Economic Evaluations of Artificial Intelligence in Ophthalmology

Ruamviboonsuk, Paisan; Chantra, Somporn; Seresirikachorn, Kasem; Ruamviboonsuk, Varis; Sangroongruangsri, Sermsiri

doi:10.1097/apo.0000000000000403

Cited by 33 publications

(24 citation statements)

References 50 publications

(99 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cost is an important issue in the adoption of AI-assisted eye disease diagnosis technology. Therefore, it is necessary to conduct health economics evaluation [ 36 ]. Fortunately, evidence has shown the cost of screening could be saved by using AI technology, which is mainly attributable to the substantial reduction in human assessment time and workforce without sacrificing screening performance [ 5 ].…”

Section: Discussionmentioning

confidence: 99%

Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment

Lin¹,

Li²,

Zou³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

Background Deep learning–assisted eye disease diagnosis technology is increasingly applied in eye disease screening. However, no research has suggested the prerequisites for health care service providers and residents willing to use it. Objective The aim of this paper is to reveal the preferences of health care service providers and residents for using artificial intelligence (AI) in community-based eye disease screening, particularly their preference for accuracy. Methods Discrete choice experiments for health care providers and residents were conducted in Shanghai, China. In total, 34 medical institutions with adequate AI-assisted screening experience participated. A total of 39 medical staff and 318 residents were asked to answer the questionnaire and make a trade-off among alternative screening strategies with different attributes, including missed diagnosis rate, overdiagnosis rate, screening result feedback efficiency, level of ophthalmologist involvement, organizational form, cost, and screening result feedback form. Conditional logit models with the stepwise selection method were used to estimate the preferences. Results Medical staff preferred high accuracy: The specificity of deep learning models should be more than 90% (odds ratio [OR]=0.61 for 10% overdiagnosis; P<.001), which was much higher than the Food and Drug Administration standards. However, accuracy was not the residents’ preference. Rather, they preferred to have the doctors involved in the screening process. In addition, when compared with a fully manual diagnosis, AI technology was more favored by the medical staff (OR=2.08 for semiautomated AI model and OR=2.39 for fully automated AI model; P<.001), while the residents were in disfavor of the AI technology without doctors’ supervision (OR=0.24; P<.001). Conclusions Deep learning model under doctors’ supervision is strongly recommended, and the specificity of the model should be more than 90%. In addition, digital transformation should help medical staff move away from heavy and repetitive work and spend more time on communicating with residents.

show abstract

Section: Discussionmentioning

confidence: 99%

Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment

Lin¹,

Li²,

Zou³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Images obtained can be graded remotely by trained graders or using smartphone-based automated analysis software [ 20 ]. Recent evidence from cost-effectiveness analysis shows that AI, either standalone or used with humans, might be more cost-effective than manual DR screening [ 21 ]. Unfortunately, efforts to improve screening programs faced with the lack of treatments such as photocoagulation and intravitreal injections of vascular endothelial growth factor (VEGF) that are unavailable in many parts of Africa.…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of remote screening for diabetic retinopathy among patients referred to Mozambican Diabetes Association (AMODIA): a retrospective observational study

et al. 2022

View full text Add to dashboard Cite

Aims Diabetes represents a growing public health problem in sub-Saharan Africa, where diabetic retinopathy (DR) is a major cause of permanent visual loss. We reported the results of a remote screening of DR among urbanized Mozambican people with diabetes. Methods We retrospectively collected retinal images and clinical characteristics from 536 patients screened for DR in Maputo (Mozambique), over a period of 2 years (2018–2019). Retinal photographs were captured, digitally stored, and scored locally and by an expert ophthalmologist in Italy remotely. Results The overall prevalence of DR was 29% with sight-threatening forms accounting for 8.1% of that number. Inter-reader agreement between the local and the Italian ophthalmologists was poor ( k < 0.2). Patients with DR were older, had a longer duration of disease, worse glycaemic control, and a higher prevalence of comorbidities. In the multivariate logistic regression analysis, HbA1c, diabetes duration, and coronary heart disease (CHD) were associated with DR. Conclusion Prevalence of DR among urbanized Mozambican patients was similar to that observed in Western countries. Telediagnosis might partially overcome the paucity of local ophthalmologists with experience in DR. Supplementary Information The online version contains supplementary material available at 10.1007/s00592-021-01834-3.

show abstract

“…Nevertheless, it should be noted that for a proper assessment, correct segmentation is necessary, and often there is improvement in NVC recognition with manual segmentation [79,81,86,93,103,104]. In the future, the accuracy of NVC detection in vitreoretinal slabs might increase with improvement of auto segmentation, deep learning and artificial intelligence [81,86,[107][108][109][110][111]. IRMAs seem to generate higher false positive rates due to the retinal slab image, despite the vitreoretinal slab image not showing extension into the vitreous cavity [82].…”

Section: Discussionmentioning

confidence: 99%

Update on Optical Coherence Tomography and Optical Coherence Tomography Angiography Imaging in Proliferative Diabetic Retinopathy

2021

View full text Add to dashboard Cite

Proliferative diabetic retinopathy (PDR) is a major cause of blindness in diabetic individuals. Optical coherence tomography (OCT) and OCT-angiography (OCTA) are noninvasive imaging techniques useful for the diagnosis and assessment of PDR. We aim to review several recent developments using OCT and discuss their present and potential future applications in the clinical setting. An electronic database search was performed so as to include all studies assessing OCT and/or OCTA findings in PDR patients published from 1 January 2020 to 31 May 2021. Thirty studies were included, and the most recently published data essentially focused on the higher detection rate of neovascularization obtained with widefield-OCT and/or OCTA (WF-OCT/OCTA) and on the increasing quality of retinal imaging with quality levels non-inferior to widefield-fluorescein angiography (WF-FA). There were also significant developments in the study of retinal nonperfusion areas (NPAs) using these techniques and research on the impact of PDR treatment on NPAs and on vascular density. It is becoming increasingly clear that it is critical to use adequate imaging protocols focused on optimized segmentation and maximized imaged retinal area, with ongoing technological development through artificial intelligence and deep learning. These latest findings emphasize the growing applicability and role of noninvasive imaging in managing PDR with the added benefit of avoiding the repetition of invasive conventional FA.

show abstract

Economic Evaluations of Artificial Intelligence in Ophthalmology

Cited by 33 publications

References 50 publications

Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment

Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment

Effectiveness of remote screening for diabetic retinopathy among patients referred to Mozambican Diabetes Association (AMODIA): a retrospective observational study

Update on Optical Coherence Tomography and Optical Coherence Tomography Angiography Imaging in Proliferative Diabetic Retinopathy

Contact Info

Product

Resources

About