Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program.
Attasit SrisubatKankamon KittrongsiriSermsiri SangroongruangsriChalida KhemvarananJacqueline Baras ShreibatiJack ChingJohn HernandezRicha TiwariFred HerschYun LiuPrut HanutsahaVaris RuamviboonsukSaowalak TurongkaraveeRajiv RamanPaisan RuamviboonsukPublished in: Ophthalmology and therapy (2023)
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.
Keyphrases
- diabetic retinopathy
- endothelial cells
- deep learning
- type diabetes
- cardiovascular disease
- clinical trial
- risk factors
- induced pluripotent stem cells
- optical coherence tomography
- pluripotent stem cells
- machine learning
- quality improvement
- skeletal muscle
- convolutional neural network
- resistance training
- weight loss
- single molecule
- replacement therapy