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

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dc.contributor.authorHwang, De-Kuang
dc.contributor.authorHsu, Chih-Chien
dc.contributor.authorChang, Kao-Jung
dc.contributor.authorChao, Daniel
dc.contributor.authorSun, Chuan-Hu
dc.contributor.authorJheng, Ying-Chun
dc.contributor.authorA Yarmishyn, Aliaksandr
dc.contributor.authorWu, Jau-Ching
dc.contributor.authorTsai, Ching-Yao
dc.contributor.authorWang, Mong-Lien
dc.contributor.authorPeng, Chi-Hsien
dc.contributor.authorChien, Ke-Hung
dc.contributor.authorKao, Chung-Lan
dc.contributor.authorLin, Tai-Chi
dc.contributor.authorWoung, Lin-Chung
dc.contributor.authorChen, Shih-Jen
dc.contributor.authorChiou, Shih-Hwa
dc.date.accessioned2020-06-08T14:40:00Z
dc.date.available2020-06-08T14:40:00Z
dc.date.issued0000-00-00
dc.description.abstractArtificial 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 Under 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
dc.identifier.urihttp://dx.doi.org/10.7150/thno.28447
dc.identifier.urihttps://lib.digitalsquare.io/handle/123456789/64951
dc.relation.uriTheranostics
dc.titleArtificial intelligence-based decision-making for age-related macular degeneration.en
dcterms.abstractArtificial 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 Under 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
dcterms.contributorHwang, De-Kuang
dcterms.contributorHsu, Chih-Chien
dcterms.contributorChang, Kao-Jung
dcterms.contributorChao, Daniel
dcterms.contributorSun, Chuan-Hu
dcterms.contributorJheng, Ying-Chun
dcterms.contributorA Yarmishyn, Aliaksandr
dcterms.contributorWu, Jau-Ching
dcterms.contributorTsai, Ching-Yao
dcterms.contributorWang, Mong-Lien
dcterms.contributorPeng, Chi-Hsien
dcterms.contributorChien, Ke-Hung
dcterms.contributorKao, Chung-Lan
dcterms.contributorLin, Tai-Chi
dcterms.contributorWoung, Lin-Chung
dcterms.contributorChen, Shih-Jen
dcterms.contributorChiou, Shih-Hwa
dcterms.identifierhttp://dx.doi.org/10.7150/thno.28447
dcterms.relationTheranostics
dcterms.titleArtificial intelligence-based decision-making for age-related macular degeneration.en
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