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. 2021 Oct;69(10):2892-2894.
doi: 10.4103/ijo.IJO_1820_21.

Modeling and mitigating human annotations to design processing systems with human-in-the-loop machine learning for glaucomatous defects: The future in artificial intelligence

Prasanna V Ramesh  1 , Shruthy V Ramesh  2 , K Aji  3 , Prajnya Ray  3 , S Tamilselvan  4 , Sathyan Parthasarathi  5 , Meena Kumari Ramesh  6 , Ramesh Rajasekaran  7
Affiliations

Modeling and mitigating human annotations to design processing systems with human-in-the-loop machine learning for glaucomatous defects: The future in artificial intelligence

Prasanna V Ramesh et al. Indian J Ophthalmol. 2021 Oct.
No abstract available

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Conflict of interest statement

None

Figures

Figure 1
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 labeling
Figure 2
Figure 2
Sample fundus photograph of an eye with glaucomatous cupping utilized for annotating
Figure 3
Figure 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 head
Figure 4
Figure 4
Sample fundus photograph of an eye with glaucomatous cupping and retinal nerve fiber layer defect utilized for annotating
Figure 5
Figure 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 region
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References

    1. Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125:1199–206. - PubMed
    1. Al-Aswad LA, Kapoor R, Chu CK, Walters S, Gong D, Garg A, et al. Evaluation of a deep learning system for identifying glaucomatous optic neuropathy based on color fundus photographs. J Glaucoma. 2019;28:1029–34. - PubMed
    1. Cerentini A, Welfer D, Cordeiro d'Ornellas M, Pereira Haygert CJ, Dotto GN. Automatic identification of glaucoma using deep learning methods. Stud Health Technol Inform. 2017;245:318–21. - PubMed
    1. Haleem MS, Han L, Hemert JV, Li B, Fleming A, Pasquale LR, et al. A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis. J Med Syst. 2017;42:20. - PMC - PubMed
    1. Thompson AC, Jammal AA, Medeiros FA. A deep learning algorithm to quantify neuroretinal rim loss from optic disc photographs. Am J Ophthalmol. 2019;201:9–18. - PMC - PubMed