GLANCE: Visual Analytics for Monitoring Glaucoma Progression

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dc.contributor.authorBrandt, Astrid van denen_US
dc.contributor.authorChristopher, Marken_US
dc.contributor.authorZangwill, Linda M.en_US
dc.contributor.authorRezapour, Jasminen_US
dc.contributor.authorBowd, Christopheren_US
dc.contributor.authorBaxter, Sally L.en_US
dc.contributor.authorWelsbie, Derek S.en_US
dc.contributor.authorCamp, Andrewen_US
dc.contributor.authorMoghimi, Sasanen_US
dc.contributor.authorDo, Jiun L.en_US
dc.contributor.authorWeinreb, Robert N.en_US
dc.contributor.authorSnijders, Chris C. P.en_US
dc.contributor.authorWestenberg, Michel A.en_US
dc.contributor.editorKozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgiaen_US
dc.date.accessioned2020-09-28T06:11:53Z
dc.date.available2020-09-28T06:11:53Z
dc.date.issued2020
dc.description.abstractDeep learning is increasingly used in the field of glaucoma research. Although deep learning models can achieve high accuracy, issues with trust, interpretability, and practical utility form barriers to adoption in clinical practice. In this study, we explore whether and how visualizations of deep learning-based measurements can be used for glaucoma management in the clinic. Through iterative design sessions with ophthalmologists, vision researchers, and manufacturers of optical coherence tomography (OCT) instruments, we distilled four main tasks, and designed a visualization tool that incorporates a visual field (VF) prediction model to provide clinical decision support in managing glaucoma progression. The tasks are: (1) assess reliability of a prediction, (2) understand why the model made a prediction, (3) alert to features that are relevant, and (4) guide future scheduling of VFs. Our approach is novel in that it considers utility of the system in a clinical context where time is limited. With use cases and a pilot user study, we demonstrate that our approach can aid clinicians in clinical management decisions and obtain appropriate trust in the system. Taken together, our work shows how visual explanations of automated methods can augment clinicians' knowledge and calibrate their trust in DL-based measurements during clinical decision making.en_US
dc.description.sectionheadersVA and Uncertainty
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20201175
dc.identifier.isbn978-3-03868-109-0
dc.identifier.issn2070-5786
dc.identifier.pages85-96
dc.identifier.urihttps://doi.org/10.2312/vcbm.20201175
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20201175
dc.publisherThe Eurographics Associationen_US
dc.titleGLANCE: Visual Analytics for Monitoring Glaucoma Progressionen_US
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VCBM 2020: Eurographics Workshop on Visual Computing for Biology and Medicine