Using multi-modal imaging to refine the phenotype of PRPH2-associated retinal degeneration.

OBJECTIVE: To refine retinal PRPH2-associated retinal degeneration (PARD) phenotypes using multimodal imaging. DESIGN: Retrospective review of clinical records and multimodal imaging. SUBJECTS: Patients who visited the inherited retinal degeneration (IRD) clinic at two tertiary referral eye centers with molecularly confirmed IRD due to PRPH2 variants. METHODS: Retinal imaging was reviewed using ultra-widefield (UWF) pseudocolor, UWF fundus autofluorescence (FAF), and spectral-domain optical coherence tomography (SD-OCT). Phenotypes were identified in the macular or peripheral region. A combined phenotype was considered if any phenotypes were present in both macular and peripheral regions. Mixed phenotypes in the macula or peripheral retina were considered if there were two distinct phenotypes identified in the same eye. The presence or absence of atrophy in the macular or peripheral area was also noted. MAIN OUTCOME MEASURE: Grading of multimodal imaging by phenotype and atrophy. RESULTS: A total of 144 eyes of 72 patients were included in this study. The majority of the eyes had combined macular and peripheral phenotypes (89/14, 61.8%), while 44 (30.6%) eyes had isolated macular findings, and 11 (7.6%) had isolated peripheral findings. Twenty-five eyes were classified with mixed macular phenotypes while fundus flavimaculatus dystrophy type was the most common combined macular and peripheral phenotype (54/144, 37.5%): n = 10 with macular dystrophy and macular flavimaculatus dystrophy, and n = 15 with butterfly pattern dystrophy and macular flavimaculatus dystrophy. Nearly half of the eyes (71/144, 49.3%) were identified to have concomitant outer retinal atrophy. Fundus flavimaculatus type dystrophy was also associated with the highest proportion of concomitant atrophy (57/71, 80.3%). CONCLUSION: PARD demonstrates a wide array of phenotypes using multimodal imaging. We report that combinations of classically described phenotypes were often seen. Additionally, macular and peripheral atrophy were often associated with PARD phenotypes. Refinement of PARD phenotypes using newer multimodal imaging techniques will likely assist diagnosis and future clinical trials.

Ocular fundus changes and association with systemic conditions in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple organs and systems. Ocular involvement is estimated to manifest in one-third of individuals with SLE, of which lupus retinopathy and choroidopathy represent the severe subtype accompanied by vision impairment. Advancements in multimodal ophthalmic imaging have allowed ophthalmologists to reveal subclinical microvascular and structural changes in fundus of patients with SLE without ocular manifestations. Both ocular manifestations and subclinical fundus damage have been shown to correlate with SLE disease activity and, in some patients, even precede other systemic injuries as the first presentation of SLE. Moreover, ocular fundus might serve as a window into the state of systemic vasculitis in patients with SLE. Given the similarities of the anatomy, physiological and pathological processes shared among ocular fundus, and other vital organ damage in SLE, such as kidney and brain, it is assumed that ocular fundus involvement has implications in the diagnosis and evaluation of other systemic impairments. Therefore, evaluating the fundus characteristics of patients with SLE not only contributes to the early diagnosis and intervention of potential vision damage, but also holds considerate significance for the evaluation of SLE vasculitis state and prediction of other systemic injuries.

Multi-step framework for glaucoma diagnosis in retinal fundus images using deep learning.

Glaucoma is one of the most common causes of blindness in the world. Screening glaucoma from retinal fundus images based on deep learning is a common method at present. In the diagnosis of glaucoma based on deep learning, the blood vessels within the optic disc interfere with the diagnosis, and there is also some pathological information outside the optic disc in fundus images. Therefore, integrating the original fundus image with the vessel-removed optic disc image can improve diagnostic efficiency. In this paper, we propose a novel multi-step framework named MSGC-CNN that can better diagnose glaucoma. In the framework, (1) we combine glaucoma pathological knowledge with deep learning model, fuse the features of original fundus image and optic disc region in which the interference of blood vessel is specifically removed by U-Net, and make glaucoma diagnosis based on the fused features. (2) Aiming at the characteristics of glaucoma fundus images, such as small amount of data, high resolution, and rich feature information, we design a new feature extraction network RA-ResNet and combined it with transfer learning. In order to verify our method, we conduct binary classification experiments on three public datasets, Drishti-GS, RIM-ONE-R3, and ACRIMA, with accuracy of 92.01%, 93.75%, and 97.87%. The results demonstrate a significant improvement over earlier results.

Evaluating the potential of retinal photography in chronic kidney disease detection: a review.

Background: Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives: The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology: A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results: Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion: In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.

Diabetic retinopathy screening with confocal fundus camera and artificial intelligence - assisted grading.

PURPOSE: Screening for diabetic retinopathy (DR) by ophthalmologists is costly and labour-intensive. Artificial Intelligence (AI) for automated DR detection could be a clinically and economically alternative. We assessed the performance of a confocal fundus imaging system (DRSplus, Centervue SpA), coupled with an AI algorithm (RetCAD, Thirona B.V.) in a real-world setting. METHODS: 45° non-mydriatic retinal images from 506 patients with diabetes were graded both by an ophthalmologist and by the AI algorithm, according to the International Clinical Diabetic Retinopathy severity scale. Less than moderate retinopathy (DR scores 0, 1) was defined as non-referable, while more severe stages were defined as referable retinopathy. The gradings were then compared both at eye-level and patient-level. Key metrics included sensitivity, specificity all measured with a 95% Confidence Interval. RESULTS: The percentage of ungradable eyes according to the AI was 2.58%. The performances of the AI algorithm for detecting referable DR were 97.18% sensitivity, 93.73% specificity at eye-level and 98.70% sensitivity and 91.06% specificity at patient-level. CONCLUSIONS: DRSplus paired with RetCAD represents a reliable DR screening solution in a real-world setting. The high sensitivity of the system ensures that almost all patients requiring medical attention for DR are referred to an ophthalmologist for further evaluation.

Infrared live imaging and navigated laser for nematode photocoagulation in a child with diffuse unilateral subacute neuroretinitis (DUSN).

Purpose: To report the application of an infrared fundus imaging and navigated laser system to photocoagulate a nematode in diffuse unilateral subacute neuroretinitis (DUSN). Observations: A 14-year-old boy with DUSN was treated with systemic albendazole and corticosteroids. Laser photocoagulation of the visible nematode was performed using a navigated laser in live infrared fundus view (Navilas 577s, OD-OS GmbH, Berlin, Germany). While the localization of the nematode was difficult in regular fundoscopy due to the light-shy helminth, it could be well localized and targeted with the infrared live video mode and navigated laser system. No inflammatory flare-up was observed after the nematode was killed. Conclusions and Importance: Laser photocoagulation and systemic antihelminthic therapy are an established treatment for DUSN. Infrared imaging and navigated laser systems seem useful in targeting and killing mobile nematodes.

Cross-sectional Analysis of Outer Retinal Tubulation in Inherited Retinal Diseases: A Multicenter Study.

PURPOSE: This study aims to explore genetic variants that potentially lead to outer retinal tubulation (ORT), estimate the prevalence of ORT in these candidate genes, and investigate the clinical etiology of ORT in patients with inherited retinal diseases (IRDs), with respect to each gene. DESIGN: Retrospective cohort study. METHODS: A retrospective cross-sectional review was conducted on 565 patients with molecular diagnoses of IRD, confirming the presence of ORT as noted in each patient's respective spectral-domain optical coherence tomography (SD-OCT) imaging. Using SD-OCT imaging, the presence of ORT was analyzed in relation to specific genetic variants and phenotypic characteristics. Outcomes included the observed ORT frequencies across two gene-specific cohorts: non- retinal pigment epithelium (RPE)-specific genes, and RPE-specific genes; and to investigate the analogous characteristics caused by variants in these genes. RESULTS: Among the 565 patients included in this study, 104 exhibited ORT on SD-OCT. We observed ORT frequencies among the following genes from our patient cohort: 100% (23/23) forCHM, 100%(2/2) forPNPLA6, 100% (4/4) forRCBTB1, 100% formtDNA[100% (4/4) forMT-TL1and 100% (1/1) formtDNAdeletion], 100% (1/1) forOAT, 95.2% (20/21) forCYP4V2, 72.7% (8/11) forCHMfemale carriers, 66.7% (2/3) forC1QTNF5, 57.1% (8/14) forPROM1, 53.8% (7/13) forPRPH2, 42.9% (3/7) forCERKL, 28.6% (2/7) forCDHR1, 20% (1/5) forRPE65, 4% (18/445) forABCA4.In contrast, ORT was not observed in any patients with photoreceptor-specific gene variants, such asRHO(n=13),USH2A(n=118),EYS(n=70),PDE6B(n=10),PDE6A(n=4),and others. CONCLUSION: These results illustrate a compelling association between the presence of ORT and IRDs caused by variants in RPE-specific genes, as well as non-RPE-specific genes. In contrast, IRDs caused by photoreceptor-specific genes are typically not associated with ORT occurrence. Further analysis revealed that ORT tends to manifest in IRDs with milder intraretinal pigment migration (IPM), a finding that is typically associated with RPE-specific genes. These findings regarding ORT, genetic factors, atrophic patterns in the fundus, and IPM provide valuable insight into the complex etiology of IRDs. Future prospective studies are needed to further explore the association and underlying mechanisms of ORT in these contexts.

Predictive value of blood coagulation and routine blood indices for rebleeding after endoscopic treatment in hepatitis B-related cirrhotic patients with esophagogastric fundal varices: a logistic regression model analysis.

OBJECTIVE: To evaluate the predictive value of blood coagulation and routine blood indices for rebleeding after endoscopic treatment of ruptured esophagogastric fundal varices (EGVB) in cirrhotic patients with hepatitis B infection. METHODS: This retrospective analysis included 248 patients with hepatitis B-related cirrhosis and EGVB who received initial endoscopic treatment from October 2019 to March 2022 and were followed up for 12 months. Patients were divided into rebleeding and non-rebleeding groups. Laboratory indices were analyzed, and univariate and multivariate analyses identified predictors of rebleeding. The efficacy of a logistic regression model was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), and a risk factor nomogram was constructed for assessing the predictive efficiency of those risk factors. RESULTS: Univariate analysis showed significant differences in portal vein diameters and lower Child-Pugh scores in the rebleeding group in contrast to those in the non-rebleeding group. Key laboratory markers such as platelet count (PLT), albumin (ALB), alanine aminotransferase (ALT), lymphocytes (LYM), and prognostic nutritional index (PNI) were lower, while prothrombin time (PT) and lactate levels (LN) were higher in the rebleeding group than those in the non-rebleeding group. Multivariate analysis identified portal vein diameter, PLT, ALT, PT, LYM, and PNI as significant predictors of rebleeding. The logistic model demonstrated high accuracy (AUC=0.986) and clinical value, validated by ROC curves, calibration curves (C-index =0.986), and DCA results. A risk factor predictive nomogram was successfully constructed. CONCLUSION: This study developed a logistic regression model with a nomogram for predicting EGVB-related rebleeding in patients with hepatitis B-related cirrhosis, achieving an AUC of 0.986, indicating high accuracy and significant clinical relevance.

Categories and Profiles of Uveitis in Vogt-Koyanagi-Harada Syndrome With Systemic Correlation: Inferences From a Tertiary Multispecialty Hospital.

Introduction Vogt-Koyanagi-Harada (VKH) syndrome is a granulomatous, autoimmune panuveitis, affecting the eyes, ears, skin, and meninges. It can cause choroiditis and can progress to the retina and optic disc causing visual loss. Imaging using fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), and enhanced depth imaging-ocular coherence tomography (EDI-OCT) is required for clinical evaluation and management. Steroids and immunosuppression are the treatment modalities used. Aim The aim of this study is to report the correlation and severity of uveitis in relation to systemic manifestations. Method A retrospective study including 100 patients with VKH syndrome was carried out. They were classified based on clinical manifestations and investigations such as FFA, ICGA, B-scan ultrasonography (USG), and ocular coherence tomography (OCT). Patients were characterized as complete, incomplete, and probable VKH syndrome. Laboratory investigations were performed, and statistical analysis was done. Results Probable VKH syndrome was found to be the most common form of presentation in our study population. Defective vision was the most common complaint among the patients. Extraocular manifestations included tinnitus, vertigo, alopecia, headache, fatigue, and vitiligo and were seen in 33% of the patients. Disc edema and serous retinal detachment were seen in 85% of the patients. Improvement was noted in 25% of the patients with the use of corticosteroids. Conclusion Response to treatment with systemic corticosteroids and immunosuppression in the acute phase of uveitis is better compared to chronic uveitis. The ophthalmologist is usually first consulted in VKH syndrome due to presenting ocular complaints. A multidisciplinary approach is key to providing holistic management.

American Society of Retina Specialists Artificial Intelligence Task Force Report.

Since the Artificial Intelligence Committee of the American Society of Retina Specialists developed the initial task force report in 2020, the artificial intelligence (AI) field has seen further adoption of US Food and Drug Administration-approved AI platforms and significant development of AI for various retinal conditions. With expansion of this technology comes further areas of challenges, including the data sources used in AI, the democracy of AI, commercialization, bias, and the need for provider education on the technology of AI. The overall focus of this committee report is to explore these recent issues as they relate to the continued development of AI and its integration into ophthalmology and retinal practice.

Enhancing the ophthalmic AI assessment with a fundus image quality classifier using local and global attention mechanisms.

Background: The assessment of image quality (IQA) plays a pivotal role in the realm of image-based computer-aided diagnosis techniques, with fundus imaging standing as the primary method for the screening and diagnosis of ophthalmic diseases. Conventional studies on fundus IQA tend to rely on simplistic datasets for evaluation, predominantly focusing on either local or global information, rather than a synthesis of both. Moreover, the interpretability of these studies often lacks compelling evidence. In order to address these issues, this study introduces the Local and Global Attention Aggregated Deep Neural Network (LGAANet), an innovative approach that integrates both local and global information for enhanced analysis. Methods: The LGAANet was developed and validated using a Multi-Source Heterogeneous Fundus (MSHF) database, encompassing a diverse collection of images. This dataset includes 802 color fundus photography (CFP) images (302 from portable cameras), and 500 ultrawide-field (UWF) images from 904 patients with diabetic retinopathy (DR) and glaucoma, as well as healthy individuals. The assessment of image quality was meticulously carried out by a trio of ophthalmologists, leveraging the human visual system as a benchmark. Furthermore, the model employs attention mechanisms and saliency maps to bolster its interpretability. Results: In testing with the CFP dataset, LGAANet demonstrated remarkable accuracy in three critical dimensions of image quality (illumination, clarity and contrast based on the characteristics of human visual system, and indicates the potential aspects to improve the image quality), recording scores of 0.947, 0.924, and 0.947, respectively. Similarly, when applied to the UWF dataset, the model achieved accuracies of 0.889, 0.913, and 0.923, respectively. These results underscore the efficacy of LGAANet in distinguishing between varying degrees of image quality with high precision. Conclusion: To our knowledge, LGAANet represents the inaugural algorithm trained on an MSHF dataset specifically for fundus IQA, marking a significant milestone in the advancement of computer-aided diagnosis in ophthalmology. This research significantly contributes to the field, offering a novel methodology for the assessment and interpretation of fundus images in the detection and diagnosis of ocular diseases.

Evaluation of sensitivity and specificity of the slit-light method in the diagnosis of cyclotorsion.

PURPOSE: The purpose of this study was to evaluate the amount of sensitivity and specificity of the slit-light (SL) method in the diagnosis of ocular cyclotorsion. MATERIALS AND METHODS: One hundred and twenty eyes of 60 individuals (10-60 years old), with mean visual acuity of 0.08 ± 0.14 LogMAR, were divided into two groups (normal and torsion groups). Individuals without ocular motility disorder were selected as normal and patients with extraocular motility disorders and oblique muscle dysfunctions as the torsion group. The sensitivity and specificity of SL in the diagnosis of ocular torsion were measured by masked investigators and compared to fundus photography (FP). Inter- and intraobserver variability of these techniques was also determined. RESULTS: The amounts of sensitivity and specificity of SL, measured by the first examiner, were 60% and 92% for intorsion and 50% and 96% for extorsion assessment, respectively. These amounts were 53% and 95% for intorsion, and 54% and 97% for extorsion by the second examiner. The contingency coefficient between the two examiners was 68.6% for SL. This amount was 61% between FP and SL for the first examiner and 63% for the second. The contingency coefficient for the repeatability of SL was 72.2% for the first examiner and 75.7% for the second. This amount was 71.2% between the two examiners. CONCLUSION: SL can be considered a useful method for the diagnosis of cyclotorsion.

Fine-Tuning SSL-Model to Enhance Detection of Cilioretinal Arteries on Colored Fundus Images.

Cilioretinal arteries are a common congenital anomaly of retinal blood supply. This paper presents a deep learning-based approach for the automated detection of a CRA from color fundus images. Leveraging the Vision Transformer architecture, a pre-trained model from RETFound was fine-tuned to transfer knowledge from a broader dataset to our specific task. An initial dataset of 85 was expanded to 170 images through data augmentation using self-supervised learning-driven techniques. To address the imbalance in the dataset and prevent overfitting, Focal Loss and Early Stopping were implemented. The model's performance was evaluated using a 70-30 split of the dataset for training and validation. The results showcase the potential of ophthalmic foundation models in enhancing detection of CRAs and reducing the effort required for labeling by retinal experts, as promising results could be achieved with only a small amount of training data through fine-tuning.

Hyperautofluorescent material inside areas of macular atrophy may reveal non-lipofuscin fluorophores in late stage AMD.

PURPOSE: To characterize fundus autofluorescence (FAF) in complete (cRORA) and incomplete retinal pigment epithelium and outer retinal atrophy (iRORA) by fluorescence lifetime imaging ophthalmology (FLIO). METHODS: Overall, 98 macular atrophy (MA) lesions in 42 eyes of 37 age-related macular degeneration (AMD) patients (mean age: 80.9 ± 5.8 years), 25 of them classified as iRORA and 73 as cRORA by OCT, were investigated by FLIO in a short (SSC: 498-560 nm) and a long wavelength channel (LSC: 560-720 nm). Differences of FAF lifetimes and peak emission wavelength (PEW) between atrophic lesions and intact retinal pigment epithelium (RPE) in the outer ring of the ETDRS grid were considered. RESULTS: FAF lifetimes in MA were longer and PEW were significantly (p < 0.001) shorter than in intact RPE by 112 ± 78 ps (SSC), 91 ± 64 ps (LSC), 27 ± 18 nm (PEW) in iRORA and by 227 ± 112 ps (SSC), 167 ± 81 ps (LSC), and 54 ± 17 nm (PEW) in cRORA. 37% of iRORA and 24% of cRORA were hyperautofluorescent in SSC. Persistent sub-RPE-BL material in MA was newly found as a hyperautofluorescent entity with lifetimes considerably longer than that of drusen and RPE. CONCLUSIONS: Despite RPE and, thus, lipofuscin are greatly absent in MA, considerable FAF, preferably at short wavelengths, was found in those lesions. Drusen, persistent sub-RPE-BL material, basal laminar deposits, persistent activated RPE, and sclera were identified as putative sources of this fluorescence. FLIO can help to characterize respective fluorophores.

The addition of pembrolizumab to neoadjuvant chemoradiotherapy did not increase the risk of developing postoperative anastomotic leakage for ESCC: an analysis from a prospective cohort.

BACKGROUND: To compare the difference of postoperative anastomotic leakage (AL) rate between neoadjuvant chemoradiotherapy (NCRT) with pembrolizumab and NCRT group, and investigate the risk factors of developing AL for locally advanced esophageal squamous cell cancer (ESCC). MATERIALS AND METHODS: The GF was contoured on the pretreatment planning computed tomography and dosimetric parameters were retrospectively calculated. Univariate and multivariate logistic regression analysis was performed to determine the independent risk predictors for the entire cohort. A nomogram risk prediction model for postoperative AL was established. RESULTS: A total of 160 ESCC patients were included for analysis. Of them, 112 were treated with NCRT with pembrolizumab and 44 patients with NCRT. Seventeen (10.6%) patients experienced postoperative AL with a rate of 10.7% (12/112) in NCRT with pembrolizumab and 11.4% (5/44) in NCRT group. For the entire cohort, mean, D50, Dmax, V5, V10 and V20 GF dose were statistically higher in those with AL (all p < 0.05). Multivariate logistic regression analysis indicated that tumor length (p = 0.012), volume of GF (p = 0.003) and mean dose of GF (p = 0.007) were independently predictors for postoperative AL. Using receiver operating characteristics analysis, the mean dose limit on the GF was defined as 14 Gy. CONCLUSION: Based on our prospective database, no significant difference of developing AL were observed between NCRT with pembrolizumab and NCRT group. We established an individualized nomograms based on mean GF dose combined with clinical indicators to predict AL in the early postoperative period.

Improve the efficiency and accuracy of ophthalmologists' clinical decision-making based on AI technology.

BACKGROUND: As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy. METHODS: In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors. RESULTS: The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors. CONCLUSION: The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

Clinical Applications and Future Directions of Smartphone Fundus Imaging.

The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical applications, and future directions. The traditional fundus imaging techniques are limited by their cost, portability, and accessibility, particularly in resource-limited settings. Smartphone fundus imaging emerges as a cost-effective, portable, and accessible alternative. This technology facilitates the early detection and monitoring of various retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders, thereby democratizing access to essential diagnostic services. Despite its advantages, smartphone fundus imaging faces challenges in image quality, standardization, regulatory considerations, and medicolegal issues. By addressing these limitations, this review highlights the areas for future research and development to fully harness the potential of smartphone fundus imaging in enhancing patient care and visual outcomes. The integration of this technology into telemedicine is also discussed, underscoring its role in facilitating remote patient care and collaborative care among physicians. Through this review, we aim to contribute to the understanding and advancement of smartphone fundus imaging as a valuable tool in ophthalmic practice, paving the way for its broader adoption and integration into medical diagnostics.

Longitudinal evaluation of morphological, functional and vascular alterations in a rat model of experimental glaucoma.

Glaucoma, the leading cause of irreversible blindness worldwide, is a neurodegenerative disease characterized by chronic axonal damages and progressive loss of retinal ganglion cells, with increased intraocular pressure (IOP) as the primary risk factor. While current treatments focus solely on reducing IOP, understanding glaucoma through experimental models is essential for developing new therapeutic strategies and biomarkers for early diagnosis. Our research group developed an ocular hypertension rat model based on limbal plexus cautery, which provides significant glaucomatous neurodegeneration up to four weeks after injury. We evaluated long-term morphological, functional, and vascular alterations in this model. Our results showed that transient ocular hypertension, lasting approximately one week, can lead to progressive increase in optic nerve cupping and retinal ganglion cells loss. Remarkably, the pressure insult caused several vascular changes, such as arteriolar and venular thinning, and permanent choroidal vascular swelling. This study provides evidence of the longitudinal effects of a pressure insult on retinal structure and function using clinical modalities and techniques. The multifactorial changes reported in this model resemble the complex retinal ganglion cell degeneration found in glaucoma patients, and therefore may also provide a unique tool for the development of novel interventions to either halt or slow down disease progression.

Relationship between fundus sex index obtained using color fundus parameters and body height or axial length in the Kumejima population.

PURPOSE: To investigate the relationship between the fundus sex index obtained from fundus photographs and body height or axial length in the Kumejima population. STUDY DESIGN: Prospective cross-sectional observational population study. METHODS: Using color fundus photographs obtained from the Kumejima population, 1,653 healthy right eyes with reliable fundus parameter measurements were included in this study. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red-green-blue intensity in the eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. The masculine or feminine fundus was quantified using machine learning (L2 regularized binominal logistic regression and leave one out cross validation), with the range of 0-1 as the predictive value, and defined as the fundus sex index. The relationship between the fundus sex index and body height or axial length was investigated using Spearman's correlation. RESULTS: The mean age of the 838 men and 815 women included in this study was 52.8 and 54.0 years, respectively. The correlation coefficient between fundus sex index and body height was - 0.40 (p < 0.001) in all, 0.01 (p = 0.89) in men, and - 0.04 (p = 0.30) in women, and that between fundus sex index and axial length was - 0.23 (p < 0.001) in all, - 0.12 (p < 0.001) in men, and - 0.13 (p < 0.001) in women. CONCLUSION: This study shows that a larger number of masculine fundi tend to have longer axial lengths in each sex group. However, sex index was not significantly related with body height either in men or in women.

Highly Accurate and Precise Automated Cup-to-Disc Ratio Quantification for Glaucoma Screening.

Objective: An enlarged cup-to-disc ratio (CDR) is a hallmark of glaucomatous optic neuropathy. Manual assessment of the CDR may be less accurate and more time-consuming than automated methods. Here, we sought to develop and validate a deep learning-based algorithm to automatically determine the CDR from fundus images. Design: Algorithm development for estimating CDR using fundus data from a population-based observational study. Participants: A total of 181 768 fundus images from the United Kingdom Biobank (UKBB), Drishti_GS, and EyePACS. Methods: FastAI and PyTorch libraries were used to train a convolutional neural network-based model on fundus images from the UKBB. Models were constructed to determine image gradability (classification analysis) as well as to estimate CDR (regression analysis). The best-performing model was then validated for use in glaucoma screening using a multiethnic dataset from EyePACS and Drishti_GS. Main Outcome Measures: The area under the receiver operating characteristic curve and coefficient of determination. Results: Our gradability model vgg19_batch normalization (bn) achieved an accuracy of 97.13% on a validation set of 16 045 images, with 99.26% precision and area under the receiver operating characteristic curve of 96.56%. Using regression analysis, our best-performing model (trained on the vgg19_bn architecture) attained a coefficient of determination of 0.8514 (95% confidence interval [CI]: 0.8459-0.8568), while the mean squared error was 0.0050 (95% CI: 0.0048-0.0051) and mean absolute error was 0.0551 (95% CI: 0.0543-0.0559) on a validation set of 12 183 images for determining CDR. The regression point was converted into classification metrics using a tolerance of 0.2 for 20 classes; the classification metrics achieved an accuracy of 99.20%. The EyePACS dataset (98 172 healthy, 3270 glaucoma) was then used to externally validate the model for glaucoma classification, with an accuracy, sensitivity, and specificity of 82.49%, 72.02%, and 82.83%, respectively. Conclusions: Our models were precise in determining image gradability and estimating CDR. Although our artificial intelligence-derived CDR estimates achieve high accuracy, the CDR threshold for glaucoma screening will vary depending on other clinical parameters. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.