Journal
THERANOSTICS
Volume 9, Issue 1, Pages 232-245Publisher
IVYSPRING INT PUBL
DOI: 10.7150/thno.28447
Keywords
deep learning; convolutional neural network; artificial intelligence (AI); AI-based website; telemedicine; cloud website
Categories
Funding
- Ministry of Science and Technology (MOST) [105-2633-B-009-003, 105-3011-B010-001, 106-2633-B-009-001, 106-2319-B-001-003, 106-2119-M-010-001, 106-3114-B-010-002, 107-2633-B-009-003]
- Academia Sinica
- MOST [MOST 104-0210-01-09-02, 105-0210-01-13-01, 106-0210-01-15-02, 107-0210-01-19-01]
- Taipei Veterans General Hospital [V104E14-001-MY3-2, V105C-077, V106E-004-2, V106C-001, V107C-139, V107E-002-2]
- Department of Health Cancer Center Research of Excellence [MOHW105-TDU-B211-134003, MOHW105-TDU-B-211-133017, MOHW106-TDU-B-211-113001, MOHW107-TDU-B-211-123001]
- NRPB Human iPSC Alliance-Core Service [MOST 105-2325-B-010-005]
- VGH
- TSGH
- NDMC
- AS Joint Research Program [VTA105-V1-5-1, VTA107-V1-5-1]
- NTUH Joint Research Program [VN106-02, VN107-16]
- National Health Research Institutes, Taiwan [NHRI-EX10610621BI, NHRI-EX107-10621BI]
- Cancer Progression Research Center, National Yang-Ming University
- Aiming for the SPROUT Project-Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) of National Chiao Tung University from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project, Ministry of Edu
Ask authors/readers for more resources
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 https://www.ym.edu.tw/similar to AI-OCT/. Patients can upload their OCT images to the website to verify whether they have AMD and require treatment. Using an AI-based cloud service represents a real solution for medical imaging diagnostics and telemedicine.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available