Clement C. Tham

Qinghua He, Hongyang Jiang, Danqi Fang et al.

Diabetic macular ischemia (DMI), marked by the loss of retinal capillaries in the macular area, contributes to vision impairment in patients with diabetes. Although color fundus photographs (CFPs), combined with artificial intelligence (AI), have been extensively applied in detecting various eye diseases, including diabetic retinopathy (DR), their applications in detecting DMI remain unexplored, partly due to skepticism among ophthalmologists regarding its feasibility. In this study, we propose a graph neural network-based multispectral view learning (GNN-MSVL) model designed to detect DMI from CFPs. The model leverages higher spectral resolution to capture subtle changes in fundus reflectance caused by ischemic tissue, enhancing sensitivity to DMI-related features. The proposed approach begins with computational multispectral imaging (CMI) to reconstruct 24-wavelength multispectral fundus images from CFPs. ResNeXt101 is employed as the backbone for multi-view learning to extract features from the reconstructed images. Additionally, a GNN with a customized jumper connection strategy is designed to enhance cross-spectral relationships, facilitating comprehensive and efficient multispectral view learning. The study included a total of 1,078 macula-centered CFPs from 1,078 eyes of 592 patients with diabetes, of which 530 CFPs from 530 eyes of 300 patients were diagnosed with DMI. The model achieved an accuracy of 84.7 percent and an area under the receiver operating characteristic curve (AUROC) of 0.900 (95 percent CI: 0.852-0.937) on eye-level, outperforming both the baseline model trained from CFPs and human experts (p-values less than 0.01). These findings suggest that AI-based CFP analysis holds promise for detecting DMI, contributing to its early and low-cost screening.

Erfan Noury, Suria S. Mannil, Robert T. Chang et al.

We describe a new approach to automated Glaucoma detection in 3D Spectral Domain Optical Coherence Tomography (OCT) optic nerve scans. First, we gathered a unique and diverse multi-ethnic dataset of OCT scans consisting of glaucoma and non-glaucomatous cases obtained from four tertiary care eye hospitals located in four different countries. Using this longitudinal data, we achieved state-of-the-art results for automatically detecting Glaucoma from a single raw OCT using a 3D Deep Learning system. These results are close to human doctors in a variety of settings across heterogeneous datasets and scanning environments. To verify correctness and interpretability of the automated categorization, we used saliency maps to find areas of focus for the model. Matching human doctor behavior, the model predictions indeed correlated with the conventional diagnostic parameters in the OCT printouts, such as the retinal nerve fiber layer. We further used our model to find new areas in the 3D data that are presently not being identified as a diagnostic parameter to detect glaucoma by human doctors. Namely, we found that the Lamina Cribrosa (LC) region can be a valuable source of helpful diagnostic information previously unavailable to doctors during routine clinical care because it lacks a quantitative printout. Our model provides such volumetric quantification of this region. We found that even when a majority of the RNFL is removed, the LC region can distinguish glaucoma. This is clinically relevant in high myopes, when the RNFL is already reduced, and thus the LC region may help differentiate glaucoma in this confounding situation. We further generalize this approach to create a new algorithm called DiagFind that provides a recipe for finding new diagnostic information in medical imagery that may have been previously unusable by doctors.

Xi Wang, Hao Chen, Luyang Luo et al.

Optical Coherence Tomography (OCT) imaging plays an important role in glaucoma diagnosis in clinical practice. Early detection and timely treatment can prevent glaucoma patients from permanent vision loss. However, only a dearth of automated methods has been developed based on OCT images for glaucoma study. In this paper, we present a novel framework to effectively classify glaucoma OCT images from normal ones. A semi-supervised learning strategy with smoothness assumption is applied for surrogate assignment of missing function regression labels. Besides, the proposed multi-task learning network is capable of exploring the structure and function relationship from the OCT image and visual field measurement simultaneously, which contributes to classification performance boosting. Essentially, we are the first to unify the structure analysis and function regression for glaucoma screening. It is also worth noting that we build the largest glaucoma OCT image dataset involving 4877 volumes to develop and evaluate the proposed method. Extensive experiments demonstrate that our framework outperforms the baseline methods and two glaucoma experts by a large margin, achieving 93.2%, 93.2% and 97.8% on accuracy, F1 score and AUC, respectively.