Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

Global research trends and future directions in diabetic macular edema research: A bibliometric and visualized analysis

BACKGROUND

Diabetic Macular Edema (DME) significantly impairs vision in diabetics, with varied patient responses to current treatments like anti-vascular endothelial growth factor (VEGF) therapy underscoring the necessity for continued research into more effective strategies. This study aims to evaluate global research trends and identify emerging frontiers in DME to guide future research and clinical management.

METHODS

A qualitative and quantitative analysis of publications related to diabetic macular edema retrieved from the Web of Science Core Collection (WoSCC) between its inception and September 4, 2023, was conducted. Microsoft Excel, CiteSpace, VOSviewer, Bibliometrix Package, and Tableau were used for the bibliometric analysis and visualization. This encompasses an examination of the overall distribution of annual output, major countries, regions, institutions, authors, core journals, co-cited references, and keyword analyses.

RESULTS

Overall, 5624 publications were analyzed, indicating an increasing trend in DME research. The United States was identified as the leading country in DME research, with the highest h-index of 135 and 91,841 citations. Francesco Bandello emerged as the most prolific author with 97 publications. Neil M. Bressler has the highest h-index and highest total citation count of 46 and 9692, respectively. The journals "Retina - the Journal of Retinal and Vitreous Diseases" and "Ophthalmology" were highlighted as the most prominent in this field. "Retina" leads with 354 publications, a citation count of 11,872, and an h-index of 59. Meanwhile, "Ophthalmology" stands out with the highest overall citation count of 31,558 and the highest h-index of 90. The primary research focal points in diabetic macular edema included "prevalence and risk factors," "pathological mechanisms," "imaging modalities," "treatment strategies," and "clinical trials." Emerging research areas encompassed "deep learning and artificial intelligence," "novel treatment modalities," and "biomarkers."

CONCLUSION

Our bibliometric analysis delineates the leading role of the United States in DME research. We identified current research hotspots, including epidemiological studies, pathophysiological mechanisms, imaging advancements, and treatment innovations. Emerging trends, such as the integration of artificial intelligence and novel therapeutic approaches, highlight future directions. These insights underscore the importance of collaborative and interdisciplinary approaches in advancing DME research and clinical management.

Risk factors for diabetic retinopathy, diabetic macular edema, and sight-threatening diabetic retinopathy

OBJECTIVE

To identify the risk factors for the development of diabetic retinopathy (DR), diabetic macular edema (DME), and sight-threatening DR (STDR) based on a city-wide diabetes screening program.

RESEARCH DESIGN AND METHODS

Diabetic patients were prospectively recruited between June 2016 and December 2022. All patients underwent dilated fundus photography centered on the disc and macula or macular spectral domain optical coherence tomography (SD-OCT) scan. Complete medical history was documented. Systematic examination, blood analysis, and urinalysis were performed. Multivariate logistic regression analysis adjusting for age and sex was conducted.

RESULTS

Out of 7274 diabetic patients, 6840 had gradable images, among which 3054 (42.0%) were graded as DR, 1153 (15.9%) as DME, and 1500 (20.6%) as STDR. The factors associated with DR, DME, and STDR included younger age (odds ratio [OR]: 0.96, 0.97, and 0.96 respectively), lower BMI (OR: 0.97, 0.95, and 0.95 respectively), longer duration of diabetes (OR: 1.07, 1.03, and 1.05 respectively) and positive of urinary albumin (OR: 2.22, 2.56, and 2.88 respectively). Other associated factors included elevated blood urea nitrogen (OR: 1.22, 1.28, and 1.27 respectively), higher LDL-cholesterol, lower blood hemoglobin (OR: 0.98, 0.98, and 0.98), insulin intake, presence of diabetic foot pathologies and diabetic peripheral neuropathy. We also identified novel risk factors, including high serum potassium (OR: 1.37, 1.46, and 1.55 respectively), high-serum sodium (OR: 1.02, 1.02, and 1.04 respectively). Better family income was a protective factor for DR, DME, and STDR. Alcohol consumption once a week was also identified as a protective factor for DR.

CONCLUSIONS

Similar risk factors for DR, DME, and STDR were found in this study. Our data also indicates high serum sodium, high serum potassium, low blood hemoglobin, and level of family income as novel associated factors for DR, DME, and STDR, which can help with DR monitoring and management.

Recommendations for diabetic macular edema management by retina specialists and large language model-based artificial intelligence platforms

PURPOSE

To study the role of artificial intelligence (AI) in developing diabetic macular edema (DME) management recommendations by creating and comparing responses to clinicians in hypothetical AI-generated case scenarios. The study also examined whether its joint recommendations followed national DME management guidelines.

METHODS

The AI hypothetically generated 50 ocular case scenarios from 25 patients using keywords like age, gender, type, duration and control of diabetes, visual acuity, lens status, retinopathy stage, coexisting ocular and systemic co-morbidities, and DME-related retinal imaging findings. For DME and ocular co-morbidity management, we calculated inter-rater agreements (kappa analysis) separately for clinician responses, AI-platforms, and the "majority clinician response" (the maximum number of identical clinician responses) and "majority AI-platform" (the maximum number of identical AI responses). Treatment recommendations for various situations were compared to the Indian national guidelines.

RESULTS

For DME management, clinicians (ĸ=0.6), AI platforms (ĸ=0.58), and the 'majority clinician response' and 'majority AI response' (ĸ=0.69) had moderate to substantial inter-rate agreement. The study showed fair to substantial agreement for ocular co-morbidity management between clinicians (ĸ=0.8), AI platforms (ĸ=0.36), and the 'majority clinician response' and 'majority AI response' (ĸ=0.49). Many of the current study's recommendations and national clinical guidelines agreed and disagreed. When treating center-involving DME with very good visual acuity, lattice degeneration, renal disease, anaemia, and a recent history of cardiovascular disease, there were clear disagreements.

CONCLUSION

For the first time, this study recommends DME management using large language model-based generative AI. The study's findings could guide in revising the global DME management guidelines.

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.

Diabetic Macular Edema Optical Coherence Tomography Biomarkers Detected with EfficientNetV2B1 and ConvNeXt

(1) Background: Diabetes mellitus (DM) is a growing challenge, both for patients and physicians, in order to control the impact on health and prevent complications. Millions of patients with diabetes require medical attention, which generates problems regarding the limited time for screening but also addressability difficulties for consultation and management. As a result, screening programs for vision-threatening complications due to DM have to be more efficient in the future in order to cope with such a great healthcare burden. Diabetic macular edema (DME) is a severe complication of DM that can be prevented if it is timely screened with the help of optical coherence tomography (OCT) devices. Newly developing state-of-the-art artificial intelligence (AI) algorithms can assist physicians in analyzing large datasets and flag potential risks. By using AI algorithms in order to process OCT images of large populations, the screening capacity and speed can be increased so that patients can be timely treated. This quick response gives the physicians a chance to intervene and prevent disability. (2) Methods: This study evaluated ConvNeXt and EfficientNet architectures in correctly identifying DME patterns on real-life OCT images for screening purposes. (3) Results: Firstly, we obtained models that differentiate between diabetic retinopathy (DR) and healthy scans with an accuracy of 0.98. Secondly, we obtained a model that can indicate the presence of edema, detachment of the subfoveolar neurosensory retina, and hyperreflective foci (HF) without using pixel level annotation. Lastly, we analyzed the extent to which the pretrained weights on natural images "understand" OCT scans. (4) Conclusions: Pretrained networks such as ConvNeXt or EfficientNet correctly identify features relevant to the differentiation between healthy retinas and DR, even though they were pretrained on natural images. Another important aspect of our research is that the differentiation between biomarkers and their localization can be obtained even without pixel-level annotation. The "three biomarkers model" is able to identify obvious subfoveal neurosensory detachments, retinal edema, and hyperreflective foci, as well as very small subfoveal detachments. In conclusion, our study points out the possible usefulness of AI-assisted diagnosis of DME for lowering healthcare costs, increasing the quality of life of patients with diabetes, and reducing the waiting time until an appropriate ophthalmological consultation and treatment can be performed.