Diagnosis Of Retinopathy Research Articles

Revolutionising Diabetic Retinopathy Diagnosis with Modified Regularisation Long Short-Term Memory Framework

The diagnosis of Diabetic Retinopathy (DR) demands a paradigm shift towards more accurate and efficient solutions to overcome vision impairment. Therefore, the current study introduces a new Modified Regularisation Long Short-term Memory (MR-LSTM) framework approach for DR diagnosis. The proposed framework leverages the power of deep learning and provides a dynamic and robust solution for the early detection of DR, which in turn preserves a patient’s vision. The proposed framework uses a DR Debrecen Dataset from the UCI database with 21 distinct features relevant to retinal health, and employs a series of data preprocessing steps, including data cleaning, normalisation, and transformation, to ensure data quality and compatibility. The MR-LSTM framework excels at capturing temporal dependencies in sequential retinal images, offering a unique advantage in understanding the progression of DR. The MR-LSTM framework is implemented using Python libraries, and the results are compared with those of other popular models. It is observed that the MR-LSTM framework outperforms other models and achieves an accuracy of 97.12 percent and an F1 Score of 98.49. Furthermore, the Receiver Operating Characteristic (ROC) curve reveals an area under the curve of 0.97, highlighting the exceptional ability to discriminate between positive and negative cases of the proposed framework. By revolutionising DR diagnosis with the proposed MR-LSTM framework, it can achieve accurate, timely, and accessible solutions in the fight against vision-threatening conditions.

Association of Neighborhood Socioeconomic Disadvantage with Proliferative Diabetic Retinopathy

To evaluate the association between neighborhood socioeconomic deprivation, distance from ophthalmology clinics, and diagnosis of proliferative diabetic retinopathy (PDR). Retrospective cohort study. Adult patients (≥18 years) with diabetes mellitus at Johns Hopkins Hospital and University of Wisconsin-Madison. Patient addresses were geocoded and the block group was linked to the 2021 national Area Deprivation Index (ADI). ADI was divided into quartiles, with higher quartiles indicating greater socioeconomic disadvantage. The distance between patient's residence and ophthalmology clinics was calculated. Multivariable logistic regression models were used to analyze the association between ADI quartile, distance from clinic, and PDR, adjusted for demographics and insurance status. The interaction between ADI and distance was tested. Diagnosis of PDR. 73,618 patients were included. A significant interaction was observed between ADI quartile and distance from ophthalmology clinics (P < .001). Among patients residing within 8 miles of clinics, those in higher ADI quartiles had increased odds of PDR compared to those in ADI Q1 (Q2: OR 1.36, 95% CI 1.12-1.65; Q3: OR 1.79, 95% CI 1.46-2.19; Q4: OR 2.60, 95% CI 2.09-3.25; P<.001 for trend). Conversely, for patients living more than 8 miles from clinics, the odds of PDR were similar across ADI quartiles (Q1: OR 0.85, 95% CI 0.73-0.98; Q2: OR 1.02, 95% CI 0.87-1.12; Q3: OR 1.08, 95% CI 0.90-1.30). However, patients in all ADI quartiles more than 8 miles had greater odds of PDR compared to those in the same ADI quartile within 8 miles (OR 3.15, 95% CI 2.61-3.80, OR 1.97, 95% CI 1.71-2.27, OR 1.79, 95% CI 1.51-2.12, and OR 1.31, 95% CI 1.07-1.61 in ADI Q1 to Q4 respectively). Patients living in neighborhoods with greater socioeconomic disadvantage and further from ophthalmology clinics have greater odds of PDR. These findings suggest the potential utility of targeted interventions in socioeconomically deprived and distant areas to reduce PDR-related blindness.

Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy

The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a crucial role in DR, yet there are currently no reports of machine learning (ML) algorithms being utilized for the development of immune-related molecular markers in DR. Based on the datasets GSE102485 and GSE160306, differentially expressed genes (DEGs) were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Five ML algorithms including Bayesian, Learning Vector Quantization (LVQ), Wrapper (Boruta), Random Forest (RF), and Logistic Regression were employed to select immune-related genes associated with DR (DR.Sig). Seven ML algorithms including Naive Bayes (NB), RF, Support Vector Machine (SVM), AdaBoost Classification Trees (AdaBoost), Boosted Logistic Regressions (LogitBoost), K-Nearest Neighbors (KNN), and Cancerclass were utilized to construct a predictive model for DR. The relationship between DR.Sig genes and immune cells was analyzed using single-sample Gene Set Enrichment Analysis (ssGSEA). Additionally, drug sensitivity prediction of DR.Sig genes and molecular docking were performed. Through the utilization of 5 ML algorithms, 6 immune-related biomarkers closely related to the occurrence of DR were identified, including FCGR2B, CSRP1, EDNRA, SDC2, TEK, and CIITA. The DR predictive model constructed based on these 6 DR.Sig genes using the Cancerclass algorithm demonstrated superior predictive performance compared to 4 previously published DR-related biomarkers. In vivo and in vitro experiments also provided strong validation of the expression of the 6 genes in DR. Positive correlations were observed between these genes and 22 types of immune cells. Molecular docking results revealed that CSRP1, EDNRA, and TEK exhibited the highest affinities with the small molecule compounds etoposide, FR-139317, and camptothecin, respectively. The models constructed based on various ML algorithms can effectively predict the occurrence of DR events and hold potential for targeted drug therapies, providing a basis for the early diagnosis and targeted treatment of DR.

Predicting vision-threatening diabetic retinopathy in patients with type 2 diabetes mellitus: Systematic review, meta-analysis, and prospective validation study.

Delayed diagnosis and treatment of vision-threatening diabetic retinopathy (VTDR) is a common cause of visual impairment in individuals with type 2 diabetes mellitus (T2DM). Identification of VTDR predictors is the key to early prevention and intervention, but the predictors from previous studies are inconsistent. This study aims to conduct a systematic review and meta-analysis of the existing evidence for VTDR predictors, then to develop a risk prediction model after quantitatively summarising the predictors across studies, and finally to validate the model with two Chinese cohorts. We systematically retrieved cohort studies that reported predictors of VTDR in T2DM patients from PubMed, Ovid, Embase, Scopus, Cochrane Library, Web of Science, and ProQuest from their inception to December 2023. We extracted predictors reported in two or more studies and combined their corresponding relative risk (RRs) using meta-analysis to obtain pooled RRs. We only selected predictors with statistically significant pooled RRs to develop the prediction model. We also prospectively collected two Chinese cohorts of T2DM patients as the validation set and assessed the discrimination and calibration performance of the prediction model by the time-dependent ROC curve and calibration curve. Twenty-one cohort studies involving 622 490 patients with T2DM and 57 107 patients with VTDR were included in the meta-analysis. Age of diabetes onset, duration of diabetes, glycosylated haemoglobin (HbA1c), estimated glomerular filtration rate (eGFR), hypertension, high albuminuria and diabetic treatment were used to construct the prediction model. We validated the model externally in a prospective multicentre cohort of 555 patients with a median follow-up of 52 months (interquartile range = 39-77). The area under the curve (AUC) of the prediction model was all above 0.8 for 3- to 10-year follow-up periods and different cut-off value of each year provided the optimal balance between sensitivity and specificity. The data points of the calibration curves for each year closely surround the corresponding dashed line. The risk prediction model of VTDR has high discrimination and calibration performance based on validation cohorts. Given its demonstrated effectiveness, there is significant potential to expand the utilisation of this model within clinical settings to enhance the detection and management of individuals at high risk of VTDR.

Sparse-View Photoacoustic Reconstruction Method for Diabetic Retinopathy Using Feature Fusion Network.

Diabetic retinopathy is one of the most prevalent microvascular complications of diabetes mellitus, and photoacoustic imaging is an effective method for imaging diabetic retinal vessels. Photoacoustic imaging is an emerging noninvasive imaging method based on the photoacoustic effect, which offers advantages of contrast, resolution, and depth imaging. Appropriate photoacoustic reconstruction methods are essential for obtaining high-quality photoacoustic images. In this study, a multi-input self-attention multiscale feature fusion network (SAMF-Net) is proposed for photoacoustic reconstruction. The algorithm accepts two inputs, namely the original photoacoustic signal and the traditional reconstructed image. Furthermore, a global feature extraction module based on the self-attention mechanism is employed to focus on the global information. The results demonstrate that the proposed method exhibits superior reconstruction capability under different sparse detection views. The method has instructive value for photoacoustic image reconstruction and has the potential for further application in the diagnosis of diabetic retinopathy.

8156 Exploring Gender-Specific Characteristics Of Diabetes Among Young Adults In Korea: The Korea National Health And Nutrition Examination Survey 2011-2012 And 2019-2020

Abstract Disclosure: C. Chung: None. Background: The escalating global incidence of diabetes among young adults could be linked to dynamic socio-environmental changes including pandemic and climate shifts. In this investigation, we aimed to discern the distinct features of diabetes among young adults in Korea. Methods: This cross-sectional study included 63,489,047 Korean young adults (32,173,248 male, 31,315,799 female) aged 20-39 years who participated in the Korean National Health and Nutrition Examination Survey 2011-2012 and 2020-2021. Diabetes cases were identified through hospital diagnoses or the prescription of insulin or non-insulinemic anti-diabetic medications. The significant insulin resistance was defined as HOMA-IR above 2.9. Complex sampling design and cross-tabulation analyses were used to examine both socioeconomic and laboratory factors. Results: The prevalence of diabetes was higher in men in both 2011-2012 and 2020-2021 (Men vs. Women, 2011-2012: 2.2 ± 0.5 vs. 1.2 ± 0.3 %, p&amp;lt;0.001; 2020-2021: 3.0 ± 0.5 vs. 1.6 ± 0.3 %, p&amp;lt;0.001). The use of insulin and oral hypoglycemic agent (OHA) was comparable between men and women in 2011-2012 (Insulin: 18.1 ± 8.2 vs. 5.2 ± 5.1 %, p=0.305; OHA, 18.2 ± 6.3 vs. 19.5 ± 6.5 %, p=0.239), but more women used insulin and more men used OHA in 2020-2021 (Insulin: 5.3 ± 3.7 vs. 28.7 ± 9.4 %, p=0.016; OHA: 31.4 ± 7.7 vs. 19.6 ± 7.3 %, p=0.009). Execution of fundus exam (25.7 ± 10.6 vs. 68.5 ± 14.1 %, p=0.021) and diagnosis of diabetic retinopathy (0 vs. 8.9 ± 3.3 %, p=0.001) was higher in women in 2020-2021. HbA1c was higher in men in 2020-2021 (8.1 ± 0.4 vs. 7.3 ± 0.3 %, p &amp;lt;0.001), but was similar between men and women in 2011-2012 (8.4 ± 0.6 vs. 8.1 ± 0.6 %, p=0.697). Obesity, one of the most common comorbidities of diabetes, was higher in women in 2011-2012 (24.8 ± 4.8 vs. 79.4 ± 9.3 %, p&amp;lt;0.001), but was higher in men in 2020-2021 (94.4 ± 2.9 vs. 67.8 ± 11.0 %, p&amp;lt;0.001). The factors associated with increase in HOMA-IR in 2020-2021 was different between men and women. Unexpectedly, men with low mental stress showed positive correlation with HOMA-IR (B = 3.9, 95% CI 0.1-7.7, p=0.047). For women, mental stress did not affect HOMA-IR, but those with regular anaerobic exercise (B = -10.5, 95% CI -18.3 - -2.6, p=0.011) and stable employment status (B = -11.9, 95% CI -19.4 - -4.8, p=0.002) presented negative correlation with HOMA-IR. Conclusion: In comparison to before the COVID-19 pandemic, men diabetic patients showed more obese tendency after COVID-19, and there was also a statistically significant difference in HbA1c disparity with women. The higher prevalence of diabetic retinopathy despite of lower HbA1c in women during pandemic era needs to be further investigated in well-designed cohort study. Presentation: 6/3/2024

Prevalence and spectrum of manifestations of tamoxifen retinopathy as assessed by multimodal and ultra-widefield retinal imaging

Background/ObjectivesTamoxifen has well-known retinal toxicity, but the epidemiology of this toxicity is poorly defined. This study aims to 1) evaluate the prevalence of tamoxifen retinopathy in a major northeastern metropolitan area based on multimodal retinal imaging and 2) determine whether the additional peripheral retina captured in ultra-widefield (UWF) imaging aids in the diagnosis of tamoxifen retinopathy. MethodsThis retrospective cohort study examined female patients initiated on tamoxifen for at least 12 months prior to their first retinal exam. Two independent graders performed blinded review of spectral-domain optical coherence tomography (OCT) images for evidence of macular toxicity and UWF images for signs of central and peripheral toxicity. A one-tailed two-proportion Z-test determined whether peripheral pigmentary changes were more prevalent in the tamoxifen cohort than age- and gender-matched controls. Results241 eyes from 123 patients were included in the tamoxifen cohort, and 258 eyes from 132 patients were included in the control cohort. Two patients demonstrated definitive tamoxifen retinopathy, for a prevalence of 1.6%. One patient demonstrated crystalline maculopathy on OCT and UWF imaging, while another demonstrated pseudocystic cavitations on OCT imaging. Neither patient had peripheral findings on UWF imaging. Peripheral pigmentary changes were not significantly more prevalent in the tamoxifen cohort. ConclusionsThese findings suggest that UWF imaging does not contribute to diagnosis of tamoxifen retinopathy, and OCT may be the most valuable tool in diagnosing tamoxifen retinopathy. Our prevalence (1.6%) diverges from higher rates in studies screening with similarly advanced retinal imaging. Further large population studies are needed.

Looking into Concept Explanation Methods for Diabetic Retinopathy Classification

Diabetic retinopathy is a common complication of diabetes, and monitoring the progression of retinal abnormalities using fundus imaging is crucial. Because the images must be interpreted by a medical expert, it is infeasible to screen all individuals with diabetes for diabetic retinopathy. Deep learning has shown impressive results for automatic analysis and grading of fundus images. One drawback is, however, the lack of interpretability, which hampers the implementation of such systems in the clinic. Explainable artificial intelligence methods can be applied to explain the deep neural networks. Explanations based on concepts have shown to be intuitive for humans to understand, but have not yet been explored in detail for diabetic retinopathy grading. This work investigates and compares two concept-based explanation techniques for explaining deep neural networks developed for automatic diagnosis of diabetic retinopathy: Quantitative Testing with Concept Activation Vectors and Concept Bottleneck Models. We found that both methods have strengths and weaknesses, and choice of method should take the available data and the end user’s preferences into account. Our code is available at &lt;a href='https://github.com/AndreaStoraas/ConceptExplanations_DR_grading'&gt;https://github.com/AndreaStoraas/ConceptExplanations_DR_grading&lt;/a&gt;

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.

The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on glucose series data from the wearable continuous glucose monitoring system. Therefore, this study developed a novel method, i.e., double deep latent autoencoder, for exploring glycemic variability influence from multi-day glucose data for diabetic retinopathy. Specifically, the model proposed in this research could encode continuous glucose sensor data with non-continuous and variable length via the integration of a data reorganization module and a novel encoding module with fragmented-missing-wise objective function. Additionally, the model implements a double deep autoencoder, which integrated convolutional neural network, long short-term memory, to jointly capturing the inter-day and intra-day glucose latent features from glucose series. The effectiveness of the proposed model is evaluated through a cross-validation method to clinical datasets of 765 type 2 diabetes patients. The proposed method achieves the highest accuracy value (0.89), precision value (0.88), and F1 score (0.73). The results suggest that our model can be used to remotely diagnose and screen for diabetic retinopathy by learning potential features of glucose series data collected by wearable continuous glucose monitoring systems.

A wavelet CNN with appropriate feed-allocation and PSO optimized activations for diabetic retinopathy grading

This work modifies the architecture of conventional CNN with the integration of Multi-resolution Analysis (MRA) in a CNN framework for Diabetic Retinopathy (DR) diagnosis and grading. Here, the HF sub-bands are subjected to optimized activations and are directly fed to the fully connected layers, as it encompasses edge features. Unlike FD-Relu, the proposed function preserves significant negative coefficients, compared to the S-Relu, the proposed third-order S-Relu is optimized such that it sustains the activations in the range suitable for the wavelet coefficients. The coefficients of higher-order terms of the proposed 3rd-order S-Relu are optimized with PSO, fitting the maximum energy of the wavelet sub-bands to ensure High Frequency (HF) edge preservation. The authors re-architecture 3 different CNNs published in the Retinal Image analysis field, with spatial and wavelet inputs with optimized activations. The highest accuracy of 96% is attained with the AlexNet re-architecture, with 35,126 fundus images secured from the Kaggle dataset. As we can infer the proposed re-architecture wavelet CNN outperformed the multiscale shallow CNNs, multiscale attention net, and stacked CNNs with a 6.6, 0.3, 0.7 per cent increase in accuracy. The entire implementation of the wavelet CNN is made available under source code.

Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability. This review describes currently available AI and imaging systems, how a stable telemedicine infrastructure is crucial to AI implementation, and how successful ROP programs have been run in both low and middle-income countries and high-income countries. More work is needed in terms of validating AI systems with different populations with various low-cost imaging devices that have recently been developed. A sustainable and cost-effective ROP screening program is crucial in the prevention of childhood blindness.

Diabetic Retinopathy Diagnosis based on Convolutional Neural Network in the Russian Population: A Multicenter Prospective Study.

Diabetic retinopathy is the most common complication of diabetes mellitus and is one of the leading causes of vision impairment globally, which is also relevant for the Russian Federation. To evaluate the diagnostic efficiency of a convolutional neural network trained for the detection of diabetic retinopathy and estimation of its severity in fundus images of the Russian population. In this cross-sectional multicenter study, the training data set was obtained from an open source and relabeled by a group of independent retina specialists; the sample size was 60,000 eyes. The test sample was recruited prospectively, 1186 fundus photographs of 593 patients were collected. The reference standard was the result of independent grading of the diabetic retinopathy stage by ophthalmologists. Sensitivity and specificity were 95.0% (95% CI; 90.8-96.4) and 96.8% (95% CI; 95.5- 99.0), respectively; positive predictive value - 98.8% (95% CI; 97.6-99.2); negative predictive value - 87.1% (95% CI, 83.4-96.5); accuracy - 95.9% (95% CI; 93.3-97.1); Kappa score - 0.887 (95% CI; 0.839-0.946); F1score - 0.909 (95% CI; 0.870-0.957); area under the ROC-curve - 95.9% (95% CI; 93.3-97.1). There was no statistically significant difference in diagnostic accuracy between the group with isolated diabetic retinopathy and those with hypertensive retinopathy as a concomitant diagnosis. The method for diagnosing DR presented in this article has shown its high accuracy, which is consistent with the existing world analogues, however, this method should prove its clinical efficiency in large multicenter multinational controlled randomized studies, in which the reference diagnostic method would be unified and less subjective than an ophthalmologist.

Prevalence and Risk Factors of Diabetic Retinopathy among Type 2 Diabetic Patients in Tertiary Care Hospital of Gandaki Province of Nepal

Background Diabetic retinopathy (DR) is one of the commonest causes of blindness in Nepal. This study aimed to determine the prevalence and risk factors of diabetic retinopathy in type 2 diabetic patients in a tertiary care hospital of Gandaki Province of Nepal. Methods This was a hospital based cross-sectional study conducted among 162 patients of type 2 diabetes aged 30 years and above. Standard proforma was used to collect socio-demographic and clinical variables of the patients. Detailed eye examination including fundus evaluation under mydriasis was done on all patients and diabetic retinopathy was classified according to Early Treatment of Diabetic Retinopathy Study Classification. Results The mean ±SD of age of the study sample was 61.99 ±11.69 years. The prevalence of diabetic retinopathy was 41.98%. Among the patients with diabetic retinopathy, the prevalence of mild NPDR, moderate NPDR, severe NPDR, very severe NPDR and PDR were 51.47%, 25%, 11.76%, 1.47%, and 10.29% respectively. There was statistically significant association of diabetic retinopathy with duration of diabetes and chronic kidney disease. However, there was no statistically significant association of diabetic retinopathy with gender, family history, hypertension, history of smoking, history of alcohol, treatment history and physical activity. Conclusions There is high prevalence of diabetic retinopathy among the diabetic patients. Longer duration of diabetes and chronic kidney disease were the significant risk factors for diabetic retinopathy. Early diagnosis and treatment of diabetic retinopathy is essential to reduce blindness in diabetic patients.

Artificial Intelligence in Detecting the Severity of Diabetic Retinopathy

BACKGROUND Diabetic retinopathy (DR) is the leading cause of visual impairment and blindness among individuals with diabetes mellitus. Early detection and timely intervention are crucial for preventing irreversible vision loss. However, traditional methods of DR screening are labor-intensive and reliant on the availability of skilled personnel, posing challenges in resource-constrained settings. Objective This study aims to evaluate the effectiveness of artificial intelligence (AI) in detecting the severity of DR compared to conventional ophthalmological assessments. METHODS A hospital-based observational study was conducted at the ophthalmology outpatient department of Tertiary care hospital, India over a six-month period. A total of 300 diabetic patients were included, and fundus photographs were obtained using a fundus camera. The images were then analyzed using an AI model trained on a diabetic retinopathy dataset. The severity of DR was graded according to established criteria, and the accuracy of the AI model was compared to that of ophthalmologist grading. RESULTS The AI model demonstrated an accuracy rate of 95.25% in grading the severity of DR. Comparison between AI and ophthalmologist grading showed close sensitivity and specificity rates across different DR grades, with the AI model slightly outperforming in certain categories. CONCLUSIONS Artificial intelligence shows promise as an effective and efficient tool for the screening and diagnosis of diabetic retinopathy. Its integration into healthcare systems could enhance early detection and treatment of DR, particularly in underserved regions with limited access to ophthalmological services. Further research and validation are warranted to optimize the use of AI in diabetic eye care and ensure its equitable distribution and ethical use.

Retraction Note: Early diagnosis of diabetic retinopathy using unsupervised learning

Retraction Note: Early diagnosis of diabetic retinopathy using unsupervised learning

Population-Based Artificial Intelligence Assessment of Relationship Between the Risk Factors for Diabetic Retinopathy in Indian Population

ABSTRACT Purpose Risk factors (RFs), like ‘body mass index (BMI),’ ‘age,’ and ‘gender’ correlate with Diabetic Retinopathy (DR) diagnosis and have been widely studied. This study examines how these three secondary RFs independently affect the predictive capacity of primary RFs. Methods The dataset consisted of four population-based studies on the prevalence of DR and associated RFs in India between 2001 and 2010. An Autoencoder was employed to categorize RFs as primary or secondary. This study evaluated six primary RFs coupled independently with each secondary RF on five machine-learning models. Results The secondary RF ‘gender’ gave a maximum increase in Area under the curve (AUC) score to predict DR when combined separately with ‘insulin treatment,’ ‘fasting plasma glucose,’ ‘hypertension history,’ and ‘glycosylated hemoglobin’ with a maximum increase in AUC for the Naive Bayes model from 0.573 to 0.646, for the Support Vector Machines (SVM) model from 0.644 to 0.691, for the SVM model from 0.487 to 0.607, and for the Decision Tree model from 0.8 to 0.848, respectively. The secondary RFs ‘age’ and ‘BMI’ gave a maximum increase in AUC score to predict DR when combined separately with ‘diabetes mellitus duration’ and ‘systolic blood pressure,’ with a maximum increase in AUC for the SVM model from 0.389 to 0.621, and for the Decision Tree model from 0.617 to 0.713, respectively. Conclusion The risk factor ‘gender’ was the best secondary RF in predicting DR compared to ‘age’ and ‘BMI,’ increasing the predictive power of four primary RFs.

THE EFFECT OF SARCOPENIC OBESITY AND VISUAL IMPAIRMENT ON BASIC FUNCTIONAL ACTIVITY

Age-associated cataracts and diabetic retinopathy in combination with sarcopenic obesity, formed mainly against the background of physical inactivity due to vision deficiency, potentially contribute to a decrease in patients' activity in daily life, but the latter has not been studied practically by special tests taking into account visual impairment. Objective. Assess the impact of diabetic retinopathy and cataracts on the daily activities of elderly patients with sarcopenic obesity. Materials and methods. The study involved 128 patients aged 60–74 years with cataracts and sarcopenic obesity and 125 patients of the same age with diabetic retinopathy and sarcopenic obesity. All patients underwent a comprehensive clinical and instrumental ophthalmological examination. The appropriate criteria presented in the clinical guidelines were used in the diagnosis of cataracts and diabetic retinopathy. Sarcopenia was determined by the European Working Group on Sarcopenia in older people scale, supplemented by carpal dynamometry, and obesity was determined by body mass index. Results. A more significant effect of cataracts combined with sarcopenic obesity, causing complete dependence on others (12,18 scores) in daily life activities, was found than the effect of diabetic retinopathy combined with sarcopenic obesity, causing moderate dependence (10,18 scores) (p&lt;0.01). Patients of the compared groups also have statistically significant differences in all types of activities in everyday life, including leading ones – threading a needle and cutting nails. In particular, restrictions on threading into a needle in patients with cataracts and sarcopenic obesity have 1,84±0,03 scores, which is significantly higher than in patients with diabetic retinopathy and sarcopenic obesity (p 0,01). A similar pattern has been established for nail clipping, the limits for which are 1,75±0,03 and 1,43±0,04 scores, respectively. However, the following ranking places of the considered limitations of activity in everyday life differ statistically significantly, and in the third position in patients with cataracts and sarcopenic obesity is self–writing (1,68±0,04 scores), and in patients with diabetic retinopathy and sarcopenic obesity – reading the text (1,38±0,03 scores) (p&lt;0,01). The fourth rank also differs when in patients with cataracts and sarcopenic obesity it corresponds to reading the text (1,53±0,04 scores), and in patients with diabetic retinopathy and sarcopenic obesity – writing the text (1,32±0,02 scores) (p&lt;0,01). Factor analysis of the effect of cataracts combined with sarcopenic obesity and diabetic retinopathy combined with sarcopenic obesity showed the greatest factor load in both groups on the formation of dependence on others when threading a needle (0,829 and 0,572, respectively) and when performing nail clipping (0,807 and 0,469, respectively), however, the effect of cataracts and sarcopenic obesity on The development of limitations of these types of activities in everyday life is more significant than diabetic retinopathy and sarcopenic obesity. Conclusion. Among the considered age-associated ophthalmic diseases in the elderly, cataracts combined with sarcopenic obesity significantly limit the activity of patients in everyday life.

Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population

BackgroundIn healthcare systems in general, access to diabetic retinopathy (DR) screening is limited. Artificial intelligence has the potential to increase care delivery. Therefore, we trained and evaluated the diagnostic accuracy of a machine learning algorithm for automated detection of DR.MethodsWe included color fundus photographs from individuals from 4 databases (primary and specialized care settings), excluding uninterpretable images. The datasets consist of images from Brazilian patients, which differs from previous work. This modification allows for a more tailored application of the model to Brazilian patients, ensuring that the nuances and characteristics of this specific population are adequately captured. The sample was fractionated in training (70%) and testing (30%) samples. A convolutional neural network was trained for image classification. The reference test was the combined decision from three ophthalmologists. The sensitivity, specificity, and area under the ROC curve of the algorithm for detecting referable DR (moderate non-proliferative DR; severe non-proliferative DR; proliferative DR and/or clinically significant macular edema) were estimated.ResultsA total of 15,816 images (4590 patients) were included. The overall prevalence of any degree of DR was 26.5%. Compared with human evaluators (manual method of diagnosing DR performed by an ophthalmologist), the deep learning algorithm achieved an area under the ROC curve of 0.98 (95% CI 0.97–0.98), with a specificity of 94.6% (95% CI 93.8–95.3) and a sensitivity of 93.5% (95% CI 92.2–94.9) at the point of greatest efficiency to detect referable DR.ConclusionsA large database showed that this deep learning algorithm was accurate in detecting referable DR. This finding aids to universal healthcare systems like Brazil, optimizing screening processes and can serve as a tool for improving DR screening, making it more agile and expanding care access.

Combining functional and morphological retinal vascular characteristics achieves high-precision diagnosis of mild non-proliferative diabetic retinopathy

BackgroundTo explore the functional and morphological variations of retinal vessels in diabetes with no clinically detectable retinopathy (NDR) and mild non-proliferative diabetic retinopathy (NPDR) and to establish a high-performance mild NPDR diagnostic model.MethodsNormal subjects and type 2 diabetes patients with NDR and mild NPDR were recruited. Oxygen-saturation-related functional parameter (optical density ratio ODR) and morphological characteristics (fractal dimension Df, vessel area rate VAR, mean vascular diameter Dm, vessel tortuosity τ) of different vascular areas were extracted with single fundus photography and comprehensively analyzed among groups. An interpretable model combining marine predator algorithm (MPA) and support vector machine (SVM) based on characteristic selection was proposed for mild NPDR diagnosis.ResultsA total of 91 NDR subjects, 75 mild NPDR subjects, and 111 sex- and age-matched normal controls were analyzed. Increased main vessels ODR, while lower VAR of all areas except outer ring macula, lower Dm of all vessels and decreased τ of all areas were associate with NDR (e.g. main vessels ODR: OR [95%CI] 1.42[1.07–1.89], full macula τ:0.53[0.38–0.74]). Increased ODR of all areas, higher Dm of all areas except inner ring macula, increased inner ring macula τ, while decreased Df of full and inner ring macula, lower VAR of all areas were associate with mild NPDR (e.g. main vessels ODR:5.68[3.03–10.65], inner ring macula VAR: 0.48[0.33–0.69]). The MPA-SVM model with selected characteristics obtained the best diagnosis performance (AUC:0.940 ± 0.014; Accuracy:90.4 ± 3.9%; Sensitivity:89.2 ± 6.4%; Specificity:91.3 ± 6.4%).ConclusionsMore significant retinal vascular variations are associate with the incidence of mild NPDR than NDR. High-precision mild NPDR diagnosis is achieved combining the morphological and functional vascular characteristics based on characteristic selection.

Review Paper on Detection of Diabetic Retinopathy through Quantum Transfer Learning

Diabetic Retinopathy (DR) is a common complication among diabetes patients that can cause vision impairment owing to lesions on the retina. Late-stage discovery of DR often leads to irreversible blindness. The usual approach of diagnosing DR through retinal fundus imaging by ophthalmologists is both time-consuming and costly. Although classical transfer learning methods have been extensively employed for computer-aided DR detection, their high maintenance costs can restrict their performance. In contrast, Quantum Transfer Learning is projected to provide a more effective solution to this difficulty, acting on heuristic principles and being highly optimized for the task. Our suggested method will exploit this hybrid quantum transfer learning mechanism to detect DR. We propose to utilize the Blindness Detection dataset from Kaggle to develop our model, leveraging pre-trained classical neural networks for initial feature extraction. For the classification stage, we will utilize a Variational Quantum Classifier. This future effort seeks to prove that quantum computing, coupled with quantum machine learning, can do tasks with a level of power and efficiency unattainable by classical computers alone. By harnessing these new technologies, we intend to greatly enhance the identification and diagnosis of Diabetic Retinopathy, perhaps saving many from the risk of blindness