ArticleScientific reports2023
Assessing the external validity of machine learning-based detection of glaucoma.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Deep Learning Prediction of Personalized Peripapillary Retinal Nerve Fiber Layer Thickness Norms from Fundus Images in Glaucoma.medRxiv : the preprint server for health sciences · 2026Article
- Attention‑Guided Hybrid Deep Learning with Fibonacci and Annular Ring Handcrafted Features for Glaucoma Detection in Retinal Images.Journal of imaging informatics in medicine · 2026Article
- Applications of machine learning in glaucoma diagnosis based on tabular data: a systematic review.BMC biomedical engineering · 2025Article
- Optic Cup and Disc Segmentation of Fundus Images Using Artificial Intelligence Externally Validated With Optical Coherence Tomography Measurements.Translational vision science & technology · 2025Article
- Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.International journal of molecular sciences · 2025Review
- Integrating Deep Learning with Electronic Health Records for Early Glaucoma Detection: A Multi-Dimensional Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis.International journal of ophthalmology · 2024Article
- Assessing the transportability of clinical prediction models for cognitive impairment using causal models.BMC medical research methodology · 2023Article
- Non-Invasive Retinal Blood Vessel Wall Measurements with Polarization-Sensitive Optical Coherence Tomography for Diabetes Assessment: A Quantitative Study.Biomolecules · 2023Article
- Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion.Frontiers in physiology · 2023Article
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
- Utilizing deep learning to predict Alzheimer's disease and mild cognitive impairment with optical coherence tomography.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- A Practical Guide to Evaluating Artificial Intelligence Imaging Models in Scientific Literature.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Studies using machine learning (ML) approaches have reported high diagnostic accuracies for glaucoma detection. However, none assessed model performance across ethnicities. The aim of the study is to externally validate ML models for glaucoma detection from optical coherence tomography (OCT) data. We performed a prospective, cross-sectional study, where 514 Asians (257 glaucoma/257 controls) were enrolled to construct ML models for glaucoma detection, which was then tested on 356 Asians (183 glaucoma/173 controls) and 138 Caucasians (57 glaucoma/81 controls). We used the retinal nerve fibre layer (RNFL) thickness values produced by the compensation model, which is a multiple regression model fitted on healthy subjects that corrects the RNFL profile for anatomical factors and the original OCT data (measured) to build two classifiers, respectively. Both the ML models (area under the receiver operating [AUC] = 0.96 and accuracy = 92%) outperformed the measured data (AUC = 0.93; P < 0.001) for glaucoma detection in the Asian dataset. However, in the Caucasian dataset, the ML model trained with compensated data (AUC = 0.93 and accuracy = 84%) outperformed the ML model trained with original data (AUC = 0.83 and accuracy = 79%; P < 0.001) and measured data (AUC = 0.82; P < 0.001) for glaucoma detection. The performance with the ML model trained on measured data showed poor reproducibility across different datasets, whereas the performance of the compensated data was maintained. Care must be taken when ML models are applied to patient cohorts of different ethnicities.
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