Nderitu Paul, Nunez do Rio Joan M, Webster Laura, Mann Samantha, Cardoso M Jorge, Modat Marc, Hopkins David, Bergeles Christos, Jackson Timothy L
Section of Ophthalmology, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Department of Ophthalmology, King's Ophthalmology Research Unit (KORU), King's College Hospital, London, UK.
Commun Med (Lond). 2024 Aug 21;4(1):167. doi: 10.1038/s43856-024-00590-z.
Predicting diabetic retinopathy (DR) progression could enable individualised screening with prompt referral for high-risk individuals for sight-saving treatment, whilst reducing screening burden for low-risk individuals. We developed and validated deep learning systems (DLS) that predict 1, 2 and 3 year emergent referable DR and maculopathy using risk factor characteristics (tabular DLS), colour fundal photographs (image DLS) or both (multimodal DLS).
From 162,339 development-set eyes from south-east London (UK) diabetic eye screening programme (DESP), 110,837 had eligible longitudinal data, with the remaining 51,502 used for pretraining. Internal and external (Birmingham DESP, UK) test datasets included 27,996, and 6928 eyes respectively.
Internal multimodal DLS emergent referable DR, maculopathy or either area-under-the receiver operating characteristic (AUROC) were 0.95 (95% CI: 0.92-0.98), 0.84 (0.82-0.86), 0.85 (0.83-0.87) for 1 year, 0.92 (0.87-0.96), 0.84 (0.82-0.87), 0.85 (0.82-0.87) for 2 years, and 0.85 (0.80-0.90), 0.79 (0.76-0.82), 0.79 (0.76-0.82) for 3 years. External multimodal DLS emergent referable DR, maculopathy or either AUROC were 0.93 (0.88-0.97), 0.85 (0.80-0.89), 0.85 (0.76-0.85) for 1 year, 0.93 (0.89-0.97), 0.79 (0.74-0.84), 0.80 (0.76-0.85) for 2 years, and 0.91 (0.84-0.98), 0.79 (0.74-0.83), 0.79 (0.74-0.84) for 3 years.
Multimodal and image DLS performance is significantly better than tabular DLS at all intervals. DLS accurately predict 1, 2 and 3 year emergent referable DR and referable maculopathy using colour fundal photographs, with additional risk factor characteristics conferring improvements in prognostic performance. Proposed DLS are a step towards individualised risk-based screening, whereby AI-assistance allows high-risk individuals to be closely monitored while reducing screening burden for low-risk individuals.
预测糖尿病视网膜病变（DR）的进展能够实现个性化筛查，及时将高危个体转诊以进行挽救视力的治疗，同时减轻低危个体的筛查负担。我们开发并验证了深度学习系统（DLS），该系统利用危险因素特征（表格DLS）、彩色眼底照片（图像DLS）或两者结合（多模态DLS）来预测1年、2年和3年后出现的可转诊DR及黄斑病变。
在英国伦敦东南部糖尿病眼病筛查项目（DESP）的162,339只用于开发的眼睛中，110,837只具有符合条件的纵向数据，其余51,502只用于预训练。内部和外部（英国伯明翰DESP）测试数据集分别包括27,996只和6928只眼睛。
内部多模态DLS预测1年后出现可转诊DR、黄斑病变或两者的受试者工作特征曲线下面积（AUROC）分别为0.95（95%CI：0.92 - 0.98）、0.84（0.82 - 0.86）、0.85（0.83 - 0.87）；2年后分别为0.92（0.87 - 0.96）、0.84（0.82 - 0.87）、0.85（0.82 - 0.87）；3年后分别为0.85（0.80 - 0.90）、0.79（0.76 - 0.82）、0.79（0.76 - 0.82）。外部多模态DLS预测1年后出现可转诊DR、黄斑病变或两者的AUROC分别为0.93（0.88 - 0.97）、0.85（0.80 - 0.89）、0.85（0.76 - 0.85）；2年后分别为0.93（0.89 - 0.97）、0.79（0.74 - 0.84）、0.80（0.76 - 0.85）；3年后分别为0.91（0.84 - 0.98）、0.79（0.74 - 0.83）、0.79（0.74 - 0.84）。
在所有时间间隔内，多模态和图像DLS的性能均显著优于表格DLS。DLS使用彩色眼底照片能够准确预测1年、2年和3年后出现的可转诊DR及可转诊黄斑病变，额外的危险因素特征可改善预测性能。所提出的DLS是迈向基于风险的个性化筛查的一步，通过人工智能辅助可以密切监测高危个体，同时减轻低危个体的筛查负担。
