Theranostics 2019; 9(1):232-245. doi:10.7150/thno.28447
Artificial intelligence-based decision-making for age-related macular degeneration
1. Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan
2. Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan
3. School of Medicine, National Yang-Ming University, Taipei, Taiwan
4. Clinical Ophthalmology, Shiley Eye Institute, University of California, San Diego, USA
5. Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
6. Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, Taiwan
7. Department of Ophthalmology, Taipei City Hospital, Taipei, Taiwan
8. Institute of Environmental Health, National Yang-Ming University, Taipei, Taiwan
9. Department of Ophthalmology, Shin Kong Wu Ho-Su Memorial Hospital & Fu-Jen Catholic University, Taipei Taiwan
10. Department of Ophthalmology, Tri-Service General Hospital & National Defense Medical Center, Taipei, Taiwan
11. Institute of Pharmacology, National Yang-Ming University, Taipei, Taiwan
12. Department of Physical Medicine and Rehabilitation, Taipei Veterans General Hospital
13. Genomic Research Center, Academia Sinica, Taipei, Taiwan.
14. Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
*Equal contributions (co-first)
Hwang DK, Hsu CC, Chang KJ, Chao D, Sun CH, Jheng YC, Yarmishyn AA, Wu JC, Tsai CY, Wang ML, Peng CH, Chien KH, Kao CL, Lin TC, Woung LC, Chen SJ, Chiou SH. Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics 2019; 9(1):232-245. doi:10.7150/thno.28447. Available from http://www.thno.org/v09p0232.htm
Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces.
Methods: In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related macular degeneration (AMD) patients and used them to train three types of CNNs to perform AMD diagnosis.
Results: Here, we present an AI- and cloud-based telemedicine interaction tool for diagnosis and proposed treatment of AMD. Through deep learning process based on the analysis of preprocessed optical coherence tomography (OCT) imaging data, our AI-based system achieved the same image discrimination rate as that of retinal specialists in our hospital. The AI platform's detection accuracy was generally higher than 90% and was significantly superior (p < 0.001) to that of medical students (69.4% and 68.9%) and equal (p = 0.99) to that of retinal specialists (92.73% and 91.90%). Furthermore, it provided appropriate treatment recommendations comparable to those of retinal specialists.
Conclusions: We therefore developed a website for realistic cloud computing based on this AI platform, available at
Keywords: deep learning, convolutional neural network, artificial intelligence (AI), AI-based website, telemedicine, cloud website