Artificial Intelligence in Glaucoma: A New Landscape of Diagnosis and Management
Abstract
:1. Introduction
1.1. Current Challenges in Glaucoma Management
1.2. Role of AI in Glaucoma
1.3. Objectives
2. AI in Glaucoma Detection and Diagnosis
2.1. AI in Functional Imaging
2.2. AI in Structural Imaging
2.3. Integrating Multiple Modalities Using AI for Detection and Diagnosis
2.4. Telemedicine and Remote Monitoring
3. AI in Monitoring Glaucoma Progression and Prediction
3.1. AI in Functional Imaging for Progression Monitoring and Prediction
3.2. AI in Structural Imaging for Progression Monitoring and Prediction
3.3. Integrating Multiple Modalities Using AI into Progression Monitoring and Prediction
4. AI in Glaucoma Treatment
4.1. AI in Surgical Interventions
4.2. Treatment Response Prediction
5. Ethical, Legal, and Social Implications
5.1. Ethical Considerations
5.2. Legal Implications
5.3. Social Implications
6. Limitations of AI in Glaucoma
7. Future Directions
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Weinreb, R.N.; Aung, T.; Medeiros, F.A. The Pathophysiology and Treatment of Glaucoma: A review. JAMA 2014, 311, 1901–1911. [Google Scholar] [CrossRef] [PubMed]
- Nadler, Z.; Wollstein, G.; Ishikawa, H.; Schuman, J.S. Clinical Application of Ocular Imaging. Optom. Vis. Sci. 2012, 89, E543–E553. [Google Scholar] [CrossRef] [PubMed]
- Quigley, H.A.; Broman, A.T. The number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 2006, 90, 262–267. [Google Scholar] [CrossRef]
- Tham, Y.-C.; Li, X.; Wong, T.Y.; Quigley, H.A.; Aung, T.; Cheng, C.-Y. Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A systematic review and meta-analysis. Ophthalmology 2014, 121, 2081–2090. [Google Scholar] [CrossRef] [PubMed]
- Stein, J.D.; Talwar, N.; LaVerne, A.M.; Nan, B.; Lichter, P.R. Trends in Use of Ancillary Glaucoma Tests for Patients with Open-Angle Glaucoma from 2001 to 2009. Ophthalmology 2012, 119, 748–758. [Google Scholar] [CrossRef] [PubMed]
- Medeiros, F.; Alencar, L.M. The role of standard automated perimetry and newer functional methods for glaucoma diagnosis and follow-up. Indian J. Ophthalmol. 2011, 59, S53–S58. [Google Scholar] [CrossRef] [PubMed]
- Bengtsson, B.; Heijl, A. A Visual Field Index for Calculation of Glaucoma Rate of Progression. Arch. Ophthalmol. 2008, 145, 343–353. [Google Scholar] [CrossRef]
- Li, F.; Wang, Z.; Qu, G.; Song, D.; Yuan, Y.; Xu, Y.; Gao, K.; Luo, G.; ** of Structure to Function in Glaucoma. Transl. Vis. Sci. Technol. 2020, 9, 19. [Google Scholar] [CrossRef]
- Wang, R.; Bradley, C.; Herbert, P.; Hou, K.; Ramulu, P.; Breininger, K.; Unberath, M.; Yohannan, J. Deep learning-based identification of eyes at risk for glaucoma surgery. Sci. Rep. 2024, 14, 599. [Google Scholar] [CrossRef] [PubMed]
- Lim, W.S.; Ho, H.-C.; Chen, Y.-W.; Lee, C.-K.; Chen, P.-J.; Lai, F.; Jang, J.-S.R.; Ko, M.-L. Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: Focus group study on high prevalence of myopia. BMC Med. Imaging 2022, 22, 206. [Google Scholar] [CrossRef]
- Benzebouchi, N.E.; Azizi, N.; Ashour, A.S.; Dey, N.; Sherratt, R.S. Multi-modal classifier fusion with feature cooperation for glaucoma diagnosis. J. Exp. Theor. Artif. Intell. 2019, 31, 841–874. [Google Scholar] [CrossRef]
- Bhuiyan, A.; Govindaiah, A.; Smith, R.T. An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging. J. Ophthalmol. 2021, 2021, 6694784. [Google Scholar] [CrossRef] [PubMed]
- Zhu, A.; Tailor, P.; Verma, R.; Zhang, I.; Schott, B.; Ye, C.; Szirth, B.; Habiel, M.; Khouri, A.S. Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19. J. Telemed. Telecare 2023, 1357633X231158832. [Google Scholar] [CrossRef] [PubMed]
- Gonzalez-Hernandez, M.; Gonzalez-Hernandez, D.; Betancor-Caro, N.; Guedes-Guedes, I.; Guldager, M.K.; de la Rosa, M.G. Glaucoma Incidence and Progression in Diabetics: The Canary Islands Study Using the Laguna ONhE Application. J. Clin. Med. 2022, 11, 7294. [Google Scholar] [CrossRef] [PubMed]
- Qiao, Y.; Luo, J.; Cui, T.; Liu, H.; Tang, H.; Zeng, Y.; Liu, C.; Li, Y.; Jian, J.; Wu, J.; et al. Soft Electronics for Health Monitoring Assisted by Machine Learning. Nano-Micro Lett. 2023, 15, 66. [Google Scholar] [CrossRef]
- Jones, P.R.; Campbell, P.; Callaghan, T.; Jones, L.; Asfaw, D.S.; Edgar, D.F.; Crabb, D.P. Glaucoma Home Monitoring Using a Tablet-Based Visual Field Test (Eyecatcher): An Assessment of Accuracy and Adherence Over 6 Months. Arch. Ophthalmol. 2020, 223, 42–52. [Google Scholar] [CrossRef] [PubMed]
- Bekollari, M.; Dettoraki, M.; Stavrou, V.; Glotsos, D.; Liaparinos, P. Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device. Diagnostics 2024, 14, 349. [Google Scholar] [CrossRef]
- Payne, N.; Gangwani, R.; Barton, K.; Sample, A.P.; Cain, S.M.; Burke, D.T.; Newman-Casey, P.A.; Shorter, K.A. Medication Adherence and Liquid Level Tracking System for Healthcare Provider Feedback. Sensors 2020, 20, 2435. [Google Scholar] [CrossRef]
- Yousefi, S.; Kiwaki, T.; Zheng, Y.; Sugiura, H.; Asaoka, R.; Murata, H.; Lemij, H.; Yamanishi, K. Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning. Arch. Ophthalmol. 2018, 193, 71–79. [Google Scholar] [CrossRef] [PubMed]
- Elze, T.; Pasquale, L.R.; Shen, L.Q.; Chen, T.C.; Wiggs, J.L.; Bex, P.J. Patterns of functional vision loss in glaucoma determined with archetypal analysis. J. R. Soc. Interface 2015, 12, 20141118. [Google Scholar] [CrossRef] [PubMed]
- Kass, M.A.; Heuer, D.K.; Higginbotham, E.J.; Johnson, C.; Keltner, J.L.; Miller, J.P.; Parrish, R.K.; Wilson, M.R.; Gordon, M.O. The Ocular Hypertension Treatment Study: A randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma. Arch. Ophthalmol. 2002, 120, 701–713, discussion 829–830. [Google Scholar] [CrossRef] [PubMed]
- Singh, R.K.; Smith, S.; Fingert, J.; Gordon, M.; Kass, M.; Scheetz, T.; Segrè, A.V.; Wiggs, J.; Elze, T.; Zebardast, N. Machine Learning–Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study. Investig. Opthalmol. Vis. Sci. 2024, 65, 35. [Google Scholar] [CrossRef] [PubMed]
- Pham, Q.T.M.; Han, J.C.; Park, D.Y.; Shin, J. Multimodal Deep Learning Model of Predicting Future Visual Field for Glaucoma Patients. IEEE Access 2023, 11, 19049–19058. [Google Scholar] [CrossRef]
- Mariottoni, E.B.; Datta, S.; Shigueoka, L.S.; Jammal, A.A.; Tavares, I.M.; Henao, R.; Carin, L.; Medeiros, F.A. Deep Learning–Assisted Detection of Glaucoma Progression in Spectral-Domain OCT. Ophthalmol. Glaucoma 2023, 6, 228–238. [Google Scholar] [CrossRef] [PubMed]
- Li, A.; Tandon, A.K.; Sun, G.; Dinkin, M.J.; Oliveira, C. Early Detection of Optic Nerve Changes on Optical Coherence Tomography Using Deep Learning for Risk-Stratification of Papilledema and Glaucoma. J. Neuro-Ophthalmol. 2023, 44, 47–52. [Google Scholar] [CrossRef] [PubMed]
- Normando, E.M.; Yap, T.E.; Maddison, J.; Miodragovic, S.; Bonetti, P.; Almonte, M.; Mohammad, N.G.; Ameen, S.; Crawley, L.; Ahmed, F.; et al. A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells). Expert Rev. Mol. Diagn. 2020, 20, 737–748. [Google Scholar] [CrossRef] [PubMed]
- Li, F.; Su, Y.; Lin, F.; Li, Z.; Song, Y.; Nie, S.; Xu, J.; Chen, L.; Chen, S.; Li, H.; et al. A deep-learning system predicts glaucoma incidence and progression using retinal photographs. J. Clin. Investig. 2022, 132, e157968. [Google Scholar] [CrossRef]
- Lin, T.P.; Hui, H.Y.; Ling, A.; Chan, P.P.; Shen, R.; Wong, M.O.; Chan, N.C.; Leung, D.Y.; Xu, D.; Lee, M.L.; et al. Risk of Normal Tension Glaucoma Progression from Automated Baseline Retinal-Vessel Caliber Analysis: A Prospective Cohort Study. Arch. Ophthalmol. 2022, 247, 111–120. [Google Scholar] [CrossRef]
- Hussain, S.; Chua, J.; Wong, D.; Lo, J.; Kadziauskiene, A.; Asoklis, R.; Barbastathis, G.; Schmetterer, L.; Yong, L. Predicting glaucoma progression using deep learning framework guided by generative algorithm. Sci. Rep. 2023, 13, 19960. [Google Scholar] [CrossRef] [PubMed]
- Herbert, P.; Hou, K.; Bradley, C.; Hager, G.; Boland, M.V.; Ramulu, P.; Unberath, M.; Yohannan, J. Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data. Ophthalmol. Glaucoma 2023, 6, 466–473. [Google Scholar] [CrossRef]
- Qidwai, U.; Qidwai, U.; Sivapalan, T.; Ratnarajan, G. iMIGS: An innovative AI based prediction system for selecting the best patient-specific glaucoma treatment. MethodsX 2023, 10, 102209. [Google Scholar] [CrossRef]
- Conlon, R.; Saheb, H.; Ahmed, I.I.K. Glaucoma treatment trends: A review. Can. J. Ophthalmol. 2016, 52, 114–124. [Google Scholar] [CrossRef]
- Ciociola, E.C.; Fernandez, E.; Kaufmann, M.; Klifto, M.R. Future directions of glaucoma treatment: Emerging gene, neuroprotection, nanomedicine, stem cell, and vascular therapies. Curr. Opin. Ophthalmol. 2023, 35, 89–96. [Google Scholar] [CrossRef] [PubMed]
- Lin, W.-C.; Chen, A.; Song, X.; Weiskopf, N.G.; Chiang, M.F.; Hribar, M.R. Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data. J. Am. Med. Inform. Assoc. 2023, 31, 456–464. [Google Scholar] [CrossRef]
- Wang, S.Y.; Tseng, B.; Hernandez-Boussard, T. Deep Learning Approaches for Predicting Glaucoma Progression Using Electronic Health Records and Natural Language Processing. Ophthalmol. Sci. 2022, 2, 100127. [Google Scholar] [CrossRef] [PubMed]
- Baxter, S.L.; Marks, C.; Kuo, T.-T.; Ohno-Machado, L.; Weinreb, R.N. Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records. Arch. Ophthalmol. 2019, 208, 30–40. [Google Scholar] [CrossRef]
- Lin, K.Y.; Urban, G.; Yang, M.C.; Lee, L.-C.; Lu, D.-W.; Alward, W.L.M.; Baldi, P. Accurate Identification of the Trabecular Meshwork under Gonioscopic View in Real Time Using Deep Learning. Ophthalmology 2022, 129, 402–412. [Google Scholar] [CrossRef]
- Nespolo, R.G.; Yi, D.; Cole, E.; Valikodath, N.; Luciano, C.; Leiderman, Y.I. Evaluation of Artificial Intelligence–Based Intraoperative Guidance Tools for Phacoemulsification Cataract Surgery. JAMA Ophthalmol. 2022, 140, 170–177. [Google Scholar] [CrossRef]
- Banna, H.U.; Zanabli, A.; McMillan, B.; Lehmann, M.; Gupta, S.; Gerbo, M.; Palko, J. Evaluation of machine learning algorithms for trabeculectomy outcome prediction in patients with glaucoma. Sci. Rep. 2022, 12, 2473. [Google Scholar] [CrossRef] [PubMed]
- Lin, M.; ** Review of Reviews. J. Pers. Med. 2022, 12, 1914. [Google Scholar] [CrossRef] [PubMed]
- Ruamviboonsuk, P.; Ruamviboonsuk, V.; Tiwari, R. Recent evidence of economic evaluation of artificial intelligence in ophthalmology. Curr. Opin. Ophthalmol. 2023, 34, 449–458. [Google Scholar] [CrossRef] [PubMed]
- Ting, D.S.W.; Pasquale, L.R.; Peng, L.; Campbell, J.P.; Lee, A.Y.; Raman, R.; Tan, G.S.W.; Schmetterer, L.; Keane, P.A.; Wong, T.Y. Artificial intelligence and deep learning in ophthalmology. Br. J. Ophthalmol. 2019, 103, 167–175. [Google Scholar] [CrossRef] [PubMed]
- Mursch-Edlmayr, A.S.; Ng, W.S.; Diniz-Filho, A.; Sousa, D.C.; Arnold, L.; Schlenker, M.B.; Duenas-Angeles, K.; Keane, P.A.; Crowston, J.G.; Jayaram, H. Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice. Transl. Vis. Sci. Technol. 2020, 9, 55. [Google Scholar] [CrossRef] [PubMed]
- Kang, J.H.; Wang, M.; Frueh, L.; Rosner, B.; Wiggs, J.L.; Elze, T.; Pasquale, L.R. Cohort Study of Race/Ethnicity and Incident Primary Open-Angle Glaucoma Characterized by Autonomously Determined Visual Field Loss Patterns. Transl. Vis. Sci. Technol. 2022, 11, 21. [Google Scholar] [CrossRef] [PubMed]
- Shi, M.; Luo, Y.; Tian, Y.; Shen, L.; Elze, T.; Zebardast, N.; Eslami, M.; Kazeminasab, S.; Boland, M.V.; Friedman, D.S.; et al. Equitable Artificial Intelligence for Glaucoma Screening with Fair Identity Normalization. medRxiv 2023. [Google Scholar] [CrossRef]
- Vieira, C.M.; Oliveira, M.V.D.C.; Guimarães, M.D.P.; Rocha, L.; Dias, D.R.C. Applied Explainable Artificial Intelligence (XAI) in the classification of retinal images for support in the diagnosis of Glaucoma. In Proceedings of the 29th Brazilian Symposium on Multimedia and the Web, Ribeirão Preto, Brazil, 23–27 October 2023; Association for Computing Machinery: New York, NY, USA, 2023; pp. 82–90. [Google Scholar] [CrossRef]
- Kamal, S.; Dey, N.; Chowdhury, L.; Hasan, S.I.; Santosh, K. Explainable AI for Glaucoma Prediction Analysis to Understand Risk Factors in Treatment Planning. IEEE Trans. Instrum. Meas. 2022, 71, 1–9. [Google Scholar] [CrossRef]
- Li, C. Glaucoma Detection Based on Optical Coherence Tomography Imaging. Master’s Thesis, Nanyang Technological University, Singapore, 2023. [Google Scholar] [CrossRef]
- Mehta, P.; Petersen, C.A.; Wen, J.C.; Banitt, M.R.; Chen, P.P.; Bojikian, K.D.; Egan, C.; Lee, S.-I.; Balazinska, M.; Lee, A.Y.; et al. Automated Detection of Glaucoma with Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images. Arch. Ophthalmol. 2021, 231, 154–169. [Google Scholar] [CrossRef]
- Ran, A.R.; Cheung, C.Y.; Wang, X.; Chen, H.; Luo, L.-Y.; Chan, P.P.; Wong, M.O.M.; Chang, R.T.; Mannil, S.S.; Young, A.L.; et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: A retrospective training and validation deep-learning analysis. Lancet Digit. Health 2019, 1, e172–e182. [Google Scholar] [CrossRef] [PubMed]
- Rai, A. Explainable AI: From black box to glass box. J. Acad. Mark. Sci. 2020, 48, 137–141. [Google Scholar] [CrossRef]
- Hossain, I.; Zamzmi, G.; Mouton, P.R.; Salekin, S.; Sun, Y.; Goldgof, D. Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions. ACM Comput. Surv. 2023. [Google Scholar] [CrossRef]
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Ji, P.X.; Ramalingam, V.; Balas, M.; Pickel, L.; Mathew, D.J. Artificial Intelligence in Glaucoma: A New Landscape of Diagnosis and Management. J. Clin. Transl. Ophthalmol. 2024, 2, 47-63. https://doi.org/10.3390/jcto2020005
Ji PX, Ramalingam V, Balas M, Pickel L, Mathew DJ. Artificial Intelligence in Glaucoma: A New Landscape of Diagnosis and Management. Journal of Clinical & Translational Ophthalmology. 2024; 2(2):47-63. https://doi.org/10.3390/jcto2020005
Chicago/Turabian StyleJi, Patrick **ang, Vethushan Ramalingam, Michael Balas, Lauren Pickel, and David J. Mathew. 2024. "Artificial Intelligence in Glaucoma: A New Landscape of Diagnosis and Management" Journal of Clinical & Translational Ophthalmology 2, no. 2: 47-63. https://doi.org/10.3390/jcto2020005