branches that study the development of algorithms that mimic certain aspects of human intelligence. Over the past few years, there has been a rapid advancement in the technology of computer-aided diagnosis (CAD). This in turn has led to an increase in the use of deep learning methods in a variety of applications. For us to be able to understand how AI can be used in order to recognize eye diseases, it is crucial that we have a deep understanding of how AI works in its core concepts. This paper aims to describe the most recent and applicable uses of artificial intelligence in the various fields of ophthalmology disease
Artificial intelligence and deep learning in ophthalmology
Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI 'black-box' algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward
An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective
The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care