The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review

Enhancing Readability and Detection of Age-Related Macular Degeneration Using Optical Coherence Tomography Imaging: An AI Approach

Artificial intelligence has been used effectively in medical diagnosis. The objective of this project is to examine the application of a collective AI model using weighted fusion of predicted probabilities from different AI architectures to diagnose various retinal conditions based on optical coherence tomography (OCT). A publicly available Noor dataset, comprising 16,822, images from 554 retinal OCT scans of 441 patients, was used to predict a diverse spectrum of age-related macular degeneration (AMD) stages: normal, drusen, or choroidal neovascularization. These predictions were compared with predictions from ResNet, EfficientNet, and Attention models, respectively, using precision, recall, F1 score, and confusion matric and receiver operating characteristics curves. Our collective model demonstrated superior accuracy in classifying AMD compared to individual ResNet, EfficientNet, and Attention models, showcasing the effectiveness of using trainable weights in the ensemble fusion process, where these weights dynamically adapt during training rather than being fixed values. Specifically, our ensemble model achieved an accuracy of 91.88%, precision of 92.54%, recall of 92.01%, and F1 score of 92.03%, outperforming individual models. Our model also highlights the refinement process undertaken through a thorough examination of initially misclassified cases, leading to significant improvements in the model's accuracy rate to 97%. This study also underscores the potential of AI as a valuable tool in ophthalmology. The proposed ensemble model, combining different mechanisms highlights the benefits of model fusion for complex medical image analysis.

Changing Trends in the Global Burden of Cataract Over the Past 30 Years: Retrospective Data Analysis of the Global Burden of Disease Study 2019

BACKGROUND

Cataracts now account for the largest proportion of the global burden of blindness and vision loss. Understanding the changing trends in the global burden of cataracts over the past 30 years and the next 15 years is of clear significance for the prevention and control of cataracts in key populations. As far as we know, research on the future burden of cataracts is lacking.

OBJECTIVE

This study aims to assess the global burden of cataracts over the past 30 years by using age-period-cohort modeling and to estimate trends in the next 15 years.

METHODS

Data were obtained from the Global Burden of Disease Study 2019, the United Nations Development Programme, and the WHO (World Health Organization) Global Health Observatory data repository. The assessment of trends and disparities in the number and rate of disability-adjusted life years (DALYs) for cataracts from 1990 to 2019 was conducted. The association between the age-standardized DALY rate (ASDR) and the socio-demographic index (SDI), human development index (HDI), national levels of particulate matter <2.5 μm in diameter (PM2.5), and ambient ultraviolet radiation (UVR) was determined using linear regression analysis. Additionally, we used the Nordpred (Harald Fekjær and Bjørn Møller) age-period-cohort model to predict the cataract burden from 2020 to 2034.

RESULTS

Globally, the number of DALYs due to cataract increased from 3,492,604 (95% uncertainty interval [UI] 2,481,846-4,719,629) in 1990 to 6,676,281 (95% UI 4,761,210-9,006,193) in 2019. The ASDRs due to cataract decreased from 93.17 (95% UI 66.14-125.32) in 1990 to 82.94 (95% UI 59.06-111.75) in 2019, with an average annual percentage change of -0.37 (95% CI -0.44 to -0.3; P<.001). Age, female sex, air pollution, smoking, high fasting plasma glucose levels, and a high body mass index were risk factors for the burden of cataracts. SDI and HDI were negatively correlated with ASDRs of cataracts, while PM2.5 and UVR were positively associated with them. Higher DALY rates were also associated with lower SDI (R2=0.1939; P<.001), lower HDI (R2=0.2828; P<.001), national PM2.5 concentration (R2=0.1874; P<.001), and ambient UVR levels (R2=0.2354; P<.001). The prediction model suggested that the number of DALYs due to cataract will continue to rise globally, while the cataract DALY rate will continue to decrease.

CONCLUSIONS

While the ASDR of cataracts has decreased, there has been a notable increase in the number of DALYs over the past 30 years. Projections suggest that the global burden of cataracts will continue to rise over the next 15 years. To address this challenge, appropriate prevention and treatment policies must be implemented.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

EyeCNN: exploring the potential of convolutional neural networks for identification of multiple eye diseases through retinal imagery

BACKGROUND

The eyes are the most important part of the human body as these are directly connected to the brain and help us perceive the imagery in daily life whereas, eye diseases are mostly ignored and underestimated until it is too late. Diagnosing eye disorders through manual diagnosis by the physician can be very costly and time taking.

OBJECTIVE

Thus, to tackle this, a novel method namely EyeCNN is proposed for identifying eye diseases through retinal images using EfficientNet B3.

METHODS

A dataset of retinal imagery of three diseases, i.e. Diabetic Retinopathy, Glaucoma, and Cataract is used to train 12 convolutional networks while EfficientNet B3 was the topperforming model out of all 12 models with a testing accuracy of 94.30%.

RESULTS

After preprocessing of the dataset and training of models, various experimentations were performed to see where our model stands. The evaluation was performed using some well-defined measures and the final model was deployed on the Streamlit server as a prototype for public usage. The proposed model has the potential to help diagnose eye diseases early, which can facilitate timely treatment.

CONCLUSION

The use of EyeCNN for classifying eye diseases has the potential to aid ophthalmologists in diagnosing conditions accurately and efficiently. This research may also lead to a deeper understanding of these diseases and it may lead to new treatments. The webserver of EyeCNN can be accessed at ( https://abdulrafay97-eyecnn-app-rd9wgz.streamlit.app/ ).

OCT-based deep-learning models for the identification of retinal key signs

A new system based on binary Deep Learning (DL) convolutional neural networks has been developed to recognize specific retinal abnormality signs on Optical Coherence Tomography (OCT) images useful for clinical practice. Images from the local hospital database were retrospectively selected from 2017 to 2022. Images were labeled by two retinal specialists and included central fovea cross-section OCTs. Nine models were developed using the Visual Geometry Group 16 architecture to distinguish healthy versus abnormal retinas and to identify eight different retinal abnormality signs. A total of 21,500 OCT images were screened, and 10,770 central fovea cross-section OCTs were included in the study. The system achieved high accuracy in identifying healthy retinas and specific pathological signs, ranging from 93 to 99%. Accurately detecting abnormal retinal signs from OCT images is crucial for patient care. This study aimed to identify specific signs related to retinal pathologies, aiding ophthalmologists in diagnosis. The high-accuracy system identified healthy retinas and pathological signs, making it a useful diagnostic aid. Labelled OCT images remain a challenge, but our approach reduces dataset creation time and shows DL models' potential to improve ocular pathology diagnosis and clinical decision-making.