Glaucoma Screening With Artificial Intelligence
Study Details
Study Description
Brief Summary
This randomized clinical trial aims to compare the diagnostic performance of two AI-enabled screening strategies - ROTA (RNFL optical texture analysis) assessment versus optic disc photography - in detecting glaucoma within a population-based sample. Secondary objectives are to (1) compare the diagnostic performance of ROTA AI assessment versus OCT RNFL thickness assessment by AI, and ROTA AI assessment versus OCT RNFL thickness assessment by trained graders, (2) investigate the cost-effectiveness of AI ROTA assessment for glaucoma screening, and (3) estimate the prevalence of glaucoma in Hong Kong.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Detailed Description
Glaucoma is the leading cause of irreversible blindness affecting 76 million patients worldwide in 2020. Characterized by progressive degeneration of the optic nerve, early detection of disease deterioration with timely intervention is critical to prevent progressive loss in vision. In the 5th World Glaucoma Association Consensus Meeting, a diverse and representative group of glaucoma clinicians and scientists deliberated on the value and methods of glaucoma screening. Whereas it has been recognized that early detection of glaucoma for treatment is beneficial to preserve the quality of vision and quality of life as glaucoma treatments are often effective, easy to use and well tolerated, the optimal screening strategy for glaucoma has not yet been determined.
ROTA (Retinal Nerve Fiber Layer Optical Texture Analysis) is a patented algorithm designed to detect axonal fiber bundle loss in glaucoma. Unlike conventional Optical Coherence Tomography (OCT) analysis, ROTA uses non-linear transformation to reveal the optical textures and trajectories of axonal fiber bundles, allowing for intuitive and reliable recognition of RNFL abnormalities without the need for normative databases. It can be applied across different OCT models and is particularly effective at detecting focal RNFL defects in early glaucoma and varying degrees of RNFL damage in end-stage glaucoma. The proposed study will address whether the application AI on ROTA is feasible and cost-effective in the setting of glaucoma screening, and whether ROTA would outperform optic disc photography and OCT RNFL thickness assessment.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Retinal nerve fiber layer optical texture analysis (ROTA) The RNFL is imaged with OCT for ROTA. |
Diagnostic Test: ROTA assessment by AI
The RNFL is imaged with OCT for ROTA and the data are analyzed with a deep learning model.
|
| Active Comparator: Optic disc photography The optic disc is imaged with color fundus camera. |
Diagnostic Test: Optic disc assessment by AI
The optic disc is imaged with color fundus camera and the data are analyzed with a deep learning model.
|
Outcome Measures
Primary Outcome Measures
- Diagnostic performance for detection of glaucoma [up to ~1 year]
The area under the receiver operating characteristic curve (AUC) for detection of glaucoma
Secondary Outcome Measures
- Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma [up to ~1 year]
ICER for glaucoma screening measured by incremental cost per true positive case detected, incremental cost per incremental QALY
- The prevalence of glaucoma [up to ~1 year]
Proportion of patients with glaucoma
Other Outcome Measures
- Diagnostic performance for detection of macular diseases [up to ~1 year]
The area under the receiver operating characteristic curve (AUC) for detection of macular diseases
- Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma and macular diseases [up to ~1 year]
ICER for glaucoma and macular diseases screening measured by incremental cost per true positive case detected, incremental cost per incremental QALY
- The prevalence of macular diseases [up to ~1 year]
Proportion of patients with macular diseases
Eligibility Criteria
Criteria
Inclusion Criteria:
- Individuals aged 50 years or above
Exclusion Criteria:
-
Physically incapacitated
-
Not able to cooperate for clinical examination or optical coherence tomography (OCT) investigation will be excluded
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- The University of Hong Kong
- Orbis
Investigators
- Principal Investigator: Christopher Leung, The University of Hong Kong
Study Documents (Full-Text)
None provided.More Information
Additional Information:
- Glaucoma Screening, Consensus Series - 5. Hague, Netherlands: Kugler Publications, 2008.
- Consensus series 10 - Diagnosis of primary open angle glaucoma (Kugler Publications, 2016).
- Optical Texture Analysis of the Inner Retina (US 20190110681)
Publications
- Biswas S, Lin C, Leung CK. Evaluation of a Myopic Normative Database for Analysis of Retinal Nerve Fiber Layer Thickness. JAMA Ophthalmol. 2016 Sep 1;134(9):1032-9. doi: 10.1001/jamaophthalmol.2016.2343. Erratum In: JAMA Ophthalmol. 2016 Nov 1;134(11):1336.
- Flaxman SR, Bourne RRA, Resnikoff S, Ackland P, Braithwaite T, Cicinelli MV, Das A, Jonas JB, Keeffe J, Kempen JH, Leasher J, Limburg H, Naidoo K, Pesudovs K, Silvester A, Stevens GA, Tahhan N, Wong TY, Taylor HR; Vision Loss Expert Group of the Global Burden of Disease Study. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob Health. 2017 Dec;5(12):e1221-e1234. doi: 10.1016/S2214-109X(17)30393-5. Epub 2017 Oct 11.
- He M, Foster PJ, Ge J, Huang W, Zheng Y, Friedman DS, Lee PS, Khaw PT. Prevalence and clinical characteristics of glaucoma in adult Chinese: a population-based study in Liwan District, Guangzhou. Invest Ophthalmol Vis Sci. 2006 Jul;47(7):2782-8. doi: 10.1167/iovs.06-0051.
- Hou HW, Lin C, Leung CK. Integrating Macular Ganglion Cell Inner Plexiform Layer and Parapapillary Retinal Nerve Fiber Layer Measurements to Detect Glaucoma Progression. Ophthalmology. 2018 Jun;125(6):822-831. doi: 10.1016/j.ophtha.2017.12.027. Epub 2018 Feb 9.
- Kim JS, Ishikawa H, Sung KR, Xu J, Wollstein G, Bilonick RA, Gabriele ML, Kagemann L, Duker JS, Fujimoto JG, Schuman JS. Retinal nerve fibre layer thickness measurement reproducibility improved with spectral domain optical coherence tomography. Br J Ophthalmol. 2009 Aug;93(8):1057-63. doi: 10.1136/bjo.2009.157875. Epub 2009 May 7.
- Leung CK, Cheung CY, Weinreb RN, Qiu Q, Liu S, Li H, Xu G, Fan N, Huang L, Pang CP, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a variability and diagnostic performance study. Ophthalmology. 2009 Jul;116(7):1257-63, 1263.e1-2. doi: 10.1016/j.ophtha.2009.04.013. Epub 2009 May 22.
- Leung CK, Choi N, Weinreb RN, Liu S, Ye C, Liu L, Lai GW, Lau J, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: pattern of RNFL defects in glaucoma. Ophthalmology. 2010 Dec;117(12):2337-44. doi: 10.1016/j.ophtha.2010.04.002. Epub 2010 Aug 3.
- Leung CK, Lam S, Weinreb RN, Liu S, Ye C, Liu L, He J, Lai GW, Li T, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: analysis of the retinal nerve fiber layer map for glaucoma detection. Ophthalmology. 2010 Sep;117(9):1684-91. doi: 10.1016/j.ophtha.2010.01.026. Epub 2010 Jul 21.
- Leung CK, Mohamed S, Leung KS, Cheung CY, Chan SL, Cheng DK, Lee AK, Leung GY, Rao SK, Lam DS. Retinal nerve fiber layer measurements in myopia: An optical coherence tomography study. Invest Ophthalmol Vis Sci. 2006 Dec;47(12):5171-6. doi: 10.1167/iovs.06-0545.
- Leung CK, Yu M, Weinreb RN, Lai G, Xu G, Lam DS. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: patterns of retinal nerve fiber layer progression. Ophthalmology. 2012 Sep;119(9):1858-66. doi: 10.1016/j.ophtha.2012.03.044. Epub 2012 Jun 5.
- Leung CKS, Lam AKN, Weinreb RN, Garway-Heath DF, Yu M, Guo PY, Chiu VSM, Wan KHN, Wong M, Wu KZ, Cheung CYL, Lin C, Chan CKM, Chan NCY, Kam KW, Lai GWK. Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer. Nat Biomed Eng. 2022 May;6(5):593-604. doi: 10.1038/s41551-021-00813-x. Epub 2022 Jan 6.
- 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 Aug;125(8):1199-1206. doi: 10.1016/j.ophtha.2018.01.023. Epub 2018 Mar 2.
- Lin D, Xiong J, Liu C, Zhao L, Li Z, Yu S, Wu X, Ge Z, Hu X, Wang B, Fu M, Zhao X, Wang X, Zhu Y, Chen C, Li T, Li Y, Wei W, Zhao M, Li J, Xu F, Ding L, Tan G, Xiang Y, Hu Y, Zhang P, Han Y, Li JO, Wei L, Zhu P, Liu Y, Chen W, Ting DSW, Wong TY, Chen Y, Lin H. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lancet Digit Health. 2021 Aug;3(8):e486-e495. doi: 10.1016/S2589-7500(21)00086-8.
- Liu H, Li L, Wormstone IM, Qiao C, Zhang C, Liu P, Li S, Wang H, Mou D, Pang R, Yang D, Zangwill LM, Moghimi S, Hou H, Bowd C, Jiang L, Chen Y, Hu M, Xu Y, Kang H, Ji X, Chang R, Tham C, Cheung C, Ting DSW, Wong TY, Wang Z, Weinreb RN, Xu M, Wang N. Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs. JAMA Ophthalmol. 2019 Dec 1;137(12):1353-1360. doi: 10.1001/jamaophthalmol.2019.3501. Erratum In: JAMA Ophthalmol. 2019 Dec 1;137(12):1468.
- Oddone F, Lucenteforte E, Michelessi M, Rizzo S, Donati S, Parravano M, Virgili G. Macular versus Retinal Nerve Fiber Layer Parameters for Diagnosing Manifest Glaucoma: A Systematic Review of Diagnostic Accuracy Studies. Ophthalmology. 2016 May;123(5):939-49. doi: 10.1016/j.ophtha.2015.12.041. Epub 2016 Feb 15.
- Pierro L, Gagliardi M, Iuliano L, Ambrosi A, Bandello F. Retinal nerve fiber layer thickness reproducibility using seven different OCT instruments. Invest Ophthalmol Vis Sci. 2012 Aug 31;53(9):5912-20. doi: 10.1167/iovs.11-8644.
- Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014 Nov;121(11):2081-90. doi: 10.1016/j.ophtha.2014.05.013. Epub 2014 Jun 26.
- Weinreb RN, Leung CK, Crowston JG, Medeiros FA, Friedman DS, Wiggs JL, Martin KR. Primary open-angle glaucoma. Nat Rev Dis Primers. 2016 Sep 22;2:16067. doi: 10.1038/nrdp.2016.67.
- Wu K, Lin C, Lam AK, Chan L, Leung CK. Wide-field Trend-based Progression Analysis of Combined Retinal Nerve Fiber Layer and Ganglion Cell Inner Plexiform Layer Thickness: A New Paradigm to Improve Glaucoma Progression Detection. Ophthalmology. 2020 Oct;127(10):1322-1330. doi: 10.1016/j.ophtha.2020.03.019. Epub 2020 Mar 29.
- Xu G, Weinreb RN, Leung CK. Optic nerve head deformation in glaucoma: the temporal relationship between optic nerve head surface depression and retinal nerve fiber layer thinning. Ophthalmology. 2014 Dec;121(12):2362-70. doi: 10.1016/j.ophtha.2014.06.035. Epub 2014 Aug 6.
- Xu G, Weinreb RN, Leung CKS. Retinal nerve fiber layer progression in glaucoma: a comparison between retinal nerve fiber layer thickness and retardance. Ophthalmology. 2013 Dec;120(12):2493-2500. doi: 10.1016/j.ophtha.2013.07.027. Epub 2013 Sep 17.
- Yu M, Lin C, Weinreb RN, Lai G, Chiu V, Leung CK. Risk of Visual Field Progression in Glaucoma Patients with Progressive Retinal Nerve Fiber Layer Thinning: A 5-Year Prospective Study. Ophthalmology. 2016 Jun;123(6):1201-10. doi: 10.1016/j.ophtha.2016.02.017. Epub 2016 Mar 19.
- Zheng F, Yu M, Leung CK. Diagnostic criteria for detection of retinal nerve fibre layer thickness and neuroretinal rim width abnormalities in glaucoma. Br J Ophthalmol. 2020 Feb;104(2):270-275. doi: 10.1136/bjophthalmol-2018-313581. Epub 2019 May 30.
- H012_Protocol_Glaucoma