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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Ophthalmology |
| Online Access: | http://dx.doi.org/10.1155/2020/8841927 |
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| _version_ | 1832567481676333056 |
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| author | Aan Chu David Squirrell Andelka M. Phillips Ehsan Vaghefi |
| author_facet | Aan Chu David Squirrell Andelka M. Phillips Ehsan Vaghefi |
| author_sort | Aan Chu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d70d82b9f91e4e6ea973a5cb5e020f0b |
| institution | Kabale University |
| issn | 2090-004X 2090-0058 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Ophthalmology |
| spelling | doaj-art-d70d82b9f91e4e6ea973a5cb5e020f0b2025-02-03T01:01:23ZengWileyJournal of Ophthalmology2090-004X2090-00582020-01-01202010.1155/2020/88419278841927Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature ReviewAan Chu0David Squirrell1Andelka M. Phillips2Ehsan Vaghefi3School of Optometry and Vision Science, The University of Auckland, Auckland, New ZealandAuckland District Health Board, Auckland, New ZealandTe Piringa Faculty of Law, University of Waikato, Hamilton, New ZealandSchool of Optometry and Vision Science, The University of Auckland, Auckland, New ZealandThis 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.http://dx.doi.org/10.1155/2020/8841927 |
| spellingShingle | Aan Chu David Squirrell Andelka M. Phillips Ehsan Vaghefi Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review Journal of Ophthalmology |
| title | Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review |
| title_full | Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review |
| title_fullStr | Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review |
| title_full_unstemmed | Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review |
| title_short | Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review |
| title_sort | essentials of a robust deep learning system for diabetic retinopathy screening a systematic literature review |
| url | http://dx.doi.org/10.1155/2020/8841927 |
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