[Biomechanics of diabetic vitreopapillary traction syndrome]. / Biomekhanika diabeticheskogo vitreopapillyarnogo traktsionnogo sindroma.

Diabetic vitreopapillary traction syndrome (VPT) is a variant of diabetic retinopathy (DR) that can lead to vision loss in advanced stages. This review reports on the biomechanics of the vitreous in the pathogenesis of proliferative DR, in particular diabetic VPT. The article analyzes and summarizes literature data, presents the views of different authors on this problem, and provides the results of Russian and foreign scientific research on this pathology. It is concluded that further research in this area can lead to a significant improvement in the results of therapy, timely diagnosis, and preservation of vision in patients with DR.

The effect of adjuvant Mitomycin C during vitrectomy on functional and anatomical outcomes in patients with severe diabetic tractional retinal detachment.

PURPOSE: To evaluate the effect of adjuvant Mitomycin C (MMC) use on the anatomical and functional success of vitreoretinal surgery (VRS) in severe diabetic tractional retinal detachment (dTRD) patients. METHODS: A retrospective analysis of consecutive patients undergoing VRS due to severe dTRD was conducted. Patients were categorized into those who received 20 µg/0.1 mL MMC via MMC sandwich method (Group 1) and those who did not (Group 2). Demographics, surgical characteristics, visual outcomes, and complications that may related to MMC were analyzed. RESULTS: A total of 25 eyes were included, 13 in Group 1 and 12 in Group 2. No statistical difference was observed in baseline characteristics between the groups. The mean best-corrected visual acuity was 1.90 ± 0.43 logMAR and 1.93 ± 0.41 logMAR preoperatively and 1.60 ± 0.78 logMAR and 1.56 ± 0.78 logMAR postoperatively in Groups 1 and 2, respectively (p = 0.154). The postoperative mean intraocular pressure was 16.23 ± 2.55 mmHg and 13.08 ± 4.94 mmHg in Groups 1 and 2, respectively (p = 0.225). The rate of re-surgery was significantly lower in Group 1 (0% vs. 41.7% in Group 2, p = 0.015). Retina was attached in all patients at the last visit. No MMC-related complication was recorded. CONCLUSION: Intraoperative adjuvant MMC application for severe dTRD significantly reduces re-surgery rates with good anatomical and functional outcomes safely.

Comparison of the cytokines levels in aqueous humor in vitrectomized eyes versus non-vitrectomized eyes with diabetic macular edema.

BACKGROUND: This study was conducted to compare concentrations of VEGF family growth factors, inflammation-related factors, and adhesion molecules in the aqueous humor of eyes with diabetic macular edema (DME), with and without prior vitrectomy. METHODS: A total of 31 eyes were included, 11 with DME that had undergone vitrectomy, 9 with DME but without vitrectomy, and 11 from age-related cataract patients as controls. The concentrations of cytokines including TNF-α, IL-6, IL-8, IP-10, MCP-1, IFN-γ, MIP-1 α, MIP-1 ß, PECAM-1, MIF, VCAM-1, ICAM-1, PIGF were quantified using Luminex Human Discovery Assay. Central macular thickness (CMT) values of all eyes were measured using optical coherence tomography (OCT). RESULTS: (1) Vitrectomized DME eyes exhibited significantly higher levels of IL-6 and IL-8 compared to non-vitrectomized eyes (P < 0.05). (2) In vitrectomized group, after Benjamini-Hochberg correction, there was a significant positive correlation between the levels of VEGF and PlGF (rs = 0.855, P < 0.05), as well as the levels of TNF-α and IFN-γ (rs = 0.858, P < 0.05). In non-vitrectomized group, significant positive correlations were found between VEGF and PlGF levels after correcting for multiple comparisons (rs = 0.9, P < 0.05). (3) In non-vitrectomized group, the concentrations of VEGF and PlGF in aqueous humor were significantly positively correlated with CMT values (rs = 0.95, P < 0.05; rs = 0.9, P < 0.05, respectively). CONCLUSIONS: The concentrations of IL-6 and IL-8 in the aqueous humor were significantly higher in vitrectomized DME eyes compared to nonvitrectomized DME eyes and the levels of VEGF were similar in the two groups, suggesting that inflammation after vitrectomy may be a key factor in the occurrence and development of DME.

Clinical features, management, and outcomes of patients with sterile endophthalmitis associated with intravitreal bevacizumab injection: retrospective case series.

PURPOSE: To evaluate clinical features, treatment protocol, outcomes, and complications that developed in this case series of 24 patients who had consecutive sterile endophthalmitis after intravitreal bevacizumab (IVB) injection. METHODS: In this retrospective case series, IVB was repackaged in individual aliquots from the three batches that were used on the same day. IVB was injected into 26 eyes of 26 patients due to diabetic macular edema, age-related macular degeneration, and branch retinal vein occlusion. All patients had intraocular inflammation. Patients were divided into two groups severe and moderate inflammation according to the intraocular inflammation. The medical records of all patients were reviewed. At each follow-up visit, the complete ophthalmologic examination was performed, including best corrected visual acuity (BCVA), intraocular pressure, biomicroscopy, and posterior fundus examination. RESULTS: Twenty-four of 26 patients were included in the study. Two patients were excluded from this study since they didn't come to follow-up visits. The mean BCVA was 1.00 ± 0.52 Log MAR units before IVB. At the final visit, the BCVA was 1.04 ± 0.47 Log MAR units. These differences were not significant (p = 0.58). Of the 24 eyes, 16 eyes had severe, and 8 eyes had moderate intraocular inflammation. Eleven eyes in the severe inflammation group underwent pars plana vitrectomy due to intense vitreous opacity. Smear, culture results, and polymerase chain reaction results were negative. CONCLUSION: Sterile endophthalmitis may occur after IVB injection. Differential diagnosis of sterile endophthalmitis from infective endophthalmitis is crucial to adjust the appropriate treatment and prevent long-term complications due to unnecessary treatment.

Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.

BACKGROUND: Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with diabetes is increasing and telemedicine, mobile handheld devices and automated solutions may alleviate the burden for healthcare. We compared the performance of 21 artificial intelligence (AI) algorithms for referable DR screening in datasets taken by handheld Optomed Aurora fundus camera in a real-world setting. PATIENTS AND METHODS: Prospective study of 156 patients (312 eyes) attending DR screening and follow-up. Both papilla- and macula-centred 50° fundus images were taken from each eye. DR was graded by experienced ophthalmologists and 21 AI algorithms. RESULTS: Most eyes, 183 out of 312 (58.7%), had no DR and mild NPDR was noted in 21 (6.7%) of the eyes. Moderate NPDR was detected in 66 (21.2%) of the eyes, severe NPDR in 1 (0.3%), and PDR in 41 (13.1%) composing a group of 34.6% of eyes with referable DR. The AI algorithms achieved a mean agreement of 79.4% for referable DR, but the results varied from 49.4% to 92.3%. The mean sensitivity for referable DR was 77.5% (95% CI 69.1-85.8) and specificity 80.6% (95% CI 72.1-89.2). The rate for images ungradable by AI varied from 0% to 28.2% (mean 1.9%). Nineteen out of 21 (90.5%) AI algorithms resulted in grading for DR at least in 98% of the images. CONCLUSIONS: Fundus images captured with Optomed Aurora were suitable for DR screening. The performance of the AI algorithms varied considerably emphasizing the need for external validation of screening algorithms in real-world settings before their clinical application.

What is already known on this topic? Diabetic retinopathy (DR) is a common complication of diabetes. Efficient screening and timely treatment are important to avoid the development of sight-threatening DR. The increasing number of patients with diabetes and DR poses a challenge for healthcare.What this study adds? Telemedicine, mobile handheld devices and artificial intelligence (AI)-based automated algorithms are likely to alleviate the burden by improving efficacy of DR screening programs. Reliable algorithms of high quality exist despite the variability between the solutions.How this study might affect research, practice or policy? AI algorithms improve the efficacy of screening and might be implemented to clinical use after thorough validation in a real-life setting.

[Outcomes of bimanual vitreoretinal surgery in the treatment of diabetic retinopathy complications]. / Rezul'taty bimanual'noi vitreoretinal'noi khirurgii v lechenii oslozhnenii diabeticheskoi retinopatii.

The incidence of diabetic retinopathy (DR) requiring vitreorentinal surgery is increasing. The search for new effective and safe methods of treatment, the choice of the optimal time for surgery, and the assessment of long-term treatment outcomes are relevant problems. PURPOSE: This study evaluates the long-term results of vitreorentinal surgery using the bimanual technique in DR with different stages of fibrovascular proliferation. MATERIAL AND METHODS: The study included 135 patients (135 eyes) who were divided into groups depending on the predominant type of proliferation - vascular or fibrous. Patients underwent vitrectomy with membranectomy using the bimanual technique, with peripheral panretinal endolaser coagulation of the retina and tamponade of the vitreous cavity with balanced salt solution. The postoperative observation period lasted up to 12 months. RESULTS: Both groups showed statistically significant improvement in visual function and anatomical changes in central retinal thickness. A statistically significant improvement in best corrected visual acuity (BCVA) was found in patients with initially predominantly vascular proliferation. Correlation analysis showed that initially higher BCVA tends to persist in the postoperative period. A negative correlation was found between the final BCVA and the presence of type 2 diabetes mellitus, fibrous stage of proliferation, high central retinal thickness, and the presence of diabetic macular edema (DME) - both initially and after treatment. The frequency of complications in the groups was comparable, except for postoperative DME, which was more often detected in patients with fibrous proliferation. CONCLUSION: The bimanual technique of vitreorentinal surgery for complications of DR allows achieving high anatomical and functional results. Higher BCVA is noted in patients with the vascular stage of proliferation and initially high BCVA. The obtained data allow us to form a hypothesis about the possibility of earlier surgery in patients with high BCVA, but require further investigation.

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes.

PURPOSE: The duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR. METHODS: A total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi'an JiaoTong University were selected as the discovery set. One-to-one case-control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography-mass spectrometry (LC-MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry. RESULTS: The discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients. CONCLUSION: Disturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading.

Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR. Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR.

Association of the systemic immuno-inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio with diabetic microvascular complications.

Background: The systemic immuno-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are widely used and have been shown to be predictive indicators of various diseases. Diabetic nephropathy (DN), retinopathy (DR), and peripheral neuropathy (DPN) are the most prominent and common microvascular complications, which have seriously negative impacts on patients, families, and society. Exploring the associations with these three indicators and diabetic microvascular complications are the main purpose. Methods: There were 1058 individuals with type 2 diabetes mellitus (T2DM) in this retrospective cross-sectional study. SII, NLR, and PLR were calculated. The diseases were diagnosed by endocrinologists. Logistic regression and subgroup analysis were applied to evaluate the association between SII, NLP, and PLR and diabetic microvascular complications. Results: SII, NLR, and PLR were significantly associated with the risk of DN [odds ratios (ORs): 1.52, 1.71, and 1.60, respectively] and DR [ORs: 1.57, 1.79, and 1.55, respectively] by multivariate logistic regression. When NLR ≥2.66, the OR was significantly higher for the risk of DPN (OR: 1.985, 95% confidence interval: 1.29-3.05). Subgroup analysis showed no significant positive associations across different demographics and comorbidities, including sex, age, hypertension, HbA1c (glycated hemoglobin), and dyslipidemia. Conclusion: This study found a positive relationship between NLR and DN, DR, and DPN. In contrast, SII and PLR were found to be only associated with DN and DR. Therefore, for the diagnosis of diabetic microvascular complications, SII, NLR and PLR are highly valuable.

A new intelligent system based deep learning to detect DME and AMD in OCT images.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16) 2 , BCNN (VGG19) 2 , and BCNN (Inception_V3) 2 , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception_V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification.

Transition from an in-person to a telemedicine diabetic retinopathy screening program.

PURPOSE: Timely screening and treatment are essential for preventing diabetic retinopathy blindness. Improving screening workflows can reduce waiting times for specialist evaluation and thus enhance patient outcomes. This study assessed different screening approaches in a Brazilian public healthcare setting. METHODS: This retrospective study evaluated a telemedicine-based diabetic retinopathy screening implemented during the COVID-19 pandemic and compared it with in-person strategies. The evaluation was conducted from the perspective of a specialized referral center in an urban area of Central-West Brazil. In the telemedicine approach, a trained technician would capture retinal images by using a handheld camera. These images were sent to specialists for remote evaluation. Patient variables, including age, gender, duration of diabetes diagnosis, diabetes treatment, comorbidities, and waiting time, were analyzed and compared. RESULTS: In total, 437 patients with diabetes mellitus were included in the study (mean age: 62.5 ± 11.0 years, female: 61.7%, mean diabetes duration: 15.3 ± 9.7 years, insulin users: 67.8%). In the in-person assessment group, the average waiting time between primary care referral and specialist evaluation was 292.3 ± 213.9 days, and the referral rate was 73.29%. In the telemedicine group, the average waiting time was 158.8 ± 192.4 days, and the referral rate was 29.38%. The telemedicine approach significantly reduced the waiting time (p<0.001) and significantly lowered the referral rate (p<0.001). CONCLUSION: The telemedicine approach significantly reduced the waiting time for specialist evaluation in a real-world setting. Employing portable retinal cameras may address the burden of diabetic retinopathy, especially in resource-limited settings.

[Evaluation of the management and follow-up of diabetic patients in the prevention of diabetic retinopathy]. / Evaluación del manejo y seguimiento de los pacientes diabéticos en la prevención de la retinopatía diabética.

OBJECTIVE: Diabetes mellitus is a chronic disease with high morbidity and mortality, affecting 537 million adults worldwide. Spain is the second European country in prevalence, with 14.8% in the population aged twenty/seventy-nine years; with 11.6 cases per 1,000 people/year. Diabetic retinopathy (DR) is the fifth cause of vision loss worldwide and the seventh cause of blindness/visual impairment among members of the National Organization of the Blind in Spain (ONCE). Early detection of DR prevents blindness in diabetics and is conditioned by glycosylated hemoglobin. The aim of this paper was to analyze the management of diabetic patients in Aljarafe region (Seville) and identify opportunities for improvement in the coordination of their follow-up between the Primary Care physician and the ophthalmologist. METHODS: A retrospective observational study (2016-2019) was carried out, with patients registered in the diabetic census of the twenty-eight municipalities of Aljarafe. The primary care and hospital health history, and telemedicine program were consulted. About statistical analysis, for qualitative variables, totals and percentages were calculated; for quantitative variables, mean and standard deviation (if normally distributed) and median and quartiles (if non-normally distributed). RESULTS: There were 17,175 diabetics registered in Aljarafe (5.7% of the population); 14,440 patients (84.1%) had some determination of hemoglobin during the period, 9,228 (63.9%) had all of them in the appropriate range. Fundoscopic control was performed on 12,040 diabetics (70.1%), and of those who did not, 346 (10.6%) had all of them out of range. There were 1,878 (10.9%) patients without fundoscopic or metabolic control, 1,019 (54.3%) were women, 1,219 (64.9%) were under sixty-five years of age, 1,019 (54.3%) had severe comorbidity. CONCLUSIONS: Most patients have adequate screening, and more than half have determinations within range. However, a significant percentage with no glycated hemoglobin within range lack fundoscopic control, and another smaller group lack fundoscopic or metabolic control, with inter-municipal variability. We propose to improve communication channels between levels.

OBJECTIVE: La diabetes mellitus es una enfermedad crónica con alta morbimortalidad que afecta a 537 millones de adultos en el mundo. España es el segundo país europeo en prevalencia, con un 14,8% en población de veinte-setenta y nueve años, con 11,6 casos por cada 1.000 personas/año. La retinopatía diabética (RD) es la quinta causa de pérdida de visión a nivel mundial y la séptima causa de ceguera/discapacidad visual entre afiliados a la Organización Nacional de Ciegos de España (ONCE). La detección precoz de RD previene la ceguera en diabéticos y está condicionada por la hemoglobina glicosilada. El objetivo de este trabajo fue analizar el manejo de los pacientes diabéticos en la comarca del Aljarafe (Sevilla) e identificar oportunidades de mejora en la coordinación de su seguimiento entre el médico de Atención Primaria y el médico oftalmólogo. METHODS: Se realizó un estudio observacional retrospectivo (2016-2019) con los pacientes registrados en el censo de diabéticos de los veintiocho municipios del Aljarafe. Se consultó la historia de salud de Atención Primaria y Hospital, así como el programa de Telemedicina. En cuanto al análisis estadístico, para variables cualitativas se calcularon totales y porcentajes; para variables cuantitativas, media y distribución estándar (si distribución normal), y la mediana y cuartiles (distribución no normal). RESULTS: Se registraron 17.175 diabéticos en el Aljarafe (5,7% de población); 14.440 pacientes (84,1%) tenían alguna determinación de hemoglobina durante el periodo, 9.228 (63,9%) las tenían todas en rango adecuado. Tenían control fundoscópico 12.040 diabéticos (70,1%), y de los que no, 346 (10,6%) tenían todas fuera de rango. Hubo 1.878 (10,9%) pacientes sin control fundoscópico ni metabólico, 1.019 (54,3%) eran mujeres, 1.219 (64,9%) menores de sesenta y cinco años, 1.019 (54,3%) con comorbilidad grave. CONCLUSIONS: La mayoría de los pacientes presentan un cribado adecuado y, más de la mitad, determinaciones en rango. Sin embargo, un porcentaje relevante con ninguna hemoglobina glicosilada en rango carecen de control fundoscópico, y otro grupo menor está sin control fundoscópico ni metabólico, con variabilidad intermunicipios. Planteamos mejorar los circuitos de comunicación entre niveles.

Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning.

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors' output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR.

Management of diabetic ocular complications: from cellular insights to community strategies.

The editorial outlines an integrated approach to managing diabetic ocular complications, combining advanced scientific research with practical public health strategies to improve the prevention, diagnosis, and treatment of diabetic retinopathy and macular edema globally.

Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading.

BACKGROUND AND OBJECTIVE: Early detection and grading of Diabetic Retinopathy (DR) is essential to determine an adequate treatment and prevent severe vision loss. However, the manual analysis of fundus images is time consuming and DR screening programs are challenged by the availability of human graders. Current automatic approaches for DR grading attempt the joint detection of all signs at the same time. However, the classification can be optimized if red lesions and bright lesions are independently processed since the task gets divided and simplified. Furthermore, clinicians would greatly benefit from explainable artificial intelligence (XAI) to support the automatic model predictions, especially when the type of lesion is specified. As a novelty, we propose an end-to-end deep learning framework for automatic DR grading (5 severity degrees) based on separating the attention of the dark structures from the bright structures of the retina. As the main contribution, this approach allowed us to generate independent interpretable attention maps for red lesions, such as microaneurysms and hemorrhages, and bright lesions, such as hard exudates, while using image-level labels only. METHODS: Our approach is based on a novel attention mechanism which focuses separately on the dark and the bright structures of the retina by performing a previous image decomposition. This mechanism can be seen as a XAI approach which generates independent attention maps for red lesions and bright lesions. The framework includes an image quality assessment stage and deep learning-related techniques, such as data augmentation, transfer learning and fine-tuning. We used the architecture Xception as a feature extractor and the focal loss function to deal with data imbalance. RESULTS: The Kaggle DR detection dataset was used for method development and validation. The proposed approach achieved 83.7 % accuracy and a Quadratic Weighted Kappa of 0.78 to classify DR among 5 severity degrees, which outperforms several state-of-the-art approaches. Nevertheless, the main result of this work is the generated attention maps, which reveal the pathological regions on the image distinguishing the red lesions and the bright lesions. These maps provide explainability to the model predictions. CONCLUSIONS: Our results suggest that our framework is effective to automatically grade DR. The separate attention approach has proven useful for optimizing the classification. On top of that, the obtained attention maps facilitate visual interpretation for clinicians. Therefore, the proposed method could be a diagnostic aid for the early detection and grading of DR.

A case report: co-occurrence of probable Vogt-Koyanagi-Harada disease and diabetic retinopathy.

BACKGROUND: Bilateral retinal detachment and choroidal detachment in a patient are rare occurrences. The presence of bilateral diabetic retinopathy (DR) in such a case is even rarer and complicates the condition. CASE PRESENTATION: In this study, we document a case of unconventional VKH. Manifestations in this patient included intense peripheral retinal detachment and choroidal detachment, along with vitreous opacities akin to cotton wool spots, concurrent with DR. The diagnosis was considered as probable VKH with DR. Treatment according to VKH protocols, including high-dose corticosteroids, yielded positive results. CONCLUSIONS: VKH can co-occurrence with DR. VKH manifestations vary, and early, aggressive, and long-term treatment is essential. The complexity of treatment increases with concurrent DR, necessitating the use of immunosuppressants.

Case series: Superficial plexus en face may aid distinction of retinal nerve fiber layer loss from diabetic retinal ischemia versus glaucoma.

PURPOSE: This study aimed to demonstrate that the pattern and degree of capillary bed dropout in early glaucoma appear different on OCT-A superficial plexus en-face slabs compared with retinal ischemia. RNFL loss associated with retinal ischemia in diabetic patients may be explained and accounted for by overlying the RNFL deviation map on a superficial plexus en-face montage. CASE REPORTS: Three middle-aged White men with diabetes mellitus showed cup-to-disc ratios of approximately 0.7 and RNFL and ganglion thinning. Each patient had several Cirrus OCT and OCT-A scans taken of the posterior pole. The OCT-A en-face images demonstrated specific patterns of superficial capillary dropout. The appearance of superficial plexus capillary dropout in one case of glaucoma is contrasted against two cases of retinal ischemia. CONCLUSIONS: Early glaucoma appears to be associated with incomplete capillary bed dropout that extends from macular regions to the disc in a wedge- or arc-shaped pattern. Diabetic retinal ischemia appears to be associated with well-defined patchy and polygonal pockets of complete capillary bed obliteration that may not extend back to the disc. If an RNFL deviation map is superimposed over the superficial plexus en-face montage, areas of RNFL loss may correlate with and thus be well accounted for by areas of retinal ischemia in cases with RNFL thinning likely from ischemia. This approach may supplement inspection of OCT B-scans for focal retinal thinning when trying to differentiate RNFL and ganglion cell loss from retinal ischemia versus glaucoma in patients with diabetes. Formal research studies are needed to validate our observations and proposed use of OCT-A together with OCT in these patients.

Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.