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Artificial intelligence-based decision-making for age-related macular degeneration

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 pro...

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Detalles Bibliográficos
Autores principales: Hwang, De-Kuang, Hsu, Chih-Chien, Chang, Kao-Jung, Chao, Daniel, Sun, Chuan-Hu, Jheng, Ying-Chun, Yarmishyn, Aliaksandr A., Wu, Jau-Ching, Tsai, Ching-Yao, Wang, Mong-Lien, Peng, Chi-Hsien, Chien, Ke-Hung, Kao, Chung-Lan, Lin, Tai-Chi, Woung, Lin-Chung, Chen, Shih-Jen, Chiou, Shih-Hwa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6332801/
https://www.ncbi.nlm.nih.gov/pubmed/30662564
http://dx.doi.org/10.7150/thno.28447
Descripción
Sumario: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/~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.