Glaucoma Screening With Artificial Intelligence

NCT06012058 | Unknown

• 1 other identifier: H012_Protocol_Glaucoma
• Study Type: interventional
• Target: 3,175
• Locations: 1 country
• Sites: 2

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.

Conditions

Glaucoma

Keywords

Ophthalmology; Glaucoma; Artificial Intelligence; Retinal Nerve Fiber Layer Optical Texture Analysis; Optic Disc Photography Assessment

Outcome Measures

Primary Outcomes (1)

• Diagnostic performance for detection of glaucoma

  The area under the receiver operating characteristic curve (AUC) for detection of glaucoma

  up to ~1 year

Secondary Outcomes (2)

• Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma

  up to ~1 year

• The prevalence of glaucoma

  up to ~1 year

Other Outcomes (3)

• Diagnostic performance for detection of macular diseases

  up to ~1 year

• Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma and macular diseases

  up to ~1 year

• The prevalence of macular diseases

  up to ~1 year

Study Arms (2)

Retinal nerve fiber layer optical texture analysis (ROTA)

EXPERIMENTAL

The RNFL is imaged with OCT for ROTA.

Diagnostic Test: ROTA assessment by AI

Optic disc photography

ACTIVE COMPARATOR

The optic disc is imaged with color fundus camera.

Diagnostic Test: Optic disc assessment by AI

Interventions

ROTA assessment by AI DIAGNOSTIC_TEST

The RNFL is imaged with OCT for ROTA and the data are analyzed with a deep learning model.

Retinal nerve fiber layer optical texture analysis (ROTA)

Optic disc assessment by AI DIAGNOSTIC_TEST

The optic disc is imaged with color fundus camera and the data are analyzed with a deep learning model.

Optic disc photography

Eligibility Criteria

• Age: 50 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Individuals aged 50 years or above

You may not qualify if:

• Physically incapacitated
• Not able to cooperate for clinical examination or optical coherence tomography (OCT) investigation will be excluded

Sponsors & Collaborators

• The University of Hong Kong: lead
• Orbis: collaborator

Study Sites (2)

Southern District Wah Kwai Community Centre

Aberdeen, Hong Kong

RECRUITING

Kwun Tong District Health Centre

Kwun Tong, Hong Kong

RECRUITING

Related Publications (23)

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  PMID: 29032195 BACKGROUND

• 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.

  PMID: 24974815 BACKGROUND

• 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.

  PMID: 27654570 BACKGROUND

• 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.

  PMID: 19429591 BACKGROUND

• 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.

  PMID: 19464061 BACKGROUND

• 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.

  PMID: 22871835 BACKGROUND

• 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.

  PMID: 20663563 BACKGROUND

• 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.

  PMID: 20678802 BACKGROUND

• 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.

  PMID: 22677426 BACKGROUND

• 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.

  PMID: 24053994 BACKGROUND

• 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.

  PMID: 25108319 BACKGROUND

• 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.

  PMID: 26891880 BACKGROUND

• 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.

  PMID: 27442185 BACKGROUND

• 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.

  PMID: 17122099 BACKGROUND

• 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.

  PMID: 34992272 BACKGROUND

• 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.

  PMID: 31147377 BACKGROUND

• 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.

  PMID: 34325853 BACKGROUND

• 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.

  PMID: 29506863 BACKGROUND

• 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.

  PMID: 31513266 BACKGROUND

• 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.

  PMID: 16799014 BACKGROUND

• 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.

  PMID: 29433852 BACKGROUND

• 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.

  PMID: 27001534 BACKGROUND

• 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.

  PMID: 32423768 BACKGROUND

Related Links

• 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)

MeSH Terms

Conditions

Glaucoma

Condition Hierarchy (Ancestors)

Ocular Hypertension; Eye Diseases

Study Officials

• Christopher Leung

  The University of Hong Kong

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Anita Yau

CONTACT

39102673; anitayky@hku.hk

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Model Details: This is a randomized clinical trial with the primary objective to compare the diagnostic performance of two screening strategies - Retinal nerve fiber layer Optical Texture Analysis (ROTA) assessment by Artificial Intelligence (AI) versus (vs.) optic disc photography assessment by AI or trained graders - for detection of glaucoma in a population-based sample.

Study Record Dates

• First Submitted: August 21, 2023
• First Posted: August 25, 2023
• Study Start: August 26, 2023
• Primary Completion: August 25, 2024
• Study Completion: February 25, 2025
• Last Updated: September 21, 2023

Record last verified: 2023-09

Data Sharing

• IPD Sharing: Will not share

Locations

HK (2)