Embedded deep learning in ophthalmology: making ophthalmic imaging smarter.
Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging d...
| Publicado en: | Therapeutic Advances in Ophthalmology Vol. 11 |
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| Autores principales: | , , , |
| Formato: | diagnostic images pictorial Journal Article |
| Publicado: |
Sage Publications Inc.
Jan-Dec2019
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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=137794793&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137794793 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 25158414 LAW0 jtl: Therapeutic Advances in Ophthalmology issn: 25158414 maglogo: Y pubinfo: dt: Jan-Dec2019 vid: 11 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 137794793 137794793 137794793 10.1177/2515841419827172 137794793 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Embedded deep learning in ophthalmology: making ophthalmic imaging smarter. aug: au: Teikari, Petteri Najjar, Raymond P. Schmetterer, Leopold Milea, Dan affil: Advanced Ocular Imaging, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore sug: subj: Deep Learning Methods Ophthalmic Equipment and Supplies Tomography, Optical Coherence Artificial Intelligence Electronic Health Records Clinical Laboratories Quality Improvement Ophthalmology ab: Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for 'active acquisition'–embedded deep learning, leading to as high-quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning–based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog, and cloud layers, the former being performed at a device level. Improved egde-layer performance via 'active acquisition' serves as an automatic data curation operator translating to better quality data in electronic health records, as well as on the cloud layer, for improved deep learning–based clinical data mining. pubtype: Academic Journal doctype: diagnostic images pictorial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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