Combining Optical Coherence Tomography and Fundus Photography to Improve Glaucoma Screening

Artificial intelligence in stroke risk assessment and management via retinal imaging

Retinal imaging, used for assessing stroke-related retinal changes, is a non-invasive and cost-effective method that can be enhanced by machine learning and deep learning algorithms, showing promise in early disease detection, severity grading, and prognostic evaluation in stroke patients. This review explores the role of artificial intelligence (AI) in stroke patient care, focusing on retinal imaging integration into clinical workflows. Retinal imaging has revealed several microvascular changes, including a decrease in the central retinal artery diameter and an increase in the central retinal vein diameter, both of which are associated with lacunar stroke and intracranial hemorrhage. Additionally, microvascular changes, such as arteriovenous nicking, increased vessel tortuosity, enhanced arteriolar light reflex, decreased retinal fractals, and thinning of retinal nerve fiber layer are also reported to be associated with higher stroke risk. AI models, such as Xception and EfficientNet, have demonstrated accuracy comparable to traditional stroke risk scoring systems in predicting stroke risk. For stroke diagnosis, models like Inception, ResNet, and VGG, alongside machine learning classifiers, have shown high efficacy in distinguishing stroke patients from healthy individuals using retinal imaging. Moreover, a random forest model effectively distinguished between ischemic and hemorrhagic stroke subtypes based on retinal features, showing superior predictive performance compared to traditional clinical characteristics. Additionally, a support vector machine model has achieved high classification accuracy in assessing pial collateral status. Despite this advancements, challenges such as the lack of standardized protocols for imaging modalities, hesitance in trusting AI-generated predictions, insufficient integration of retinal imaging data with electronic health records, the need for validation across diverse populations, and ethical and regulatory concerns persist. Future efforts must focus on validating AI models across diverse populations, ensuring algorithm transparency, and addressing ethical and regulatory issues to enable broader implementation. Overcoming these barriers will be essential for translating this technology into personalized stroke care and improving patient outcomes.

Evaluating Diagnostic Concordance in Primary Open-Angle Glaucoma Among Academic Glaucoma Subspecialists

Objective: The study aimed to evaluate the interobserver agreement among glaucoma subspecialists in diagnosing glaucoma and to explore the causes of diagnostic discrepancies. Methods: Three experienced glaucoma subspecialists independently assessed frequency domain optical coherence tomography, fundus color photographs, and static perimetry results from 464 eyes of 275 participants, adhering to unified glaucoma diagnostic criteria. All data were collected from the Wenzhou Glaucoma Progression Study between August 2014 and June 2021. Results: The overall interobserver agreement among the three experts was poor, with a Fleiss' kappa value of 0.149. The kappa values interobserver agreement between pairs of experts ranged from 0.133 to 0.282. In 50 cases, or approximately 10.8%, the three experts reached completely different diagnoses. Agreement was more likely in cases involving larger average cup-to-disc ratios, greater vertical cup-to-disc ratios, more severe visual field defects, and thicker retinal nerve fiber layer measurements, particularly in the temporal and inferior quadrants. High myopia also negatively impacted interobserver agreement. Conclusions: Despite using unified diagnostic criteria for glaucoma, significant differences in interobserver consistency persist among glaucoma subspecialists. To improve interobserver agreement, it is recommended to provide additional training on standardized diagnostic criteria. Furthermore, for cases with inconsistent diagnoses, long-term follow-up is essential to confirm the diagnosis of glaucoma.

Automatic retinal image analysis methods using colour fundus images for screening glaucomatous optic neuropathy

OBJECTIVES

Train an automatic retinal image analysis (ARIA) method to screen glaucomatous optic neuropathy (GON) on non-mydriatic retinal images labelled with the additional results of optical coherence tomography (OCT) and assess different models for the GON classification.

METHODS

All the images were obtained from the hospital for training and 10-fold cross-validation. Two methods were used to improve the classification performance: (1) using images labelled with the additional results of OCT as the reference standard and (2) generating models using retinal features from the entire images, the region of interest (ROI) of the optic disc, and the ROI of the macula, and the combination of all the features.

RESULTS

Overall, we collected 1338 images with paired OCT scans. In 10-fold validation, ARIA achieved sensitivities of 92.2 %, 92.7% and 85.7%, specificities of 88.8%, 86.7% and 80.2% and accuracies of 90.6%, 89.9% and 83.1% using the retinal features from the entire images, the ROI of the optic disc and the ROI of the macula, respectively. We found the model combining all the features has the best classification performance and obtained a sensitivity of 92.5%, a specificity of 92.1% and an accuracy of 92.4%, which is significantly different from other models (p<0.001).

CONCLUSION

We used two methods to improve the classification performance and found the best model to detect glaucoma on colour fundus retinal images. It can become a cost-effective and relatively more accurate glaucoma screening tool than conventional methods.

Comparative Evaluation of Fundus Image Interpretation Accuracy in Glaucoma Screening Among Different Physician Groups

Purpose

To examine the variability in glaucoma screening using fundus images among physicians, including non-ophthalmologists.

Patients and Methods

Sixty-nine eyes from 69 patients, including 25 eyes with glaucoma, were included from the Jikei University Hospital from July 2019 to December 2022. Fundus images were captured using TRC-NW8 (Topcon Corporation, Tokyo, Japan), and were interpreted by 10 non-ophthalmologists, 10 non-specialist ophthalmologists, and 9 specialists for diagnostic accuracy. We analyzed differences in diagnostic accuracy among the three groups. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Kappa coefficient were compared, using the Kruskal-Wallis test followed by a post hoc Dunn's test.

Results

The sensitivity and specificity were 0.22 and 0.92 for non-ophthalmologists, 0.49 and 0.83 for non-specialist ophthalmologists, and 0.68 and 0.87 for specialists, respectively. Both specialists and non-specialist ophthalmologists showed significantly higher sensitivity than non-ophthalmologists (Dunn's test, P<0.001 and P=0.031). There was no significant difference in specificity among the three groups (Kruskal-Wallis test, P=0.086). The PPV did not differ significantly between the groups (Kruskal-Wallis test, P=0.108), while the NPV was significantly higher in specialists compared to non-ophthalmologists (Dunn's test, P<0.001). Specialists also had a significantly higher Kappa coefficient than non-ophthalmologists and non-specialist ophthalmologists (Dunn's test, P<0.001 and P=0.024).

Conclusion

Diagnostic accuracy varied significantly based on the physician's background.

Innovations in Diabetic Macular Edema Management: A Comprehensive Review of Automated Quantification and Anti-vascular Endothelial Growth Factor Intervention

Diabetic macular edema (DME) poses a significant threat to the vision and quality of life of individuals with diabetes. This comprehensive review explores recent advancements in DME management, focusing on integrating automated quantification techniques and anti-vascular endothelial growth factor (anti-VEGF) interventions. The review begins with an overview of DME, emphasizing its prevalence, impact on diabetic patients, and current challenges in management. It then delves into the potential of automated quantification, leveraging machine learning and artificial intelligence to improve early detection and monitoring. Concurrently, the role of anti-VEGF therapies in addressing the underlying vascular abnormalities in DME is scrutinized. The review synthesizes vital findings, highlighting the implications for the future of DME management. Promising outcomes from recent clinical trials and case studies are discussed, providing insights into the evolving landscape of personalized medicine approaches. The conclusion underscores the transformative potential of these innovations, calling for continued research, collaboration, and integration of these advancements into clinical practice. This review aims to serve as a roadmap for researchers, clinicians, and industry stakeholders, fostering a collective effort to enhance the precision and efficacy of DME management.

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.