Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review.
This systematic review was performed to identify the specifics of an optimal diabetic retinopathy deep learning algorithm, by identifying the best exemplar research studies of the field, whilst highlighting potential barriers to clinical implementation of such an algorithm. Searching five electronic...
| Publicado en: | Journal of Ophthalmology pp. 1 - 12 |
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| Autores principales: | , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/16/2020
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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=147022658&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147022658 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2090004X 9038 jtl: Journal of Ophthalmology issn: 2090004X maglogo: N pubinfo: dt: 11/16/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 147022658 147022658 147022658 10.1155/2020/8841927 147022658 ppf: 1 ppct: 11 formats: tig: atl: Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review. aug: au: Chu, Aan Squirrell, David Phillips, Andelka M. Vaghefi, Ehsan affil: School of Optometry and Vision Science, The University of Auckland, Auckland, New Zealand sug: subj: Deep Learning Diabetic Retinopathy Prevention and Control Health Screening Human Systematic Review Algorithms Embase Medline PubMed Cochrane Library ab: This systematic review was performed to identify the specifics of an optimal diabetic retinopathy deep learning algorithm, by identifying the best exemplar research studies of the field, whilst highlighting potential barriers to clinical implementation of such an algorithm. Searching five electronic databases (Embase, MEDLINE, Scopus, PubMed, and the Cochrane Library) returned 747 unique records on 20 December 2019. Predetermined inclusion and exclusion criteria were applied to the search results, resulting in 15 highest-quality publications. A manual search through the reference lists of relevant review articles found from the database search was conducted, yielding no additional records. A validation dataset of the trained deep learning algorithms was used for creating a set of optimal properties for an ideal diabetic retinopathy classification algorithm. Potential limitations to the clinical implementation of such systems were identified as lack of generalizability, limited screening scope, and data sovereignty issues. It is concluded that deep learning algorithms in the context of diabetic retinopathy screening have reported impressive results. Despite this, the potential sources of limitations in such systems must be evaluated carefully. An ideal deep learning algorithm should be clinic-, clinician-, and camera-agnostic; complying with the local regulation for data sovereignty, storage, privacy, and reporting; whilst requiring minimum human input. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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