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

A Review of the Latest Machine Learning Advances in Cataract Diagnosis

Cataract disorder is one of the most common vision disorders in the world. As the average age of the world population increases, many people suffer from it in middle and old age. Timely diagnosis can prevent the reduction of vision and eventually loss of sight. Considering the prevalence of Artificial Intelligence algorithms, especially in the medical industry, they could be used for Cataract diagnosis, IOL determination, and PCO diagnosis. According to the studies, the proposed models for Cataract diagnosis are very accurate. These developed algorithms have been able to make access to ophthalmology services easier and reduce treatment costs significantly

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

The Impact of Fundus Autofluorescence on the Management of Age-related Macular Degeneration

Background: Fundus autofluorescence (FAF) has been described as a topographical map of fluorophores that accumulate within the retinal pigment epithelium as a result of disease. Study aims: To evaluate whether FAF offers information relevant to age-related macular degeneration over that gathered via colour fundus photography (CFP) and optical coherence tomography (OCT). Methods: Ninety-three patients were imaged via CFP, OCT and FAF and the results analysed using Orange Data Mining artificial intelligence and SPSS software. Results: Pupillary dilation makes a significant improvement to FAF image quality. Nuclear sclerotic cataract of > 1.5 on the World Health Organisation scale indicates that there is ≃85% probability that the FAF image will not be of high quality. At > 1.9 there is ≃50% probability of the image not being clinically useful as defined by a novel grading scale. Age was negatively associated with FAF comfort. There is ≥ 90% probability of an abnormal FAF result for an eye with any of the following: > 50 small, > 40 intermediate, > 20 large drusen. Age > 92 years. > 30 packet years of smoking. Any pigmentary abnormalities. ≃80% for any reticular pseudodrusen (RPD). FAF results can be predicted via CFP and OCT data using machine learning with informedness of up to 70.2% and area under the curve (AUC) of 0.903. For transfer learning to be useful within primary care, image pre-processing is likely to be required. Geographic atrophy and pigment epithelial detachments appear to be linked to a patchy FAF pattern. RPD are linked to a reticular FAF pattern. Principle component analysis indicates that drusen were responsible for the greatest percentage of variability in this study’s data (38.6%). Conclusions: Clinical impact: FAF results can be predicted from CFP/OCT via machine learning with 70.2% informedness and AUC of 0.903. Drusen number/size were the most informative variables

Application of artificial intelligence for automatic cataract staging based on anterior segment images: comparing automatic segmentation approaches to manual segmentation

PurposeCataract is one of the leading causes of blindness worldwide, accounting for >50% of cases of blindness in low- and middle-income countries. In this study, two artificial intelligence (AI) diagnosis platforms are proposed for cortical cataract staging to achieve a precise diagnosis.MethodsA total of 647 high quality anterior segment images, which included the four stages of cataracts, were collected into the dataset. They were divided randomly into a training set and a test set using a stratified random-allocation technique at a ratio of 8:2. Then, after automatic or manual segmentation of the lens area of the cataract, the deep transform-learning (DTL) features extraction, PCA dimensionality reduction, multi-features fusion, fusion features selection, and classification models establishment, the automatic and manual segmentation DTL platforms were developed. Finally, the accuracy, confusion matrix, and area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the two platforms.ResultsIn the automatic segmentation DTL platform, the accuracy of the model in the training and test sets was 94.59 and 84.50%, respectively. In the manual segmentation DTL platform, the accuracy of the model in the training and test sets was 97.48 and 90.00%, respectively. In the test set, the micro and macro average AUCs of the two platforms reached >95% and the AUC for each classification was >90%. The results of a confusion matrix showed that all stages, except for mature, had a high recognition rate.ConclusionTwo AI diagnosis platforms were proposed for cortical cataract staging. The resulting automatic segmentation platform can stage cataracts more quickly, whereas the resulting manual segmentation platform can stage cataracts more accurately

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications

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