SCREEN-DR: Collaborative platform for diabetic retinopathy.
Background and Objective: Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the disease since the initial stages are asymptomatic...
| Publicado en: | International Journal of Medical Informatics Vol. 120; pp. 137 - 147 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Dec2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132855341&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132855341 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Dec2018 vid: 120 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 132855341 132855341 NLM30409338 132855341 10.1016/j.ijmedinf.2018.10.005 NLM30409338 132855341 ppf: 137 ppct: 10 formats: tig: atl: SCREEN-DR: Collaborative platform for diabetic retinopathy. aug: au: Pedrosa, Micael Silva, Jorge Miguel Silva, João Figueira Matos, Sérgio Costa, Carlos affil: DETI/IEETA, University of Aveiro, Portugal sug: subj: Diabetic Retinopathy Diagnosis Image Interpretation, Computer Assisted Methods Software Algorithms Artificial Intelligence Health Screening Methods Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Arthritis Impact Measurement Scales Ferrans and Powers Quality of Life Index Personal Resource Questionnaire ab: Background and Objective: Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the disease since the initial stages are asymptomatic. Most of these examinations reflect negative cases and many have poor image quality, representing an important inefficiency factor. The SCREEN-DR project aims to tackle this limitation, by researching and developing computer-aided methods for diabetic retinopathy detection. This article presents a multidisciplinary collaborative platform that was created to meet the needs of physicians and researchers, aiming at the creation of machine learning algorithms to facilitate the screening process.Methods: Our proposal is a collaborative platform for textual and visual annotation of image datasets. The architecture and layout were optimized for annotating DR images by gathering feedback from several physicians during the design and conceptualization of the platform. It allows the aggregation and indexing of imagiology studies from diverse sources, and supports the creation and annotation of phenotype-specific datasets to feed artificial intelligence algorithms. The platform makes use of an anonymization pipeline and role-based access control for securing personal data.Results: The SCREEN-DR platform has been deployed in the production environment of the SCREEN-DR project at http://demo.dicoogle.com/screen-dr, and the source code of the project is publicly available. We provide a description of the platform's interface and use cases it supports. At the time of publication, four physicians have created a total of 1826 annotations for 701 distinct images, and the annotated data has been used for training classification models. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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