Modeling and mitigating human annotations to design processing systems with human-in-the-loop machine learning for glaucomatous defects: The future in artificial intelligence Article Swipe
Prasanna Venkatesh Ramesh
,
Shruthy Vaishali Ramesh
,
Aji Kunnath Devadas
,
Prajnya Ray
,
S. Tamilselvan
,
Sathyan Parthasarathi
,
Meena Kumari Ramesh
,
Ramesh Rajasekaran
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.4103/ijo.ijo_1820_21
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.4103/ijo.ijo_1820_21
Dear Editor, The field of glaucoma diagnosis is rapidly moving towards artificial intelligence (AI) for improving and enhancing patient care.[12345] For any AI algorithm to deliver successfully, the basis starts with data annotation.[6] This is where the human interface comes into play. Data annotation is the core ingredient to the success of any AI model. Take, for example, the facial recognition AI; the only way for the image detection AI tool to detect a face in a photo is, if many photos are labeled as "face" exist within the software. If there are no annotated data, then there is no machine learning algorithm in the first place, to detect the image. Annotating data also known as labeling data is the first and most important step in creating a successful AI model.[6] One such image annotating tool, which can be utilized by all, is Microsoft Visual Object Tagging Tool (VoTT) for a comprehensive and customized data labeling [Fig. 1].[7] Customized annotations of the optic nerve head and retinal nerve fiber layer (RNFL) images [Figs. 2-5] can prove useful, in not only identifying glaucomatous discs but also in predicting segmentation of the glaucomatous cup, disc, peripapillary atrophy, and RNFL defect in the background fundus separately, which has never been reported in the literature before, according to our knowledge. This methodology of annotations, though time-consuming, can be utilized by all ophthalmologists to create their own Human-in-the-loop (HITL) AI model.Figure 1: The annotation toolbox with tools (green arrow) utilized for labeling the dataset in the Visual Object Tagging Tool (VoTT) software comprising the rectangle tool (red arrow) and polygonal tool (yellow arrow) for various types of labelingFigure 2: Sample fundus photograph of an eye with glaucomatous cupping utilized for annotatingFigure 3: Annotation of the dataset. (a) Customized labeling of the optic cup (red-dotted area). (b) Customized labeling of the optic disc (pink-dotted area). (c) Customized labeling of peripapillary atrophy (gray-dotted area). (d) Complete annotation of an eye with glaucomatous changes in the optic nerve headFigure 4: Sample fundus photograph of an eye with glaucomatous cupping and retinal nerve fiber layer defect utilized for annotatingFigure 5: Annotation of the dataset. (a) Customized labeling of the optic cup (green-dotted area). (b) Customized labeling of the optic disc (pink-dotted area). (c) Customized labeling of peri-papillary atrophy (gray-dotted area). (d) Customized annotation of the retinal nerve fiber layer (RNFL) slit defect (blue-dotted area). (e) Customized annotation of the RNFL arcuate defect (gray-dotted area). (f) Complete annotation of an eye with glaucomatous changes in the optic nerve head and RNFL regionHITL is the process of leveraging the power of machines and human intelligence to create AI models, where humans annotate data. In this loop, with humans help, the machine becomes smarter to take quick and accurate decisions. Customized human-led data annotation process can pave the way for future in AI, where the pairing of humans and machines takes place to yield better results, and not to establish the supremacy of one over the other. Declaration of patient consent The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patient(s) understand that his/her/their name(s) and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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- Type
- letter
- Language
- en
- Landing Page
- https://doi.org/10.4103/ijo.ijo_1820_21
- OA Status
- diamond
- Cited By
- 11
- References
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- Related Works
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- OpenAlex ID
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https://openalex.org/W3202616615Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4103/ijo.ijo_1820_21Digital Object Identifier
- Title
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Modeling and mitigating human annotations to design processing systems with human-in-the-loop machine learning for glaucomatous defects: The future in artificial intelligenceWork title
- Type
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letterOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-10-01Full publication date if available
- Authors
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Prasanna Venkatesh Ramesh, Shruthy Vaishali Ramesh, Aji Kunnath Devadas, Prajnya Ray, S. Tamilselvan, Sathyan Parthasarathi, Meena Kumari Ramesh, Ramesh RajasekaranList of authors in order
- Landing page
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https://doi.org/10.4103/ijo.ijo_1820_21Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.4103/ijo.ijo_1820_21Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Annotation, Toolbox, Segmentation, Human-in-the-loop, Glaucoma, Software, Fundus (uterus), Machine learning, Computer vision, Medicine, Ophthalmology, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
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2024: 3, 2023: 4, 2022: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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