Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Hwang DK

PROVIDER: S-EPMC6332801 | biostudies-other | 2019

REPOSITORIES: biostudies-other

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

Hwang De-Kuang DK Hsu Chih-Chien CC Chang Kao-Jung KJ Chao Daniel D Sun Chuan-Hu CH Jheng Ying-Chun YC Yarmishyn Aliaksandr A AA Wu Jau-Ching JC Tsai Ching-Yao CY Wang Mong-Lien ML Peng Chi-Hsien CH Chien Ke-Hung KH Kao Chung-Lan CL Lin Tai-Chi TC Woung Lin-Chung LC Chen Shih-Jen SJ Chiou Shih-Hwa SH

Theranostics 20190101 1

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. <b>Methods:</b> In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related ...[more]

PMID: 30662564

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Project description:PurposeTo investigate changes in retinal thickness, drusen volume, and visual acuity following subthreshold nanosecond laser (SNL) treatment in patients with age-related macular degeneration (ARMD).DesignRetrospective chart review.MethodsPatients with intermediate ARMD treated with a single session of SNL (2RT®, Ellex R&D Pty Ltd, Adelaide, Australia) were included. Swept-source optical coherence tomography (OCT) imaging (Triton; Topcon Medical Systems, Tokyo, Japan) was performed within 6 months before and after SNL treatment. Retinal layers were segmented using the artificial intelligence-enabled Orion® software (Voxeleron LLC, San Francisco, USA). The macular region was analyzed according to the Early Treatment Diabetic Retinopathy Study map. Mean difference and standard deviation in baseline and post-treatment retinal layer thicknesses are reported.Results37 eyes from 25 patients were included in this study (mean age 74.7±9.2 years). An average of 51±6 spots were applied around the macula of each study eye, with a mean spot power of 0.33±0.04mJ. Increases in total retinal thickness were observed within the outer temporal and inferior sectors (P<0.05). Within the annulus, there was an increase in thickness of the sub-retinal pigment epithelial (RPE) space [0.88±2.41μm, P = 0.03], defined between the RPE and Bruch's membrane. An increase in thickness of 1.13±2.55μm (P = 0.01) was also noted in the inferior sector of the photoreceptor complex, defined from the inner and outer segment junction to the RPE. Decreases in thickness were observed within the superior sector of the inner nuclear layer (INL) [-1.08±2.55μm, P = 0.01], and within the annulus of the outer nuclear layer (ONL) [-1.44±3.55μm, P = 0.02].ConclusionsAt 6 months post-SNL treatment, there were sectoral increases in OPL, photoreceptor complex, and sub-RPE space thicknesses and sectoral decreases in INL and ONL thicknesses. This pilot study demonstrates the utility of OCT combined with artificial intelligence-enabled software to track retinal changes that occur following SNL treatment in intermediate ARMD.

Dataset Information

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

Publications

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

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