Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program
Issued Date
2023-04-01
Resource Type
ISSN
21938245
eISSN
21936528
Scopus ID
2-s2.0-85148894989
Journal Title
Ophthalmology and Therapy
Volume
12
Issue
2
Start Page
1339
End Page
1357
Rights Holder(s)
SCOPUS
Bibliographic Citation
Ophthalmology and Therapy Vol.12 No.2 (2023) , 1339-1357
Suggested Citation
Srisubat A., Kittrongsiri K., Sangroongruangsri S., Khemvaranan C., Shreibati J.B., Ching J., Hernandez J., Tiwari R., Hersch F., Liu Y., Hanutsaha P., Ruamviboonsuk V., Turongkaravee S., Raman R., Ruamviboonsuk P. Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program. Ophthalmology and Therapy Vol.12 No.2 (2023) , 1339-1357. 1357. doi:10.1007/s40123-023-00688-y Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/82090
Title
Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program
Other Contributor(s)
Abstract
Introduction: Deep learning (DL) for screening diabetic retinopathy (DR) has the potential to address limited healthcare resources by enabling expanded access to healthcare. However, there is still limited health economic evaluation, particularly in low- and middle-income countries, on this subject to aid decision-making for DL adoption. Methods: In the context of a middle-income country (MIC), using Thailand as a model, we constructed a decision tree-Markov hybrid model to estimate lifetime costs and outcomes of Thailand’s national DR screening program via DL and trained human graders (HG). We calculated the incremental cost-effectiveness ratio (ICER) between the two strategies. Sensitivity analyses were performed to probe the influence of modeling parameters. Results: From a societal perspective, screening with DL was associated with a reduction in costs of ~ US$ 2.70, similar quality-adjusted life-years (QALY) of + 0.0043, and an incremental net monetary benefit of ~ US$ 24.10 in the base case. In sensitivity analysis, DL remained cost-effective even with a price increase from US$ 1.00 to US$ 4.00 per patient at a Thai willingness-to-pay threshold of ~ US$ 4.997 per QALY gained. When further incorporating recent findings suggesting improved compliance to treatment referral with DL, our analysis models effectiveness benefits of ~ US$ 20 to US$ 50 depending on compliance. Conclusion: DR screening using DL in an MIC using Thailand as a model may result in societal cost-savings and similar health outcomes compared with HG. This study may provide an economic rationale to expand DL-based DR screening in MICs as an alternative solution for limited availability of skilled human resources for primary screening, particularly in MICs with similar prevalence of diabetes and low compliance to referrals for treatment.