Artificial intelligence applications and cataract management: A systematic review

Artificial intelligence (AI)-based applications exhibit the potential to improve the quality and efficiency of patient care in different fields, including cataract management. A systematic review of the different applications of AI-based software on all aspects of a cataract patient's management, from diagnosis to follow-up, was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. All selected articles were analyzed to assess the level of evidence according to the Oxford Centre for Evidence-Based Medicine 2011 guidelines, and the quality of evidence according to the Grading of Recommendations Assessment, Development and Evaluation system. Of the articles analyzed, 49 met the inclusion criteria. No data synthesis was possible for the heterogeneity of available data and the design of the available studies. The AI-driven diagnosis seemed to be comparable and, in selected cases, to even exceed the accuracy of experienced clinicians in classifying disease, supporting the operating room scheduling, and intraoperative and postoperative management of complications. Considering the heterogeneity of data analyzed, however, further randomized controlled trials to assess the efficacy and safety of AI application in the management of cataract should be highly warranted

Current roles of artificial intelligence in ophthalmology

Artificial intelligence (AI) studies are increasingly reporting successful results in the diagnosis and prognosis prediction of ophthalmological diseases as well as systemic disorders. The goal of this review is to detail how AI can be utilized in making diagnostic predictions to enhance the clinical setting. It is crucial to keep improving methods that emphasize clarity in AI models. This makes it possible to evaluate the information obtained from ocular imaging and easily incorporate it into therapeutic decision-making procedures. This will contribute to the wider acceptance and adoption of AI-based ocular imaging in healthcare settings combining advanced machine learning and deep learning techniques with new developments. Multiple studies were reviewed and evaluated, including AI-based algorithms, retinal images, fundus and optic nerve head (ONH) photographs, and extensive expert reviews. In these studies, carried out in various countries and laboratories of the world, it is seen those complex diagnoses, which can be detected systemic diseases from ophthalmological images, can be made much faster and with higher predictability, accuracy, sensitivity, and specificity, in addition to ophthalmological diseases, by comparing large numbers of images and teaching them to the computer. It is now clear that it can be taken advantage of AI to achieve diagnostic certainty. Collaboration between the fields of medicine and engineering foresees promising advances in improving the predictive accuracy and precision of future medical diagnoses achieved by training machines with this information. However, it is important to keep in mind that each new development requires new additions or updates to various social, psychological, ethical, and legal regulations

A review of artificial intelligence applications in anterior segment ocular diseases

Background: Artificial intelligence (AI) has great potential for interpreting and analyzing images and processing large amounts of data. There is a growing interest in investigating the applications of AI in anterior segment ocular diseases. This narrative review aims to assess the use of different AI-based algorithms for diagnosing and managing anterior segment entities. Methods: We reviewed the applications of different AI-based algorithms in the diagnosis and management of anterior segment entities, including keratoconus, corneal dystrophy, corneal grafts, corneal transplantation, refractive surgery, pterygium, infectious keratitis, cataracts, and disorders of the corneal nerves, conjunctiva, tear film, anterior chamber angle, and iris. The English-language databases PubMed/MEDLINE, Scopus, and Google Scholar were searched using the following keywords: artificial intelligence, deep learning, machine learning, neural network, anterior eye segment diseases, corneal disease, keratoconus, dry eye, refractive surgery, pterygium, infectious keratitis, anterior chamber, and cataract. Relevant articles were compared based on the use of AI models in the diagnosis and treatment of anterior segment diseases. Furthermore, we prepared a summary of the diagnostic performance of the AI-based methods for anterior segment ocular entities. Results: Various AI methods based on deep and machine learning can analyze data obtained from corneal imaging modalities with acceptable diagnostic performance. Currently, complicated and time-consuming manual methods are available for diagnosing and treating eye diseases. However, AI methods could save time and prevent vision impairment in eyes with anterior segment diseases. Because many anterior segment diseases can cause irreversible complications and even vision loss, sufficient confidence in the results obtained from the designed model is crucial for decision-making by experts. Conclusions: AI-based models could be used as surrogates for analyzing manual data with improveddiagnostic performance. These methods could be reliable tools for diagnosing and managing anterior segmentocular diseases in the near future in remote areas. It is expected that future studies can design algorithms thatuse less data in a multitasking manner for the detection and management of anterior segment diseases

Computational models for stuctural analysis of retinal images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe evaluation of retina structures has been of great interest because it could be used as a non-intrusive diagnosis in modern ophthalmology to detect many important eye diseases as well as cardiovascular disorders. A variety of retinal image analysis tools have been developed to assist ophthalmologists and eye diseases experts by reducing the time required in eye screening, optimising the costs as well as providing efficient disease treatment and management systems. A key component in these tools is the segmentation and quantification of retina structures. However, the imaging artefacts such as noise, intensity homogeneity and the overlapping tissue of retina structures can cause significant degradations to the performance of these automated image analysis tools. This thesis aims to provide robust and reliable automated retinal image analysis technique to allow for early detection of various retinal and other diseases. In particular, four innovative segmentation methods have been proposed, including two for retinal vessel network segmentation, two for optic disc segmentation and one for retina nerve fibre layers detection. First, three pre-processing operations are combined in the segmentation method to remove noise and enhance the appearance of the blood vessel in the image, and a Mixture of Gaussians is used to extract the blood vessel tree. Second, a graph cut segmentation approach is introduced, which incorporates the mechanism of vectors flux into the graph formulation to allow for the segmentation of very narrow blood vessels. Third, the optic disc segmentation is performed using two alternative methods: the Markov random field image reconstruction approach detects the optic disc by removing the blood vessels from the optic disc area, and the graph cut with compensation factor method achieves that using prior information of the blood vessels. Fourth, the boundaries of the retinal nerve fibre layer (RNFL) are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. The strong experimental results of our retinal blood vessel segmentation methods including Mixture of Gaussian, Graph Cut achieved an average accuracy of 94:33%, 94:27% respectively. Our optic disc segmentation methods including Markov Random Field and Compensation Factor also achieved an average sensitivity of 92:85% and 85:70% respectively. These results obtained on several public datasets and compared with existing methods have shown that our proposed methods are robust and efficient in the segmenting retinal structures such the blood vessels and the optic disc.Brunel University Londonhttp://bura.brunel.ac.uk/bitstream/2438/10387/1/FulltextThesis.pd

Digital ocular fundus imaging: a review

Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs.Fundação para a Ciência e TecnologiaFEDErPrograma COMPET

Deep Feature Fusion Network for Computer-aided Diagnosis of Glaucoma using Optical Coherence Tomography

학위논문 (박사)-- 서울대학교 대학원 공과대학 협동과정 바이오엔지니어링전공, 2017. 8. 김희찬.Glaucoma has been able to be diagnosed noninvasively by analyzing the optic disc thickness with the development of optical coherence tomography. However, it is essential to maintain proper intraocular pressure through early diagnosis of glaucoma. Therefore, it is required to develop a computer-aided diagnosis system to accurately and objectively analyze glaucoma of early stage. In this paper, we propose deep feature fusion network for realizing computer-aided system which can accurately diagnose early glaucoma and verify the clinical efficacy through performance evaluation using patient images. Deep feature fusion network is analyzed by fusing features which are extracted by feature-based classification used in machine learning and by deep learning in deep neural network. Deep feature fusion network is deep neural network composed of heterogeneous features extracted through image processing and deep learning. The area and depth features of optic nerve defects related to glaucoma were extracted by using traditional image processing methods and the features related to distinction between glaucoma and normal subjects were extracted from the middle layer output of the deep neural network. Deep feature fusion network was developed by fusing extracted features. We analyzed features based on image processing using thickness map and deviation map of retinal nerve fiber layer and ganglion cell inner plexiform layer in order to extract features related to the area of the optic nerve defects. Optic nerve defects were segmented in each deviation map by three criteria and the area of the defects was calculated about 69 glaucoma patients and 79 normal subjects. The performance of the severity indices calculated by defects area was evaluated by the area under ROC curve (AUC). There were significant differences between glaucoma patients and normal subjects in all severity indices (p < 0.0001) and correctly distinguished between glaucoma patients and normal subjects (AUC = 0.91 to 0.95). This suggests that the area features of optic nerve defects can be used as an objective indicator of glaucoma diagnosis. We analyzed features based on another image processing using retinal nerve fiber layer thickness map and deviation map to extract the features related to the depth of the optic nerve defects. Depth related index was developed by using the ratio of the optic nerve thickness of the normal to the optic nerve thickness in the optic nerve defects analyzed by the deviation map. 108 early glaucoma patients, 96 moderate glaucoma patients, and 111 severe glaucoma patients were analyzed by using depth index and the performance was evaluated by AUC. There were significant differences between the groups in the index (p < 0.001) and the index discriminated between moderate glaucoma patients and severe glaucoma patients (AUC = 0.97) as well as early glaucoma patients and moderate glaucoma patients (AUC = 0.98). It was found that the depth index of the optic nerve defects were a significant feature to distinguish the degree of glaucoma. Two methods were used to apply thickness map to deep learning. One method is deep learning using randomly distributed weights in LeNet and the other method is deep learning using weights pre-trained by other large image data in VGGNet. We analyzed two methods for 316 normal subjects, 226 glaucoma patients of early stage, and 246 glaucoma patients of moderate and severe stage and evaluated performance through AUC for each groups. Deep neural networks learned with LeNet and VGGNet distinguished normal subjects not only from glaucoma patients (AUC = 0.94, 0.94), but also from glaucoma patients of early stage (AUC = 0.88, 0.89). It was found that two deep learning methods extract the features related to glaucoma. Finally, we developed deep feature fusion network by fusing the features extracted from image processing and the features extracted by deep learning and compared the performance with the previous studies though AUC. Deep feature fusion network fusing the features extracted in VGGNet correctly distinguished normal subjects not only from glaucoma patients (AUC = 0.96), but also from glaucoma patients of early stage (AUC = 0.92). This network is superior to the previous study (AUC = 0.91, 0.82). It showed excellent performance in distinguishing early glaucoma patients from normal subjects particularly. These results show that the proposed deep feature fusion network provides higher accuracy in diagnosis and early diagnosis of glaucoma than any other previous methods. It is expected that further accuracy of the features will be improved if additional features of demographic information and various glaucoma test results are added to deep feature fusion network. Deep feature fusion network proposed in this paper is expected to be applicable not only to early diagnosis of glaucoma but also to analyze progress of glaucoma.Chapter 1 : General Introduction 1 1.1. Glaucoma 2 1.2. Optical Coherence Tomography 5 1.3. Thesis Objectives 7 Chapter 2 : Feature Extraction for Glaucoma Diagnosis 1. Severity Index of Macular GCIPL and Peripapillary RNFL Deviation Maps 9 2.1. Introduction 10 2.2. Methods 12 2.2.1. Study subjects 12 2.2.2. Red-free RNFL photography 14 2.2.3. Cirrus OCT imaging 15 2.2.4. Deviation map analysis protocol 17 2.2.5. Statistical analysis 21 2.3. Results 23 2.4. Discussion 33 Chapter 3 : Feature Extraction for Glaucoma Diagnosis 2. RNFL Defect Depth Percentage Index of Thickness Deviation Maps 41 3.1. Introduction 42 3.2. Methods 44 3.2.1. Subjects 44 3.2.2. Red-free fundus photography imaging 46 3.2.3. Optical coherence tomography retinal nerve fiber layer imaging 51 3.2.4. Measuring depth of retinal nerve fiber layer defects on cirrus high-definition optical coherence tomography derived deviation map 52 3.2.5. Data analysis 57 3.3. Results 58 3.4. Discussion 69 Chapter 4 : Glaucoma Classification using Deep Feature Fusion Network 74 4.1. Introduction 75 4.2. Methods 77 4.2.1. Study subjects 77 4.2.2. OCT imaging 79 4.2.3. Deep Feature Fusion Network 81 4.2.4. Statistical analysis 88 4.3. Results 90 4.4. Discussion 105 Chapter 5 : Thesis Summary and Future Work 111 5.1 Thesis Summary and Contribution 112 5.2 Future Work 115 Bibliography 117 Abstract in Korean 125Docto

Effectiveness of Machine Learning Classifiers for Cataract Screening

Cataract is the leading cause of blindness and vision loss globally. The implementation of artificial intelligence (AI) in the healthcare industry has been on the rise in the past few decades and machine learning (ML) classifiers have shown to be able to diagnose patients with cataracts. A systematic review and meta-analysis were conducted to assess the diagnostic accuracy of these ML classifiers for cataracts currently published in the literature. Retrieved from nine articles, the pooled sensitivity was 94.8% and the specificity was 96.0% for adult cataracts. Additionally, an economic analysis was conducted to explore the cost-effectiveness of implementing ML to diagnostic eye camps in rural Nepal compared to traditional diagnostic eye camps. There was a total of 22,805 patients included in the decision tree, and the ML-based eye camp was able to identify 31 additional cases of cataracts, and 2546 additional cases of non-cataract

Technological Advances in the Diagnosis and Management of Pigmented Fundus Tumours

Choroidal naevi are the most common intraocular tumour. They can be pigmented or non-pigmented and have a predilection for the posterior uvea. The majority remain undetected and cause no harm but are increasingly found on routine community optometry examinations. Rarely does a naevus demonstrate growth or the onset of suspicious features to fulfil the criteria for a malignant melanoma. Because of this very small risk, optometrists commonly refer these patients to hospital eye units for a second opinion, triggering specialist examination and investigation, causing significant anxiety to patients and stretching medical resources. This PhD thesis introduces the MOLES acronym and scoring system that has been devised to categorise the risk of malignancy in choroidal melanocytic tumours according to Mushroom tumour shape, Orange pigment, Large tumour size, Enlarging tumour and Subretinal fluid. This is a simplified system that can be used without sophisticated imaging, and hence its main utility lies in the screening of patients with choroidal pigmented lesions in the community and general ophthalmology clinics. Under this system, lesions were categorised by a scoring system as ‘common naevus’, ‘low-risk naevus’, ‘high-risk naevus’ and ‘probable melanoma.’ According to the sum total of the scores, the MOLES system correlates well with ocular oncologists’ final diagnosis. The PhD thesis also describes a model of managing such lesions in a virtual pathway, showing that images of choroidal naevi evaluated remotely using a decision-making algorithm by masked non-medical graders or masked ophthalmologists is safe. This work prospectively validates a virtual naevus clinic model focusing on patient safety as the primary consideration. The idea of a virtual naevus clinic as a fast, one-stop, streamlined and comprehensive service is attractive for patients and healthcare systems, including an optimised patient experience with reduced delays and inconvenience from repeated visits. A safe, standardised model ensures homogeneous management of cases, appropriate and prompt return of care closer to home to community-based optometrists. This research work and strategies, such as the MOLES scoring system for triage, could empower community-based providers to deliver management of benign choroidal naevi without referral to specialist units. Based on the positive outcome of this prospective study and the MOLES studies, a ‘Virtual Naevus Clinic’ has been designed and adapted at Moorfields Eye Hospital (MEH) to prove its feasibility as a response to the COVID-19 pandemic, and with the purpose of reducing in-hospital patient journey times and increasing the capacity of the naevus clinics, while providing safe and efficient clinical care for patients. This PhD chapter describes the design, pathways, and operating procedures for the digitally enabled naevus clinics in Moorfields Eye Hospital, including what this service provides and how it will be delivered and supported. The author will share the current experience and future plan. Finally, the PhD thesis will cover a chapter that discusses the potential role of artificial intelligence (AI) in differentiating benign choroidal naevus from choroidal melanoma. The published clinical and imaging risk factors for malignant transformation of choroidal naevus will be reviewed in the context of how AI applied to existing ophthalmic imaging systems might be able to determine features on medical images in an automated way. The thesis will include current knowledge to date and describe potential benefits, limitations and key issues that could arise with this technology in the ophthalmic field. Regulatory concerns will be addressed with possible solutions on how AI could be implemented in clinical practice and embedded into existing imaging technology with the potential to improve patient care and the diagnostic process. The PhD will also explore the feasibility of developed automated deep learning models and investigate the performance of these models in diagnosing choroidal naevomelanocytic lesions based on medical imaging, including colour fundus and autofluorescence fundus photographs. This research aimed to determine the sensitivity and specificity of an automated deep learning algorithm used for binary classification to differentiate choroidal melanomas from choroidal naevi and prove that a differentiation concept utilising a machine learning algorithm is feasible