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. 2022 Nov;9(11):810.
doi: 10.3390/photonics9110810. Epub 2022 Oct 28.

Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions

Roberto Nunez  1 , Alon Harris  2 , Omar Ibrahim  3 , James Keller  1 , Christopher K Wikle  4 , Erin Robinson  5 , Ryan Zukerman  6 , Brent Siesky  2 , Alice Verticchio  2 , Lucas Rowe  7 , Giovanna Guidoboni  1   8
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Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions

Roberto Nunez et al. Photonics. 2022 Nov.

Abstract

Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glaucoma diagnosis and the monitoring of glaucoma progression are reviewed, including the classification of visual field images and the detection of glaucomatous change in retinal nerve fiber layer thickness. Current challenges in the direct application of artificial intelligence to further our understating of this disease are also outlined. The article also discusses how the combined use of mathematical modeling and artificial intelligence may help to address these challenges, along with stronger communication between data scientists and clinicians.

Keywords: artificial intelligence; glaucoma; glaucoma diagnosis; glaucoma progression; mathematical modeling; statistical modeling.

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Conflict of interest statement

Conflicts of Interest: Alon Harris would like to disclose that he received remuneration from AdOM, Qlaris, Luseed, and Cipla for serving as a consultant, and he serves on the board of AdOM, Qlaris, and Phileas Pharma. Alon Harris holds an ownership interest in AdOM, Luseed, Oxymap, Qlaris, Phileas Pharma, SlitLed, and QuLent. Giovanna Guidoboni would like to disclose that she received remuneration from Foresite Healthcare and Qlaris for serving as a consultant.

Figures

Figure 1.
Figure 1.
Comparison of Heidelberg retinal tomography (HRT)- vs. optical coherence tomography (OCT)-derived parameters: (left) cup area, (center) cup to disk area ratio, (right) mean retinal nerve fiber layer (RNFL) thickness.
Figure 2.
Figure 2.
Schematic representation of the data set enhancement process.
Figure 3.
Figure 3.
In both graphs, each dot represents a patient in the IGPS, plotted with respect to IOP and mean arterial pressure (MAP). On the right, patients are colored according to the label assigned to them by the fuzzy C-means algorithm applied to the enhanced dataset. The physiology-informed machine learning approach was able to quantify relative contributions of IOP and blood pressure to OAG risk for patients in these clusters.
See this image and copyright information in PMC

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