Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Applications of Artificial Intelligence in Ophthalmology: General Overview
Wei Lü, Yan Tong, Yue Yu, et al.
Journal of Ophthalmology (2018) Vol. 2018, pp. 1-15
Open Access | Times Cited: 125

Showing 1-25 of 125 citing articles:

An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)
Meha Desai, Manan Shah
Clinical eHealth (2020) Vol. 4, pp. 1-11
Open Access | Times Cited: 340
Advances in Hyaluronic Acid for Biomedical Applications
Aqeela Yasin, Ying Ren, Jingan Li, et al.
Frontiers in Bioengineering and Biotechnology (2022) Vol. 10
Open Access | Times Cited: 140
Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review
Tadeusz Ciecierski-Holmes, Ritvij Singh, Miriam Axt, et al.
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 104
Gen-AI integration in higher education: Predicting intentions using SEM-ANN approach
K. Keerthi Jain, J. Naga Venkata Raghuram
Education and Information Technologies (2024) Vol. 29, Iss. 13, pp. 17169-17209
Closed Access | Times Cited: 17
Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology
Lokman Balyen, Tünde Pető
Asia-Pacific Journal of Ophthalmology (2019)
Open Access | Times Cited: 140
Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network
Shu‐I Pao, Hong-Zin Lin, Ke‐Hung Chien, et al.
Journal of Ophthalmology (2020) Vol. 2020, pp. 1-7
Open Access | Times Cited: 98
Detecting glaucoma with only OCT: Implications for the clinic, research, screening, and AI development
Donald C. Hood, Sol La Bruna, Emmanouil Tsamis, et al.
Progress in Retinal and Eye Research (2022) Vol. 90, pp. 101052-101052
Open Access | Times Cited: 61
Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods
Ganeshsree Selvachandran, Shio Gai Quek, Raveendran Paramesran, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 2, pp. 915-964
Open Access | Times Cited: 53
Deep Transfer Learning Approaches to Predict Glaucoma, Cataract, Choroidal Neovascularization, Diabetic Macular Edema, DRUSEN and Healthy Eyes: An Experimental Review
Yogesh Kumar, Surbhi Gupta
Archives of Computational Methods in Engineering (2022) Vol. 30, Iss. 1, pp. 521-541
Closed Access | Times Cited: 49
Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013–2023)
Muhammed Halil Akpınar, Abdulkadir Şengür, Oliver Faust, et al.
Computer Methods and Programs in Biomedicine (2024) Vol. 254, pp. 108253-108253
Closed Access | Times Cited: 10
Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
Kaveri A. Thakoor, Xinhui Li, Emmanouil Tsamis, et al.
(2019)
Closed Access | Times Cited: 60
A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease
Tae Keun Yoo, Joon Yul Choi, Hong Kyu Kim
Computers in Biology and Medicine (2020) Vol. 118, pp. 103628-103628
Closed Access | Times Cited: 54
Identification of glaucoma from fundus images using deep learning techniques
JohnD Akkara, S. Ajitha, M. V. Judy
Indian Journal of Ophthalmology (2021) Vol. 69, Iss. 10, pp. 2702-2709
Open Access | Times Cited: 51
Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions
Nicoleta Anton, Bogdan Doroftei, Silvia Curteanu, et al.
Diagnostics (2022) Vol. 13, Iss. 1, pp. 100-100
Open Access | Times Cited: 29
Coherent convolution neural network based retinal disease detection using optical coherence tomographic images
Pawan Kumar Upadhyay, Somil Rastogi, K. Vimal Kumar
Journal of King Saud University - Computer and Information Sciences (2022) Vol. 34, Iss. 10, pp. 9688-9695
Open Access | Times Cited: 28
Vision transformers for segmentation of disc and cup in retinal fundus images
Yakoub Bazi, Mohamad Mahmoud Al Rahhal, Hebah ElGibreen, et al.
Biomedical Signal Processing and Control (2024) Vol. 91, pp. 105915-105915
Closed Access | Times Cited: 6
Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device
Bala Prabhakar, Rishi Kumar Singh, Khushwant S. Yadav
Computerized Medical Imaging and Graphics (2020) Vol. 87, pp. 101818-101818
Closed Access | Times Cited: 41
Imaging and artificial intelligence for progression of age-related macular degeneration
Kathleen Romond, Minhaj Nur Alam, Sasha Kravets, et al.
Experimental Biology and Medicine (2021) Vol. 246, Iss. 20, pp. 2159-2169
Open Access | Times Cited: 34
The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases
Lorenzo Ferro Desideri, Carola Rutigliani, Paolo Corazza, et al.
Journal of Optometry (2022) Vol. 15, pp. S50-S57
Open Access | Times Cited: 24
Application of artificial intelligence in Alzheimer’s disease: a bibliometric analysis
Sijia Song, Tong Li, Wei Lin, et al.
Frontiers in Neuroscience (2025) Vol. 19
Open Access
Role of artificial intelligence and machine learning in ophthalmology
JohnDavis Akkara, Anju Kuriakose
Kerala Journal of Ophthalmology (2019) Vol. 31, Iss. 2, pp. 150-150
Open Access | Times Cited: 42
Insights into the growing popularity of artificial intelligence in ophthalmology
Ramachandran Rajalakshmi, Sreetama Dutt, Anand Sivaraman, et al.
Indian Journal of Ophthalmology (2020) Vol. 68, Iss. 7, pp. 1339-1339
Open Access | Times Cited: 35
Assessing a Smartphone App (AICaries) That Uses Artificial Intelligence to Detect Dental Caries in Children and Provides Interactive Oral Health Education: Protocol for a Design and Usability Testing Study
Jin Xiao, Jiebo Luo, Oriana Ly-Mapes, et al.
JMIR Research Protocols (2021) Vol. 10, Iss. 10, pp. e32921-e32921
Open Access | Times Cited: 29
Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis
Ronald Cheung, Jacob Chun, Tom Sheidow, et al.
Eye (2021) Vol. 36, Iss. 5, pp. 994-1004
Open Access | Times Cited: 27
Challenges for ocular disease identification in the era of artificial intelligence
Neha Gour, M. Tanveer, Pritee Khanna
Neural Computing and Applications (2022) Vol. 35, Iss. 31, pp. 22887-22909
Closed Access | Times Cited: 19
Page 1 - Next Page

Scroll to top