Hasil untuk "Ophthalmology"

D. Albert, F. Jakobiec

T. D. Duane, W. Tasman

H. Dua, J. A. Gomes, A. King et al.

Wankun Xie, Min Zhao, Shu-Huai Tsai et al.

Background/Objectives: The correlation between in vivo morphological and functional changes in the degenerating retina in a large animal model of retinitis pigmentosa (RP) has not been characterized longitudinally. Herein, spectral domain optical coherence tomography (SD-OCT) was used to monitor the dynamic morphological changes in the Pro23His rhodopsin transgenic (TgP23H) pig model of RP and was correlated with electroretinography (ERG) in the rapid, early phase of photoreceptor degeneration. Methods: TgP23H and wild-type (WT) hybrid pig littermates at the ages of postnatal days 30 (P30), P60, and P90 were studied. The thickness of different retinal layers was quantified using SD-OCT and compared with histology. Retinal function was evaluated with ERG at corresponding time points. Results: In the WT pigs, retinal morphology on SD-OCT was consistent throughout the observation period. In the TgP23H pigs, the retinal thickness decreased significantly from P30 to P90. Moreover, the relative intensity of the ellipsoid zone (EZ) progressively decreased, while the intensity of the interdigitation zone–retinal pigment epithelium (IZ-RPE) progressively increased during this period. Morphological changes in SD-OCT corresponded with histology, as well as the progressively decreased amplitude of the ERG photopic a- and b-waves in the TgP23H pigs. Conclusions: Retinal degeneration can be quantified using SD-OCT by measuring retinal thickness and the intensity of the EZ and IZ-RPE bands in the TgP23H pig. The SD-OCT results correspond with the histologic and ERG assessments of retinal degeneration. These data provide a foundation for future preclinical studies investigating potential new therapeutic strategies in a large animal model of retinitis pigmentosa.

Yuto Kataoka, Yasufumi Tomioka, Morio Ueno et al.

Purpose: To describe the 10-year clinical course after cultured human corneal endothelial cell (CEC) (cHCEC) transplantation combined with central descemetorhexis in a single patient with Fuchs endothelial corneal dystrophy (FECD). Observations: A 49-year-old Japanese male was referred to the Department of Ophthalmology at Kyoto Prefectural University of Medicine, Kyoto, Japan in 2013 due to decreased visual acuity (VA) and CEC loss in his left eye caused by FECD. Upon examination, FECD-related central corneal edema, cataract, and decreased VA were observed, and on September 4, 2014 the patient underwent cHCEC transplantation in his left eye. Intraoperatively, a Descemet membrane (DM) tear occurred while abnormal CECs were being removed, thus requiring a change in the surgical plan. Subsequently, the DM was completely stripped (descemetorhexis) in an approximately 5-mm-diameter area including the pupillary center, followed by cHCEC transplantation into the anterior chamber. Prior to surgery, best-corrected VA (BCVA) was 20/50 and central corneal thickness (CCT) was 637 μm, yet corneal transparency was restored and BCVA improved to 20/20 at 6-months postoperative. At 10-years postoperative, a reasonable CEC density (CECD) was found to have adhered to the descemetorhexis area, with maintained corneal transparency; i.e., CCT measured 548 μm, CECD in the central area was 938 cells/mm2, and BCVA remained stable at 20/13. Conclusion and importance: While prospective studies are needed to generalize safety and efficacy, this FECD case treated with cHCEC transplantation combined with descemetorhexis showed no serious adverse events and sustained corneal clarity with stable CECD and CCT.

Mancini M, Palino P, Valastro A et al.

Maura Mancini,1,* Paola Palino,1,* Antonio Valastro,2,* Alessia Scolaro,1,* Giovanni William Oliverio,1,* Alessandra Mancini,3,* Pasquale Aragona,1,* Alessandro Meduri1,* 1Department of Biomedical Sciences, Ophthalmology Clinic, University of Messina, Messina, Italy; 2Ophthalmology Department, Beauregard Hospital, Azienda USL Della Valle d’Aosta, Aosta, Italy; 3Department of Ophthalmology, University Magna Graecia of Catanzaro, Catanzaro,Italy*These authors contributed equally to this workCorrespondence: Paola Palino, Department of Biomedical Sciences, Ophthalmology Clinic, University of Messina, Via Consolare Valeria 1, Messina, 98125, Italy, Tel +39 3403612250, Email palinopaola@gmail.comPurpose: To assess the safety and clinical outcomes of sutureless amniotic membrane (AM) transplantation in patients with small corneal perforations associated with severe ocular surface disease.Patients and Methods: This prospective observational study included 50 eyes of 50 patients with autoimmune or inflammatory ocular surface disease presenting with corneal perforations ≤ 3 mm. Under topical anesthesia, a double-layer AM was applied without sutures: one layer as a graft directly over the perforation and a second as a patch. A therapeutic contact lens and temporary eyelid closure were used; no topical medications were prescribed, while oral doxycycline (100 mg twice daily) was given for 15 days. Patients were examined at 1 week, 2 weeks, 1 month, 3 months, and 6 months. Primary outcomes were anatomical closure and corneal thinnest point (swept-source OCT). Secondary outcomes included complications and need for further surgery.Results: Complete anatomical closure was achieved in all cases (100%), with a mean healing time of 14.2 ± 3.6 days (range 9– 21). Corneal thinnest point increased significantly from 215 ± 38 μm at baseline to 402 ± 39 μm at 6 months (p = 0.000031, repeated-measures ANOVA). No cases of AM displacement, secondary infection, intraocular complications, or re-intervention were observed. The procedure was well tolerated in all patients.Conclusion: Sutureless AM transplantation is a safe, minimally invasive, and effective option for managing ≤ 3 mm non-infectious corneal perforations in severe ocular surface disease. It enables rapid anatomical restoration, avoids suture-related inflammation, and may be particularly advantageous in fragile or immunocompromised patients.Keywords: corneal perforation, ocular surface disease, amniotic membrane, sutureless grafting, dry eye, amniotic membrane transplantation, sutureless technique, autoimmune keratopathy, anterior segment optical coherence tomography

Sarrafpour S, Maheshwari A, Lee JH et al.

Soshian Sarrafpour,1 Akash Maheshwari,2 Jun Hui Lee,3 Benjamin K Young,4 Ji Liu,1 Christopher C Teng5 1Department of Ophthalmology & Visual Science, Yale School of Medicine, New Haven, CT, USA; 2School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA; 3Department of Ophthalmology, Keck School of Medicine of USC, Los Angeles, CA, USA; 4Casey Eye Institute, Oregon Health and Science University, Portland, OR, USA; 5Department of Ophthalmology & Visual Sciences, UMass Chan Medical School, Worcester, MA, USACorrespondence: Soshian Sarrafpour, Department of Ophthalmology & Visual Science, Yale School of Medicine, 40 Temple Street, Suite 3D, New Haven, CT, 06510, USA, Tel +1-203-377-4337, Fax +1-203-785-6220, Email soshian.sarrafpour@yale.eduPurpose: Patients increasingly seek information about medical treatment options from the internet. This study evaluated the quality and accuracy of YouTube and Facebook videos on glaucoma treatment options.Methods: A comprehensive search of “glaucoma” and “eye pressure” combined with “treatment” or “cure” was performed. YouTube videos with at least 25,000 views and 25 views per day and Facebook videos with at least 1000 total views were included. Videos were excluded if they were not in English or about humans. The quality of videos was evaluated by two independent reviewers using a modified Currency, Relevance, Authority, Accuracy, and Criteria (CRAAP) metric. Videos were categorized as educational, testimonial, or advertisement.Results: A total of 74 YouTube videos and 19 Facebook videos were included. Of the YouTube videos, 89.7% were educational, 5.5% testimonials, and 4.8% adverts. Of the Facebook videos, 65.8% were educational, 21.1% testimonials, and 13.2% adverts. The inter-rater reliability was acceptable after kappa values were calculated. Fifteen percent of YouTube videos and eighteen percent of Facebook videos were graded as containing misinformation or misleading information. Audio and video quality scores were similar between categories. Higher accuracy and comprehensiveness scores were seen for educational videos. Seventy-four percent of YouTube videos and 66% of Facebook videos addressed the question of what is glaucoma, 65% of YouTube videos and 47% of Facebook videos discussed the course of untreated disease, 64% of YouTube videos and 34% of Facebook videos discussed the goals of treatment, and only 17% of YouTube videos and 0% of Facebook videos discussed the risks of the proposed treatment options.Conclusion: Patients are increasingly using YouTube and Facebook for medical information. This study found that many videos lack useful information and some provide information that may be detrimental. Physicians should be aware of this risk and educate patients appropriately.Keywords: glaucoma treatment, YouTube, Facebook, patient education

Jia Zhou, Chang-Xing Ma

In clinical studies with paired organs, binary outcomes often exhibit intra-subject correlation and may include a mixture of unilateral and bilateral observations. Under Donner's constant correlation model, we develop three likelihood-based test statistics (the likelihood ratio, Wald-type, and score tests) for assessing the risk difference between two proportions. Simulation studies demonstrate good control of type I error and comparable power among the three tests, with the score test showing slightly better stability. Applications to otolaryngologic and ophthalmologic data illustrate the methods. An online calculator is also provided for power analysis and risk difference testing. The score test is recommended for practical use and future studies with combined unilateral and bilateral binary data.

Argha Kamal Samanta, Harshika Goyal, Vasudha Joshi et al.

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, demanding accurate automated diagnostic systems. While general-domain vision-language models like Contrastive Language-Image Pre-Training (CLIP) perform well on natural image tasks, they struggle in medical domain applications, particularly in cross-modal retrieval for ophthalmological images. We propose a novel knowledge-enhanced joint embedding framework that integrates retinal fundus images, clinical text, and structured patient data through a multimodal transformer architecture to address the critical gap in medical image-text alignment. Our approach employs separate encoders for each modality: a Vision Transformer (ViT-B/16) for retinal images, Bio-ClinicalBERT for clinical narratives, and a multilayer perceptron for structured demographic and clinical features. These modalities are fused through a joint transformer with modality-specific embeddings, trained using multiple objectives including contrastive losses between modality pairs, reconstruction losses for images and text, and classification losses for DR severity grading according to ICDR and SDRG schemes. Experimental results on the Brazilian Multilabel Ophthalmological Dataset (BRSET) demonstrate significant improvements over baseline models. Our framework achieves near-perfect text-to-image retrieval performance with Recall@1 of 99.94% compared to fine-tuned CLIP's 1.29%, while maintaining state-of-the-art classification accuracy of 97.05% for SDRG and 97.97% for ICDR. Furthermore, zero-shot evaluation on the unseen DeepEyeNet dataset validates strong generalizability with 93.95% Recall@1 versus 0.22% for fine-tuned CLIP. These results demonstrate that our multimodal training approach effectively captures cross-modal relationships in the medical domain, establishing both superior retrieval capabilities and robust diagnostic performance.

Yang Bai, Haoran Cheng, Yang Zhou et al.

Despite the promise of foundation models in medical AI, current systems remain limited - they are modality-specific and lack transparent reasoning processes, hindering clinical adoption. To address this gap, we present EVLF-FM, a multimodal vision-language foundation model (VLM) designed to unify broad diagnostic capability with fine-grain explainability. The development and testing of EVLF-FM encompassed over 1.3 million total samples from 23 global datasets across eleven imaging modalities related to six clinical specialties: dermatology, hepatology, ophthalmology, pathology, pulmonology, and radiology. External validation employed 8,884 independent test samples from 10 additional datasets across five imaging modalities. Technically, EVLF-FM is developed to assist with multiple disease diagnosis and visual question answering with pixel-level visual grounding and reasoning capabilities. In internal validation for disease diagnostics, EVLF-FM achieved the highest average accuracy (0.858) and F1-score (0.797), outperforming leading generalist and specialist models. In medical visual grounding, EVLF-FM also achieved stellar performance across nine modalities with average mIOU of 0.743 and Acc@0.5 of 0.837. External validations further confirmed strong zero-shot and few-shot performance, with competitive F1-scores despite a smaller model size. Through a hybrid training strategy combining supervised and visual reinforcement fine-tuning, EVLF-FM not only achieves state-of-the-art accuracy but also exhibits step-by-step reasoning, aligning outputs with visual evidence. EVLF-FM is an early multi-disease VLM model with explainability and reasoning capabilities that could advance adoption of and trust in foundation models for real-world clinical deployment.

Weiyi Zhang, Peranut Chotcomwongse, Yinwen Li et al.

Diabetic macular edema (DME) significantly contributes to visual impairment in diabetic patients. Treatment responses to intravitreal therapies vary, highlighting the need for patient stratification to predict therapeutic benefits and enable personalized strategies. To our knowledge, this study is the first to explore pre-treatment stratification for predicting DME treatment responses. To advance this research, we organized the 2nd Asia-Pacific Tele-Ophthalmology Society (APTOS) Big Data Competition in 2021. The competition focused on improving predictive accuracy for anti-VEGF therapy responses using ophthalmic OCT images. We provided a dataset containing tens of thousands of OCT images from 2,000 patients with labels across four sub-tasks. This paper details the competition's structure, dataset, leading methods, and evaluation metrics. The competition attracted strong scientific community participation, with 170 teams initially registering and 41 reaching the final round. The top-performing team achieved an AUC of 80.06%, highlighting the potential of AI in personalized DME treatment and clinical decision-making.

Jay Zoellin, Colin Merk, Mischa Buob et al.

Integrating deep learning into medical imaging is poised to greatly advance diagnostic methods but it faces challenges with generalizability. Foundation models, based on self-supervised learning, address these issues and improve data efficiency. Natural domain foundation models show promise for medical imaging, but systematic research evaluating domain adaptation, especially using self-supervised learning and parameter-efficient fine-tuning, remains underexplored. Additionally, little research addresses the issue of catastrophic forgetting during fine-tuning of foundation models. We adapted the DINOv2 vision transformer for retinal imaging classification tasks using self-supervised learning and generated two novel foundation models termed DINORET and BE DINORET. Publicly available color fundus photographs were employed for model development and subsequent fine-tuning for diabetic retinopathy staging and glaucoma detection. We introduced block expansion as a novel domain adaptation strategy and assessed the models for catastrophic forgetting. Models were benchmarked to RETFound, a state-of-the-art foundation model in ophthalmology. DINORET and BE DINORET demonstrated competitive performance on retinal imaging tasks, with the block expanded model achieving the highest scores on most datasets. Block expansion successfully mitigated catastrophic forgetting. Our few-shot learning studies indicated that DINORET and BE DINORET outperform RETFound in terms of data-efficiency. This study highlights the potential of adapting natural domain vision models to retinal imaging using self-supervised learning and block expansion. BE DINORET offers robust performance without sacrificing previously acquired capabilities. Our findings suggest that these methods could enable healthcare institutions to develop tailored vision models for their patient populations, enhancing global healthcare inclusivity.

I. R. Slootweg, M. Thach, K. R. Curro-Tafili et al.

Background/Aim. This study aims to predict Amyloid Positron Emission Tomography (AmyloidPET) status with multimodal retinal imaging and convolutional neural networks (CNNs) and to improve the performance through pretraining with synthetic data. Methods. Fundus autofluorescence, optical coherence tomography (OCT), and OCT angiography images from 328 eyes of 59 AmyloidPET positive subjects and 108 AmyloidPET negative subjects were used for classification. Denoising Diffusion Probabilistic Models (DDPMs) were trained to generate synthetic images and unimodal CNNs were pretrained on synthetic data and finetuned on real data or trained solely on real data. Multimodal classifiers were developed to combine predictions of the four unimodal CNNs with patient metadata. Class activation maps of the unimodal classifiers provided insight into the network's attention to inputs. Results. DDPMs generated diverse, realistic images without memorization. Pretraining unimodal CNNs with synthetic data improved AUPR at most from 0.350 to 0.579. Integration of metadata in multimodal CNNs improved AUPR from 0.486 to 0.634, which was the best overall best classifier. Class activation maps highlighted relevant retinal regions which correlated with AD. Conclusion. Our method for generating and leveraging synthetic data has the potential to improve AmyloidPET prediction from multimodal retinal imaging. A DDPM can generate realistic and unique multimodal synthetic retinal images. Our best performing unimodal and multimodal classifiers were not pretrained on synthetic data, however pretraining with synthetic data slightly improved classification performance for two out of the four modalities.

Usama Younus, Nirupam Roy

This article aims to cover pupillography, and its potential use in a number of ophthalmological diagnostic applications in biomedical space. With the ever-increasing incorporation of technology within our daily lives and an ever-growing active research into smart devices and technologies, we try to make a case for a health ecosystem that revolves around continuous eye monitoring. We tend to summarize the design constraints & requirements for an IoT-based continuous pupil detection system, with an attempt at developing a pipeline for wearable pupillographic device, while comparing two compact mini-camera modules currently available in the market. We use a light algorithm that can be directly adopted to current micro-controllers, and share our results for different lighting conditions, and scenarios. Lastly, we present our findings, along with an analysis on the challenges faced and a way ahead towards successfully building this ecosystem.

Manasa Kesapragada, Yao-Hui Sun, Ksenia Zlobina et al.

Macrophages can exhibit pro-inflammatory or pro-reparatory functions, contingent upon their specific activation state. This dynamic behavior empowers macrophages to engage in immune reactions and contribute to tissue homeostasis. Understanding the intricate interplay between macrophage motility and activation status provides valuable insights into the complex mechanisms that govern their diverse functions. In a recent study, we developed a classification method based on morphology, which demonstrated that movement characteristics, including speed and displacement, can serve as distinguishing factors for macrophage subtypes. In this study, we develop a deep learning model to explore the potential of classifying macrophage subtypes based solely on raw trajectory patterns. The classification model relies on the time series of x-y coordinates, as well as the distance traveled and net displacement. We begin by investigating the migratory patterns of macrophages to gain a deeper understanding of their behavior. Although this analysis does not directly inform the deep learning model, it serves to highlight the intricate and distinct dynamics exhibited by different macrophage subtypes, which cannot be easily captured by a finite set of motility metrics. Our study uses cell trajectories to classify three macrophage subtypes: M0, M1, and M2. This advancement holds promising implications for the future, as it suggests the possibility of identifying macrophage subtypes without relying on shape analysis. Consequently, it could potentially eliminate the necessity for high-quality imaging techniques and provide more robust methods for analyzing inherently blurry images.

Hiroshi Ohguro, Megumi Watanabe, Tatsuya Sato et al.

Cell culture methods are indispensable strategies for studies in biological sciences and for drug discovery and testing. Most cell cultures have been developed using two-dimensional (2D) culture methods, but three-dimensional (3D) culture techniques enable the establishment of <i>in vitro</i> models that replicate various pathogenic conditions and they provide valuable insights into the pathophysiology of various diseases as well as more precise results in tests for drug efficacy. However, one difficulty in the use of 3D cultures is selection of the appropriate 3D cell culture technique for the study purpose among the various techniques ranging from the simplest single cell type-derived spheroid culture to the more sophisticated organoid cultures. In the simplest single cell type-derived spheroid cultures, there are also various scaffold-assisted methods such as hydrogel-assisted cultures, biofilm-assisted cultures, particle-assisted cultures, and magnet particle-assisted cultures, as well as non-assisted methods, such as static suspension cultures, floating cultures, and hanging drop cultures. Since each method can be differently influenced by various factors such as gravity force, buoyant force, centrifugal force, and magnetic force, in addition to non-physiological scaffolds, each method has its own advantages and disadvantages, and the methods have different suitable applications. We have been focusing on the use of a hanging drop culture method for modeling various non-cancerous and cancerous diseases because this technique is affected only by gravity force and buoyant force and is thus the simplest method among the various single cell type-derived spheroid culture methods. We have found that the biological natures of spheroids generated even by the simplest method of hanging drop cultures are completely different from those of 2D cultured cells. In this review, we focus on the biological aspects of single cell type-derived spheroid culture and its applications in <i>in vitro</i> models for various diseases.

C. McAlinden, Jyoti Khadka, K. Pesudovs

Min Gao, Tristan T. Hormel, Yukun Guo et al.

Purpose: Microaneurysms (MAs) have distinct, oval-shaped, hyperreflective walls on structural OCT, and inconsistent flow signal in the lumen with OCT angiography (OCTA). Their relationship to regional macular edema in diabetic retinopathy (DR) has not been quantitatively explored. Participants: A total of 99 participants, including 23 with mild, NPDR, 25 with moderate NPDR, 34 with severe NPDR, and 17 with proliferative DR. Methods: We obtained 3 x 3-mm scans with a commercial device (Solix, Visionix/Optovue) in 99 patients with DR. Trained graders manually identified MAs and their location relative to the anatomic layers from cross-sectional OCT. Microaneurysms were first classified as perfused if flow signal was present in the OCTA channel. Then, perfused MAs were further classified into fully and partially perfused MAs based on the flow characteristics in en face OCTA. The presence of retinal fluid based on OCT near MAs was compared between perfused and nonperfused types. We also compared OCT-based MA detection to fundus photography (FP)- and fluorescein angiography (FA)-based detection. Results: We identified 308 MAs (166 fully perfused, 88 partially perfused, 54 nonperfused) in 42 eyes using OCT and OCTA. Nearly half of the MAs identified in this study straddle the inner nuclear layer and outer plexiform layer. Compared with partially perfused and nonperfused MAs, fully perfused MAs were more likely to be associated with local retinal fluid. The associated fluid volumes were larger with fully perfused MAs compared with other types. OCT/OCTA detected all MAs found on FP. Although not all MAs seen with FA were identified with OCT, some MAs seen with OCT were not visible with FA or FP. Conclusions: OCT-identified MAs with colocalized flow on OCTA are more likely to be associated with DME than those without flow.

Sajid Rahim, Kourosh Sabri, Anna Ells et al.

Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder because of damage to the eye's retina which can affect babies born prematurely. Screening of ROP is essential for early detection and treatment. This is a laborious and manual process which requires trained physician performing dilated ophthalmological examination which can be subjective resulting in lower diagnosis success for clinically significant disease. Automated diagnostic methods can assist ophthalmologists increase diagnosis accuracy using deep learning. Several research groups have highlighted various approaches. Captured ROP Retcam images suffer from poor quality. This paper proposes the use of improved novel fundus preprocessing methods using pretrained transfer learning frameworks to create hybrid models to give higher diagnosis accuracy. Once trained and validated, the evaluations showed that these novel methods in comparison to traditional imaging processing contribute to better and in many aspects higher accuracy in classifying Plus disease, Stages of ROP and Zones in comparison to peer papers.

Zhe Zhang, Chen Li, Qian Li et al.

Abstract Alternative splicing is an important mechanism that enhances protein functional diversity. To date, our understanding of alternative splicing variants has been based on mRNA transcript data, but due to the difficulty in predicting protein structures, protein tertiary structures have been largely unexplored. However, with the release of AlphaFold, which predicts three-dimensional models of proteins, this challenge is rapidly being overcome. Here, we present a dataset of 315 predicted structures of abnormal isoforms in 18 uveal melanoma patients based on second- and third-generation transcriptome-sequencing data. This information comprises a high-quality set of structural data on recurrent aberrant isoforms that can be used in multiple types of studies, from those aimed at revealing potential therapeutic targets to those aimed at recognizing of cancer neoantigens at the atomic level.