Artificial Intelligence and Glaucoma: Going Back to Basics

References

• Jampel HD, Friedman D, Quigley H, et al. Agreement among glaucoma specialists in assessing progressive disc changes from photographs in open-angle glaucoma patients. Am J Ophthalmol. 2009;147:39–44.e1. doi:10.1016/j.ajo.2008.07.023
   PubMed Web of Science ®Google Scholar
• Glaucoma Today. AI for glaucoma care. Available from: https://glaucomatoday.com/articles/2022-july-aug/ai-for-glaucoma-care. Accessed May 25, 2023.
   Google Scholar
• Cybersight. AI-driven automated interpretation for fundus images. Available from: https://cybersight.org/ai-consult/. Accessed May 25, 2023.
   Google Scholar
• Thompson AC, Jammal AA, Medeiros FA. A review of deep learning for screening, diagnosis, and detection of glaucoma progression. Transl Vis Sci Technol. 2020;9:42. doi:10.1167/tvst.9.2.42
   PubMed Web of Science ®Google Scholar
• Yokoyama Y, Tanito M, Nitta K, et al. Stereoscopic analysis of optic nerve head parameters in primary open angle glaucoma: the glaucoma stereo analysis study. PLoS One. 2014;9:e99138. doi:10.1371/journal.pone.0099138
   PubMed Web of Science ®Google Scholar
• Phene S, Dunn RC, Hammel N, et al. Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs. Ophthalmology. 2019;126:1627–1639. doi:10.1016/j.ophtha.2019.07.024
   PubMed Web of Science ®Google Scholar
• Kuo W, Hӓne C, Mukherjee P, Malik J, Yuh EL. Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proc Natl Acad Sci. 2019;116:22737–22745. doi:10.1073/pnas.1908021116
   PubMed Web of Science ®Google Scholar
• AlRyalat SA, Muhtaseb R, Alshammari T. Simulating a colour-blind ophthalmologist for diagnosing and staging diabetic retinopathy. Eye. 2020;1–4. doi:10.1038/s41433-020-01232-z
   Web of Science ®Google Scholar
• American Academy of Ophthalmology (AAO). American Academy of Ophthalmology [Homepage]. Available from: https://www.aao.org/. Accessed May 25, 2023.
   Google Scholar
• The National Institute for Health and Care Excellence. NICE [Homepage]. Available from: https://www.nice.org.uk/. Accessed May 25, 2023.
   Google Scholar
• World Glaucoma Association (WGA). World Glaucoma Association [Homepage]. Available from: https://wga.one/. Accessed May 25, 2023.
   Google Scholar
• European Glaucoma Society (EGS). Available from: https://www.eugs.org/eng/default.asp. Accessed May 25, 2023.
   Google Scholar
• Glaucoma Research Foundation is dedicated to finding a cure; 2021. Available from: https://glaucoma.org/. Accessed May 25, 2023.
   Google Scholar
• Musch DC, Gillespie BW, Lichter PR, et al. Visual field progression in the Collaborative Initial Glaucoma Treatment Study the impact of treatment and other baseline factors. Ophthalmology. 2009;116:200–207. doi:10.1016/j.ophtha.2008.08.051
   PubMed Web of Science ®Google Scholar
• Weinreb RN, Aung T, Medeiros FA. The pathophysiology and treatment of glaucoma: a review. JAMA. 2014;311:1901–1911. doi:10.1001/jama.2014.3192
   PubMed Web of Science ®Google Scholar
• Ha A, Park KH. Optical coherence tomography for the diagnosis and monitoring of glaucoma. Asia-Pac J Ophthalmol. 2019;8:135.
   PubMed Web of Science ®Google Scholar
• Lloyd MJ, Mansberger SL, Fortune BA, et al. Features of optic disc progression in patients with ocular hypertension and early glaucoma. J Glaucoma. 2013;22:343–348. doi:10.1097/IJG.0b013e31824c9251
   PubMed Web of Science ®Google Scholar
• Jammal AA, Berchuck SI, Thompson AC, Costa VP, Medeiros FA. The effect of age on increasing susceptibility to retinal nerve fiber layer loss in glaucoma. Invest Ophthalmol Vis Sci. 2020;61:8. doi:10.1167/iovs.61.13.8
   PubMed Web of Science ®Google Scholar
• Mariottoni EB, Jammal AA, Berchuck SI, et al. An objective structural and functional reference standard in glaucoma. Sci Rep. 2021;11:1752. doi:10.1038/s41598-021-80993-3
   PubMed Web of Science ®Google Scholar
• Iyer JV, Boland MV, Jefferys J, Quigley H. Defining glaucomatous optic neuropathy using objective criteria from structural and functional testing. Br J Ophthalmol. 2021;105:789–793. doi:10.1136/bjophthalmol-2020-316237
   PubMed Web of Science ®Google Scholar
• Khan SM, Liu X, Nath S, et al. A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability. Lancet Digit Health. 2021;3:e51–e66. doi:10.1016/S2589-7500(20)30240-5
   PubMedGoogle Scholar
• AlRyalat SA. Machine learning on glaucoma: the missing point. Eye. 2021;1–2. doi:10.1038/s41433-021-01561-7
   Web of Science ®Google Scholar
• AlRyalat SA, Ertel MK, Seibold LK, Kahook MY. Designs and methodologies used in landmark clinical trials of glaucoma: implications for future big data mining and actionable disease treatment. Front Med. 2022;9:818568. doi:10.3389/fmed.2022.818568
   Web of Science ®Google Scholar
• AlRyalat SA, Al-Ryalat N, Ryalat S. Machine learning in glaucoma: a bibliometric analysis comparing computer science and medical fields’ research. Expert Rev Ophthalmol. 2021;16:511–515. doi:10.1080/17469899.2021.1964956
   Web of Science ®Google Scholar
• Heijl A, Leske MC, Bengtsson B, Hyman L, Bengtsson B, Hussein M. Reduction of intraocular pressure and glaucoma progression: results from the early manifest glaucoma trial. Arch Ophthalmol. 2002;120:1268–1279. doi:10.1001/archopht.120.10.1268
   PubMed Web of Science ®Google Scholar
• Gordon MO, Beiser JA, Brandt JD, et al. The ocular hypertension treatment study: baseline factors that predict the onset of primary open-angle glaucoma. Arch Ophthalmol. 2002;120:714–720. doi:10.1001/archopht.120.6.714
   PubMed Web of Science ®Google Scholar
• Sivaswamy J, Krishnadas SR, Datt Joshi G, Jain M, Syed Tabish AU, Drishti GS. Retinal image dataset for optic nerve head(ONH) segmentation. Int Symp Biomed Imaging. 2014;53–56. doi:10.1109/ISBI.2014.6867807
   Google Scholar
• Drishti GS. Retina dataset for ONH segmentation. Available from: https://www.kaggle.com/datasets/lokeshsaipureddi/drishtigs-retina-dataset-for-onh-segmentation. Accessed May 25, 2023.
   Google Scholar
• About. Computer Vision Annotation Tool (CVAT). Available from: https://openvinotoolkit.github.io/about/. Accessed May 25, 2023.
   Google Scholar
• Warfield SK, Zou KH, Wells WM. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004;23:903–921. doi:10.1109/TMI.2004.828354
   PubMed Web of Science ®Google Scholar
• Mursch-Edlmayr AS, Ng WS, Diniz-Filho A, et al. Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: translation to clinical practice. Transl Vis Sci Technol. 2020;9:55. doi:10.1167/tvst.9.2.55
   PubMed Web of Science ®Google Scholar
• Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nat Med. 2022;28:1773–1784. doi:10.1038/s41591-022-01981-2
   PubMed Web of Science ®Google Scholar
• Xiong J, Li F, Song D, et al. Multimodal machine learning using visual fields and peripapillary circular OCT scans in detection of glaucomatous optic neuropathy. Ophthalmology. 2022;129:171–180. doi:10.1016/j.ophtha.2021.07.032
   PubMed Web of Science ®Google Scholar
• Alryalat SA, Al-Antary M, Arafa Y, et al. Deep learning prediction of response to anti-VEGF among diabetic macular edema patients: Treatment Response Analyzer System (TRAS). Diagnostics. 2022;12:312. doi:10.3390/diagnostics12020312
   Web of Science ®Google Scholar
• Coyner AS, Oh MA, Shah PK, et al. External validation of a retinopathy of prematurity screening model using artificial intelligence in 3 low- and middle-income populations. JAMA Ophthalmol. 2022;140:791–798. doi:10.1001/jamaophthalmol.2022.2135
   PubMed Web of Science ®Google Scholar
• Chaurasia AK, Greatbatch CJ, Hewitt AW. Diagnostic accuracy of artificial intelligence in glaucoma screening and clinical practice. J Glaucoma. 2022;31:285. doi:10.1097/IJG.0000000000002015
   PubMed Web of Science ®Google Scholar
• Xiao X, Xue L, Ye L, Li H, He Y. Health care cost and benefits of artificial intelligence-assisted population-based glaucoma screening for the elderly in remote areas of China: a cost-offset analysis. BMC Public Health. 2021;21:1065. doi:10.1186/s12889-021-11097-w
   PubMed Web of Science ®Google Scholar
• Kuhlman C, Jackson L, Chunara R. No computation without representation: avoiding data and algorithm biases through diversity. Int Conf Knowl Discov Data Min. 2020;3593. doi:10.1145/3394486.3411074
   Google Scholar
• Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172:1122–1131.e9. doi:10.1016/j.cell.2018.02.010
   PubMed Web of Science ®Google Scholar
• Overfitting and generalization of AI in medical imaging. Available from: https://www.acrdsi.org/DSIBlog/2022/04/11/Overfitting-and-Generalization-of-AI-in-Medical-Imaging. Accessed May 25, 2023.
   Google Scholar
• Quigley HA, Boland MV, Iyer JV. Evaluating an objective definition of glaucomatous optic neuropathy: an international collaborative effort. Invest Ophthalmol Vis Sci. 2020;61:1440.
   Web of Science ®Google Scholar
• Mohamed-Noriega J, Sekhar GC. Defining and diagnosing glaucoma: a focus on blindness prevention. Community Eye Health. 2021;34:32–35.
   PubMedGoogle Scholar