AI-based Advanced approaches and dry eye disease detection based on multi-source evidence: Cases, applications, issues, and future directions

MH Wang, L Xing, Y Pan, F Gu, J Fang… - Big Data Mining and …, 2024 - ieeexplore.ieee.org
This study explores the potential of Artificial Intelligence (AI) in early screening and
prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic …

AI-based methods for detecting and classifying age-related macular degeneration: a comprehensive review

NN El-Den, M Elsharkawy, I Saleh, M Ghazal… - Artificial Intelligence …, 2024 - Springer
This paper explores the advancements and achievements of artificial intelligence (AI) in
computer vision (CV), particularly in the context of diagnosing and grading age-related …

[HTML][HTML] A Method for Ocular Disease Diagnosis through Visual Prediction Explainability

A Santone, M Cesarelli, E Colasuonno, V Bevilacqua… - Electronics, 2024 - mdpi.com
Ocular diseases can range in severity, with some being more serious than others. As a
matter of fact, there are several common and severe eye diseases, for instance, glaucoma …

Can Explainable Artificial Intelligence Optimize the Data Quality of Machine Learning Model? Taking Meibomian Gland Dysfunction Detections as a Case Study

MH Wang, R Zhou, Z Lin, Y Yu, P Zeng… - Journal of Physics …, 2023 - iopscience.iop.org
Data quality plays a crucial role in computer-aided diagnosis (CAD) for ophthalmic disease
detection. Various methodologies for data enhancement and preprocessing exist, with …

Applications of Explainable Artificial Intelligent Algorithms to Age-related Macular Degeneration Diagnosis: A Case Study Based on CNN, Attention, and CAM …

M Wang, Z Lin, J Zhou, L Xing… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
The recent years have witnessed extensive research on Explainable Artificial Intelligence
(XAI) algorithms in the field of ophthalmology. This paper introduces an improved deep …

Developing a transparent diagnosis model for diabetic retinopathy using explainable AI

T Shahzad, M Saleem, MS Farooq, S Abbas… - IEEE …, 2024 - ieeexplore.ieee.org
Diabetic retinopathy is a leading cause of vision complications and partially sighted which
pose considerable diagnostic difficulties because of its diverse and varying symptoms …

Artificial Intelligence in Retinal Screening Using OCT Images: A Review of the Last Decade (2013-2023)

MH Akpinar, A Sengur, O Faust, L Tong… - Computer Methods and …, 2024 - Elsevier
Background and objectives Optical coherence tomography (OCT) has ushered in a
transformative era in the domain of ophthalmology, offering non-invasive imaging with high …

[HTML][HTML] Recent Applications of Explainable AI (XAI): A Systematic Literature Review

M Saarela, V Podgorelec - Applied Sciences, 2024 - mdpi.com
This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …

[HTML][HTML] Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence

S Abbas, A Qaisar, MS Farooq, M Saleem, M Ahmad… - Sensors, 2024 - mdpi.com
The early prediction of ocular disease is certainly an obligatory concern in the domain of
ophthalmic medicine. Although modern scientific discoveries have shown the potential to …

Optimizing Real-Time Trichiasis Object Detection: A Comparative Analysis of YOLOv5 and YOLOv8 Performance Metrics

MH Wang, Y Yu, Z Lin, P Zeng, H Liu… - … on Systems and …, 2023 - ieeexplore.ieee.org
This study evaluates and compares the performance of YOLOv5 and YOLOv8 in trichiasis
object detection tasks, focusing on key metrics such as mean Average Precision (mAP) …