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dc.contributor.authorRoesch, Karin
dc.contributor.authorSwedish, Tristan
dc.contributor.authorRaskar, Ramesh
dc.date.accessioned2022-01-13T15:27:14Z
dc.date.available2021-10-27T20:29:00Z
dc.date.available2022-01-13T15:27:14Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/1721.1/135722.2
dc.description.abstract© 2017 Roesch et al. Most current diagnostic devices are expensive, require trained specialists to operate and gather static images with sparse data points. This leads to preventable diseases going undetected until late stage, resulting in greatly narrowed treatment options. This is especially true for retinal imaging. Future solutions are low cost, portable, self-administered by the patient, and capable of providing multiple data points, population analysis, and trending. This enables preventative interventions through mass accessibility, constant monitoring, and predictive modeling.en_US
dc.language.isoen
dc.publisherInforma UK Limiteden_US
dc.relation.isversionof10.2147/OPTH.S116265en_US
dc.rightsCreative Commons Attribution Noncommercial 3.0 unported licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/en_US
dc.sourceDove Pressen_US
dc.titleAutomated retinal imaging and trend analysis – a tool for health monitoringen_US
dc.typeArticleen_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.relation.journalClinical Ophthalmologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-08-02T12:46:07Z
dspace.orderedauthorsRoesch, K; Swedish, T; Raskar, Ren_US
dspace.date.submission2019-08-02T12:46:08Z
mit.journal.volumeVolume 11en_US
mit.metadata.statusPublication Information Neededen_US


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