Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and Meta-analysis

Murtagh, Patrick; Greene, Garrett; O’Brien, Colm

doi:10.18240/ijo.2020.01.22

Cited by 29 publications

(23 citation statements)

References 65 publications

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“…The past several years has witnessed significant technology advancement of artificial intelligence (AI) in glaucoma detection [ 8 , 10 – 12 ]. The idea is that a large amount of glaucoma specialist-labelled fundus images is used to train deep learning system (DLS) so that the algorithms can establish the association of abnormality patterns of the cup-to-disk ratio and optic disc hemorrhage specifically with glaucomatous optic neuropathy [ 13 ]. The advantages of AI automated glaucoma diagnosis are not only simple and fast, but also improved accuracy without relying on the subject judgement of experts [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…The idea is that a large amount of glaucoma specialist-labelled fundus images is used to train deep learning system (DLS) so that the algorithms can establish the association of abnormality patterns of the cup-to-disk ratio and optic disc hemorrhage specifically with glaucomatous optic neuropathy [ 13 ]. The advantages of AI automated glaucoma diagnosis are not only simple and fast, but also improved accuracy without relying on the subject judgement of experts [ 13 , 14 ]. Therefore, researchers have advocated that integrating AI into community screening can overcome resource and capability deficiencies of primary care centers by providing diagnostic support to ophthalmologists [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2021

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Background Population-based screening was essential for glaucoma management. Although various studies have investigated the cost-effectiveness of glaucoma screening, policymakers facing with uncontrollably growing total health expenses were deeply concerned about the potential financial consequences of glaucoma screening. This present study was aimed to explore the impact of glaucoma screening with artificial intelligence (AI) automated diagnosis from a budgetary standpoint in Changjiang county, China. Methods A Markov model based on health care system’s perspective was adapted from previously published studies to predict disease progression and healthcare costs. A cohort of 19,395 individuals aged 65 and above were simulated over a 15-year timeframe. Fur illustrative purpose, we only considered primary angle-closure glaucoma (PACG) in this study. Prevalence, disease progression risks between stages, compliance rates were obtained from publish studies. We did a meta-analysis to estimate diagnostic performance of AI automated diagnosis system from fundus image. Screening costs were provided by the Changjiang screening programme, whereas treatment costs were derived from electronic medical records from two county hospitals. Main outcomes included the number of PACG patients and health care costs. Cost-offset analysis was employed to compare projected health outcomes and medical care costs under the screening with what they would have been without screening. One-way sensitivity analysis was conducted to quantify uncertainties around model results. Results Among people aged 65 and above in Changjiang county, it was predicted that there were 1940 PACG patients under the AI-assisted screening scenario, compared with 2104 patients without screening in 15 years’ time. Specifically, the screening would reduce patients with primary angle closure suspect by 7.7%, primary angle closure by 8.8%, PACG by 16.7%, and visual blindness by 33.3%. Due to early diagnosis and treatment under the screening, healthcare costs surged dramatically to $107,761.4 dollar in the first year and then were constantly declining over time, while without screening costs grew from $14,759.8 in the second year until peaking at $17,900.9 in the 9th year. However, cost-offset analysis revealed that additional healthcare costs resulted from the screening could not be offset by decreased disease progression. The 5-, 10-, and 15-year accumulated incremental costs of screening versus no screening were estimated to be $396,362.8, $424,907.9, and $434,903.2, respectively. As a result, the incremental cost per PACG of any stages prevented was $1464.3. Conclusions This study represented the first attempt to address decision-maker’s budgetary concerns when adopting glaucoma screening by developing a Markov prediction model to project health outcomes and costs. Population screening combined with AI automated diagnosis for PACG in China were able to reduce disease progression risks. However, the excess costs of screening could never be offset by reduction in disease progression. Further studies examining the cost-effectiveness or cost-utility of AI-assisted glaucoma screening were needed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…While ML techniques in ophthalmology have been focused on posterior segment diseases, applications in ML have been applied to anterior segment diseases including cataracts and glaucoma. 12,13 Cataracts serve as the most common cause of vision impairment across the globe, but screening continues to be a longstanding challenge in rural and underserved areas due to the limited number of local ophthalmologists. Current algorithms have employed slit lamp photographs of cataracts with varying opacities to train ML models to detect and grade this prevalent ocular finding for possible referral.…”

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confidence: 99%

“…12 Glaucoma is an anterior segment disease that often goes undetected until central vision is affected due to the disease's progressive loss of peripheral vision. 13 Severe, untreated glaucoma can lead to a serious reduction in the quality of life and independence. However, the subtle progression of this glaucoma-induced optic neuropathy can be significantly attenuated when detected and treated early.…”

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confidence: 99%

“…ML algorithms have been developed with OCT and fundus photos to detect glaucomatous changes even when individuals have not realised these visual changes. 13 When applied into clinical practice, ML has a promising future in revolutionising how ophthalmologists can swiftly detect and treat various ocular diseases. However, while ophthalmology has served as a pioneer in this technology within healthcare, many of these AI algorithms are still in development.…”

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