Notice

This is not the latest version of this item. The latest version can be found at:https://dspace.mit.edu/handle/1721.1/135722.2

Show simple item record

dc.contributor.authorRoesch, Karin
dc.contributor.authorSwedish, Tristan
dc.contributor.authorRaskar, Ramesh
dc.date.accessioned2021-10-27T20:29:00Z
dc.date.available2021-10-27T20:29:00Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/1721.1/135722
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.
dc.language.isoen
dc.publisherInforma UK Limited
dc.relation.isversionof10.2147/OPTH.S116265
dc.rightsCreative Commons Attribution Noncommercial 3.0 unported license
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/
dc.sourceDove Press
dc.titleAutomated retinal imaging and trend analysis – a tool for health monitoring
dc.typeArticle
dc.relation.journalClinical Ophthalmology
dc.eprint.versionFinal published version
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2019-08-02T12:46:07Z
dspace.orderedauthorsRoesch, K; Swedish, T; Raskar, R
dspace.date.submission2019-08-02T12:46:08Z
mit.journal.volumeVolume 11
mit.metadata.statusAuthority Work and Publication Information Needed


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

VersionItemDateSummary

*Selected version