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The process of coronary calcification represents one of the numerous pathophysiological mechanisms involved in the atherosclerosis continuum. Optical coherence tomography (OCT) represents an ideal imaging modality to assess plaque components, especially calcium. Different calcification patterns have been contemporarily described in both early stages and advanced atherosclerosis. Microcalcifications and spotty calcifications correlate positively with macrophage burden and inflammatory markers and are more frequently found in the superficial layers of ruptured plaques in acute coronary syndrome patients. More compact, extensive calcification may reflect a later stage of the disease and was traditionally associated with plaque stability. Nevertheless, a small number of culprit coronary lesions demonstrates the presence of dense calcified plaques. The purpose of the current paper is to review the most recent OCT data on coronary calcification and the interrelation between calcification pattern and plaque vulnerability. How different calcified plaques influence treatment strategies and associated prognostic implications is of great interest.

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Background: Advances in medicine and public health mean that people are living longer; however, a significant proportion of that increased lifespan is spent in a prolonged state of declining health and wellbeing which places increasing pressure on medical, health and social services. There is a social and economic need to develop strategies to prevent or delay age-related disease and maintain lifelong health. Several studies have suggested links between the gut microbiome and age-related disease, which if confirmed would present a modifiable target for intervention development. The MOTION study aims to determine whether and how changes in the gut microbiome are associated with physical and mental capacity. A comprehensive longitudinal multiparameter study such as this has not been previously undertaken. Methods: MOTION is a longitudinal prospective cohort study with a focus on gut health and cognitive function. 360 healthy individuals aged 60 years and older, living in East Anglia, UK will be recruited to the study, stratified into one of three risk groups (cohorts) for developing dementia based on their cognitive function. Participants will attend study appointments every six months over four years, providing stool and blood samples and a health questionnaire. Participants will also undergo physical measurements and cognitive tests at alternating appointments, and undergo Optical Coherence Tomography scans at 3 timepoints. Two subgroups of participants in the study will provide colonic tissue biopsies (n = ≥30 from each cohort), and brain imaging (n = 30) at two timepoints. Discussion: This study will provide new insights into the gut-(microbiota)-brain axis and the relationship between age-associated changes in gut microbe populations and cognitive health. Such insights could help develop new microbe-based strategies to improve lifelong health and wellbeing. Trial registration: This study is registered in the ClinicalTrials.gov Database with ID: NCT04199195 Registered: May 14, 2019. [ABSTRACT FROM AUTHOR]

In industrial paper production, online monitoring of a range of quality parameters is essential for ensuring that the performance and appearance of the final product is suitable for a given application. In this article, two optical sensing techniques are investigated for non-destructive, non-contact characterization of paper thickness, surface roughness, and production defects. The first technique is optical coherence tomography based on a mid-infrared supercontinuum laser, which can cover thicknesses from ~20–90 μm and provide information about the surface finish. Detection of subsurface voids, cuts, and oil contamination was also demonstrated. The second technique is terahertz time domain spectroscopy, which is used to measure paper thicknesses of up to 443 μm. A proof-of-concept thickness measurement in freely suspended paper was also demonstrated. These demonstrations highlight the added functionality and potential of tomographic optical sensing methods towards industrial non-contact quality monitoring. [ABSTRACT FROM AUTHOR]

This study aims to systematise subfoveal choroidal thickness (SFCT) measured using optical coherence tomography (OCT) in patients with different severities of thyroid eye disease (TED) compared with healthy subjects. The PubMed, Web of Science and Scopus databases were searched for the following terms: ((Graves' ophthalmopathy) OR (thyroid eye disease) OR (Graves' orbitopathy) OR (thyroid-associated orbitopathy)) AND (choroidal thickness) AND ((optical coherence tomography) OR (OCT)). The pool of papers was narrowed down to articles published until 31 January 2023 (26, 26 and 96 papers, respectively). Twenty-five (25) articles were taken into consideration, which were original papers and included the choroidal thickness measurements among TED patients in their results. Finally, eight papers were included in the comparative analysis of the SFCT parameter in TED patients and a group of healthy controls, and seven papers in the comparative analysis of the same parameter between active and inactive TED patients. The mean value of the difference between the TED group and the healthy group was 38.79 μm, with a confidence interval (CI) from 0.09 to 77.49 μm (p = 0.0495). The mean difference between the active TED group and inactive TED group was 38.02 μm, with a CI from 8.62 to 67.42 μm (p = 0.0113). All the results were statistically significant. [ABSTRACT FROM AUTHOR]

Medical imaging methods such as optical imaging have advanced significantly in their ability to diagnose and monitor various illnesses without any invasive procedures. Optical imaging offers a detailed view of anatomical structures and molecular processes, utilizing the unique properties of light and its interactions with biological tissues. This paper provides a comprehensive overview of optical imaging principles, including techniques like Optical Coherence Tomography (OCT), fluorescence imaging, and diffuse optical imaging. Optical imaging methods provide non-invasive, detailed views of biological tissues, enabling early disease detection and monitoring, thereby significantly advancing diagnostic capabilities and improving patient outcomes. These methods are used in the detection of diseases such as cancer, cardiovascular conditions, and neurological disorders. [ABSTRACT FROM AUTHOR]

Studying highly valuable and fragile Renaissance drawings requires a non-invasive contactless analytical approach. In this work, we study an early drawing by Raffaello Sanzio, one of the most important artists of the Italian Renaissance, realized during his stay in Florence (1504–1508). Our analyses aimed to reveal the features of the paper support significant for its dating, identify and map the drawing media to understand the artist's technical approach, and document the drawing condition with imaging and single-sited optical methods. Reflectance Vis–NIR multispectral imaging spectroscopy elaborated with False-Colour and Principal Component Analysis provided information about the paper support and the drawing media. Laser scanning micro-profilometry and Optical Coherence Tomography allowed revealing the superficial micro-scale features of the support. The chemical composition of the different drawing materials was characterized by μ-Raman spectroscopy, which provided also some evidence of the conservation history of the drawing. Integration of spectroscopic and imaging data shows that Raffaello used different dry drawing media (carbon-based and Pb stylus) to sketch the figure and then refined the details and shadows with iron-gall ink. The paper presents a methodological approach for the analytical examination of fragile paper artworks. [ABSTRACT FROM AUTHOR]

The Chinese-Russian Workshop on Biophotonics and Biomedical Optics 2023 brought together scientists, engineers, and clinical researchers from Russia and China to discuss the application of optical science, photonics, and imaging technologies in biology and medicine. The workshop featured two plenary lectures and twenty invited presentations, and this special issue of the Journal of Innovative Optical Health Sciences includes selected papers from the workshop. The papers cover a range of topics, including optical imaging techniques, monitoring cell behavior, diffuse reflectance spectroscopy, photodynamic therapy, and neural networks for biomedical applications. The issue provides readers with the latest developments in biophotonics and biomedical optics. [Extracted from the article]

The prevalence of myopia, a common refractive error causing blurred distant vision, has been steadily increasing. In more severe cases, myopia manifests as high myopia and pathological myopia, which can lead to irreversible vision impairment due to associated complications like retinal detachment and macular degeneration. High myopia and pathological myopia pose serious threats to visual health, necessitating accurate and early detection for effective intervention. This research focuses on leveraging Convolutional Neural Networks (CNNs) for the automated detection and classification of high myopia and pathological myopia from fundus images. CNNs have proven to be powerful tools in image analysis tasks, particularly in discerning intricate patterns and features. Fundus photographs and optical coherence tomography scans are employed to capture detailed anatomical structures associated with high myopia and pathological myopia. [ABSTRACT FROM AUTHOR]

Eye health has become a global health concern and attracted broad attention. Over the years, researchers have proposed many state‐of‐the‐art convolutional neural networks (CNNs) to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely. However, most existing methods were dedicated to constructing sophisticated CNNs, inevitably ignoring the trade‐off between performance and model complexity. To alleviate this paradox, this paper proposes a lightweight yet efficient network architecture, mixed‐decomposed convolutional network (MDNet), to recognise ocular diseases. In MDNet, we introduce a novel mixed‐decomposed depthwise convolution method, which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low‐resolution and high‐resolution patterns by using fewer computations and fewer parameters. We conduct extensive experiments on the clinical anterior segment optical coherence tomography (AS‐OCT), LAG, University of California San Diego, and CIFAR‐100 datasets. The results show our MDNet achieves a better trade‐off between the performance and model complexity than efficient CNNs including MobileNets and MixNets. Specifically, our MDNet outperforms MobileNets by 2.5% of accuracy by using 22% fewer parameters and 30% fewer computations on the AS‐OCT dataset. [ABSTRACT FROM AUTHOR]

More than 2 million implantable collamer lenses (ICLs) have been implanted worldwide. With a central port to improve aqueous flow through the ICL, the latest iteration of this phakic intraocular lens (pIOL) has been shown to have stable outcomes with very low rates of adverse events. However, correct planning and ICL size selection continue to be important to achieve an optimal vault. Shallow or excessive vaults are not complications in and of themselves but may increase the risk of complications. Historically, surgeons have relied on measurements of anterior chamber depth (ACD) and manual, caliper-measured white-to-white (WTW) distance to select the ICL size. New diagnostic and imaging technologies such as optical coherence tomography (OCT) and ultrasound biomicroscopy (UBM) provide additional opportunities for visualization and measurement of the intraocular dimensions involved in phakic intraocular lens implantation, including sulcus-to-sulcus (STS) and angle-to-angle (ATA) diameters. This paper reviews various approaches to ICL planning and sizing that have been published in the peer-reviewed literature, all of which produce acceptable results for predicting vault and size selection. Surgeons may also want to identify a methodology for patient evaluation and ICL size selection that best aligns with their personal preferences, diagnostic technology, and familiarity with analytical optimization tools. Plain Language Summary: Phakic intraocular lenses (pIOLs) are one method for correcting nearsightedness, with or without astigmatism. This category of refractive surgery has been growing rapidly in the US and around the world. Implantation of the implantable collamer lens (ICL), one type of pIOL, is safe and effective, with stable outcomes and low adverse event rates. When complications do occur, they are typically associated with an inappropriate vault, or distance between the implant and the natural lens. Preoperative planning and accurate ICL sizing are required to achieve an optimal vault and varies, depending on the diagnostic technology available to the surgeon. This paper reviews the current approaches to ICL planning and sizing in order to provide guidance to surgeons implanting this pIOL. [ABSTRACT FROM AUTHOR]

Purpose: Chemical eye injury is an acute emergency that can result in vision loss. Neurotrophic keratitis (NK) is the most common long-term manifestation of chemical injury. NK due to alkali burn affects ocular surface health and is one of its most common causes. Here, we established a rabbit model of corneal alkali burns to evaluate the severity of NK-associated changes. Material methods: Alkali burns were induced in NZ rabbits by treating the cornea with (i) a 5 mm circular filter paper soaked in 0.75 N NaOH for 10 s (Mild NK) and (ii) trephination using a guarded trephine (5 mm diameter and 150-micron depth), followed by alkali burn, with a 5 mm circular filter paper soaked in 0.75 N NaOH for 10 s (a severe form of NK). Immediately after, the cornea was rinsed with 10 mL of normal saline to remove traces of NaOH. Clinical features were evaluated on Day 0, Day 1, Day 7, Day 15, and Day 21 post-alkali burn using a slit lamp, Pentacam, and anterior segment optical coherence tomography (AS-OCT). NK-like changes in epithelium, sub-basal nerve plexus, and stroma were observed using in vivo confocal microscopy (IVCM), and corneal sensation were measured using an aesthesiometer post alkali injury. After 21 days, pro-inflammatory cytokines were evaluated for inflammation through ELISA. Results: Trephination followed by alkali burn resulted in the loss of epithelial layers (manifested using fluorescein stain), extensive edema, and increased corneal thickness (550 µm compared to 380 µm thickness of control) evaluated through AS-OCT and increased opacity score in alkali-treated rabbit (80 compared to 16 controls). IVCM images showed complete loss of nerve fibers, which failed to regenerate over 30 days, and loss of corneal sensation—conditions associated with NK. Cytokines evaluation of IL6, VEGF, and MMP9 indicated an increased angiogenic and pro-inflammatory milieu compared to the milder form of NK and the control. Discussion: Using clinical parameters, we demonstrated that the alkali-treated rabbit model depicts features of NK. Using IVCM in the NaOH burn animal model, we demonstrated a complete loss of nerve fibers with poor self-healing capability associated with sub-basal nerve degeneration and compromised corneal sensation. This pre-clinical rabbit model has implications for future pre-clinical research in neurotrophic keratitis. [ABSTRACT FROM AUTHOR]

This paper deals with the study of organic coatings (patinas) on historical bronzes, specifically those applied on small-size statues in Renaissance workshops. These coatings, often transparent and translucent, contain a mixture of organic and inorganic components and may be still preserved in hidden parts of statues in indoor displays. However, the complexity of the original varnishes, their degradation and alteration over time, and the coexistence of materials added for conservation and maintenance purposes are challenging for their characterization. The often well-preserved surface of varnished bronzes and their small size make it mandatory to make the most of using noninvasive techniques for their investigation. To this end, to simulate the actual historical coatings, we prepared a set of mock-ups following ancient recipes and using materials that were available in the Renaissance. We used the samples to assess to what extent it is possible to disclose the formulation (binders, colourants, and other additives) and the thickness of a Renaissance patina with noninvasive methods. Microprofilometry (MP), optical coherence tomography (OCT), and eddy current (EC) gauge were tested on the samples and the results were combined with reflectance Fourier transform infrared (FT-IR) spectroscopy. The analyses performed on the mock-ups set the ground for investigating a Renaissance bronze featuring reddish semi-transparent varnish layers. The achievements are discussed in this paper, along with the limitations of the use of a noninvasive approach. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) with high resolution was found lucrative for coronary heart disease diagnosis and treatment such as evaluating the degree of vascular stenosis and guiding stent implantation. Lumen contour is an essential information for assessing the shape and stenosis of blood vessels. The automatic and accurate extraction of lumen contour can significantly reduce the burden of doctors and improve the efficiency of diagnosis and treatment of coronary heart disease. The existing contour segmentation methods have poor performance in extracting the contour of the image with a missing blood vessel wall. Therefore, this paper proposes a two-stage contour extraction method based on deep learning. First, the method uses the basic contour extraction network to complete the feature image extraction and coarse contour segmentation, then inputs the feature image into the attentional class feature (ACF) module, and uses the coarse segmentation results to guide the generation of the new feature map for the refined contour segmentation. Finally, the fully connected conditional random field (CRF) module is used for post-processing yielding a more refined and smooth segmentation boundary. The experiments show that compared with the other methods, the lumen contour segmentation method proposed in this paper can adequately resolve the problem of partial vessel wall absence in OCT images and improve the accuracy of lumen contour extraction. [ABSTRACT FROM AUTHOR]

This breadth of scope is reflected by the papers included in this special issue, which touch topics including geometric Computer Vision, medical image processing, physical scene understanding, and interpretability of deep neural networks. A novel resampling-based uncertainty estimator enables uncertainty estimation under non-ideal conditions and thereby extends the classical covariance estimator. This special issue covers a wide range of topics from the area of Computer Vision, Pattern Recognition, and Machine Learning. [Extracted from the article]

Diabetic retinopathy (DR) is a common complication of diabetes which may lead to blindness. Early diagnosis can effectively prevent the deterioration of the disease and enable timely treatment. Ophthalmologists diagnose DR by observing ultra-wide optical coherence tomography angiography (UW-OCTA) images, which visualize unprecedented detail of DR lesions. In this paper, we propose an end-to-end task-specific network (TSNet) for joint DR grading and lesion segmentation of UW-OCTA images. Specifically, we design task-specific attention block to generate task-specific feature maps for respective segmentation and classification tasks. Furthermore, we devise task-specific fusion block to fuse the original task-specific feature map and augmented task-specific feature map for the following segmentation and classification decoders to generate DR lesion predictive mask and DR grading predictive result. Experiments on a public-available UW-OCTA dataset demonstrate that our model outperforms state-of-the-art (SOTA) multi-task models and achieves promising results on both DR lesion segmentation and DR grading classification tasks [ABSTRACT FROM AUTHOR]

This paper explores the advancements and achievements of artificial intelligence (AI) in computer vision (CV), particularly in the context of diagnosing and grading age-related macular degeneration (AMD), one of the most common leading causes of blindness and low vision that impact millions of patients globally. Integrating AI in biomedical engineering and healthcare has significantly enhanced the understanding and development of the CV application to mimic human problem-solving abilities. By leveraging AI-based models, ophthalmologists can improve the accuracy and speed of disease diagnosis, enabling early treatment and mitigating the severity of the conditions. This paper presents a comprehensive analysis of many studies on AMD published between 2014 and 2024, with more than 80% published after 2020. Various methodologies and techniques are examined, particularly emphasizing utilizing different retinal imaging modalities like color fundus photography and optical coherence tomography (OCT), where 66% of the studies used OCT datasets. This review aims to compare the efficacy of these AI-based approaches, including machine learning and deep learning, in detecting and diagnosing different stages and grades of AMD based on the evaluation of different performance metrics using different private and public datasets. In addition, this paper introduces some suggested AI solutions for future work. [ABSTRACT FROM AUTHOR]

Background and Objective: Coronary artery disease remains a leading cause of mortality among individuals with cardiovascular conditions. The therapeutic use of bioresorbable vascular scaffolds (BVSs) through stent implantation is common, yet the effectiveness of current BVS segmentation techniques from Intravascular Optical Coherence Tomography (IVOCT) images is inadequate. Methods: This paper introduces an enhanced segmentation approach using a novel Wavelet-based U-shape network to address these challenges. We developed a Wavelet-based U-shape network that incorporates an Attention Gate (AG) and an Atrous Multi-scale Field Module (AMFM), designed to enhance the segmentation accuracy by improving the differentiation between the stent struts and the surrounding tissue. A unique wavelet fusion module mitigates the semantic gaps between different feature map branches, facilitating more effective feature integration. Results: Extensive experiments demonstrate that our model surpasses existing techniques in key metrics such as Dice coefficient, accuracy, sensitivity, and Intersection over Union (IoU), achieving scores of 85.10%, 99.77%, 86.93%, and 73.81%, respectively. The integration of AG, AMFM, and the fusion module played a crucial role in achieving these outcomes, indicating a significant enhancement in capturing detailed contextual information. Conclusion: The introduction of the Wavelet-based U-shape network marks a substantial improvement in the segmentation of BVSs in IVOCT images, suggesting potential benefits for clinical practices in coronary artery disease treatment. This approach may also be applicable to other intricate medical imaging segmentation tasks, indicating a broad scope for future research. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to evaluate macular microvascular changes and their correlation with visual outcomes after idiopathic epiretinal membrane (iERM) surgery. Methods: Forty-seven eyes operated for iERM were included in this retrospective case series. The foveal avascular zone (FAZ) area, and the vessel density (VD) in the superficial and the deep capillary plexus (SCP and DCP) were evaluated using optical coherence tomography angiography (OCTA). The association between the OCTA parameters and best-corrected visual acuity (BCVA) was examined preoperatively and postoperatively. Regression analyses were conducted to determine the potential predictive factors for visual recovery. Results: At baseline, the FAZ area in iERM eyes was significantly smaller than that in the control eyes (p < 0.001). iERM eyes also had a lower macular VD in both the SCP and the DCP (p < 0.001). Preoperative BCVA was negatively correlated with the FAZ area (r = −0.499, p < 0.001) and macular VD in the DCP (r = −0.422, p = 0.003). A negative correlation was also found between postoperative BCVA and macular VD in both the SCP (r = −0.394, p = 0.006) and the DCP (r = −0.569, p < 0.001). In the bivariate analyses, age, preoperative BCVA, iERM stage, and baseline macular VD in the SCP were significantly associated with BCVA at 6 months post-surgery. Multivariate regression analysis revealed that the preoperative BCVA was the only predictor of visual outcomes in iERM eyes (p < 0.001). Conclusions: Idiopathic epiretinal membrane (iERM) causes microvascular changes, including foveal avascular zone (FAZ) contraction and decreased macular vessel density (VD) in both the superficial capillary plexus (SCP) and the deep capillary plexus (DCP). These changes were significantly correlated with pre- and/or postoperative best-corrected visual acuity (BCVA). However, none of these alterations appeared to have prognostic value for visual outcomes in patients with iERM. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study is to describe and validate an optical-coherence-tomography (OCT)-based method to easily calculate specific volumes, addressing the limitations of current OCT software in automating volumetric analysis for specific entities in retinal pathologies. Methods: After manually drawing the specific entity on linear OCT scans using the calliper function and automated measurement of its area, the following formula was used for volumetric calculation: Volume [mm3] = ∑area [mm2] × OCT-scan distance [mm]. Retinal volume (RV) was measured by two independent observers in eyes with a normal foveal profile (NFP) and was compared with the automated measurements performed by the OCT software (Engineering GmbH, Heidelberg, Germany); the same process was repeated for the volume of the foveal cavity (FC) or foveoschisis (FS) in eyes with lamellar macular holes (LMHs). Power calculations were conducted to ensure adequate sample size. The measurements were re-acquired after six weeks. Intra- and inter-observer variability as well as comparison to automated RV calculations were analysed. Results: This study included a total of 62 eyes divided into two groups: the NFP (30 eyes) and LMH (32 eyes) groups. The Bland–Altman plots showed a high degree of agreement in both groups for inter-observer and intra-observer agreement. In addition, in the NFP group, a high degree of agreement was demonstrated between human observers and the OCT software (Spectralis). Conclusions: An easy, reliable, and widely applicable method to calculate volumes is described and validated in this paper, showing excellent inter- and intra-observer agreement, which can be applied to any entity requiring a specific study in the context of retinal pathologies. [ABSTRACT FROM AUTHOR]

Background: Line-field confocal optical coherence tomography is a novel technology able to reproduce a "virtual biopsy" of the skin. The aim of this review is to explore the application of line-field confocal optical coherence tomography (LC-OCT) in various skin diseases, covering skin cancers, inflammatory and infectious skin diseases, genetic diseases, cosmetic procedures, and less common disorders. Methods: Study selection was conducted based on LC-OCT and using pertinent MeSh terms, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines from inception to March 2024; to evaluate the quality and risk of bias of studies, Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used. Results: the search retrieved 154 papers according to the selection criteria; after removing publications by one or more of the exclusion criteria, a total of 96 studies were found to be suitable for the analysis. Conclusions: Increasing evidence supports the use of LC-OCT as an adjunctive diagnostic tool for the in vivo diagnosis of a variety of skin tumors. As this device can be considered a "bridge" between dermoscopy and histopathology, widening applications in numerous fields of clinical dermatology, including inflammatory skin disease treatment, presurgical mapping, cosmetic procedures, and monitoring of non-invasive therapies, have been explored. [ABSTRACT FROM AUTHOR]

In this paper, we design a multimodal visible/near-infrared photoacoustic microscopy and optical coherence tomography (VIS/NIR-PAM-OCT) system for imaging both retina and retinal pigment epithelium (RPE)/choroid complex layer. F127 and DSPE-PEG-cRGD encapsulated IR-1048 nanoparticles (FINPs) exhibiting absorption peak up to 1,064 nm were utilized as contrast agents to enhance NIR-PAM for in vivo imaging of fundus tissues. The fundus structure and vessels are clearly visualized by the multimodal imaging, and their parameters were quantitatively analyzed. NIR-PAM and OCT imaging of fundus were time-serially monitored over 60 min following the intravenous injection of FINPs into rats. The results indicated a 134 % increase in image signals in PAM at 1 min, along with an 8.23 % intensity enhancement in OCT. Moreover, laser-induced choroidal neovascularization (CNV) was specifically detected and accurately quantified using VIS/NIR-PAM-OCT. Lastly, FINPs demonstrated excellent biocompatibility in hematology analysis and pathology testing. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the evidence for alterations of blood flow, vascular and perfusion densities in the choroid, macula, peripapillary region, and the area surrounding the optic nerve head (ONH) in patients with thyroid-associated ophthalmopathy (TAO) based on changes of OCTA parameters. Methods: A systematic review of Pubmed, Google Scholar, Scopus, WOS, Cochrane, and Embase databases, including quality assessment of published studies, investigating the alterations of OCTA parameters in TAO patients was conducted. The outcomes of interest comprised changes of perfusion and vascular densities in radial peripapillary capillary (RPC), ONH, superficial and deep retinal layers (SRL and DRL), choriocapillaris (CC) flow, and the extent of the foveal avascular zone (FAZ). Results: From the total of 1253 articles obtained from the databases, the pool of papers was narrowed down to studies published until March 20th, 2024. Lastly, 42 studies were taken into consideration which contained the data regarding the alterations of OCTA parameters including choriocapillary vascular flow, vascular and perfusion densities of retinal microvasculature, SRL, and DRL, changes in macular all grid sessions, changes of foveal, perifoveal and parafoveal densities, macular whole image vessel density (m-wiVD) and FAZ, in addition to alterations of ONH and RPC whole image vessel densities (onh-wiVD and rpc-wiVD) among TAO patients. The correlation of these parameters with visual field-associated parameters, such as Best-corrected visual acuity (BCVA), Visual field mean defect (VF-MD), axial length (AL), P100 amplitude, and latency, was also evaluated among TAO patients. Conclusion: The application of OCTA has proven helpful in distinguishing active and inactive TAO patients, as well as differentiation of patients with or without DON, indicating the potential promising role of some OCTA measures for early detection of TAO with high sensitivity and specificity in addition to preventing the irreversible outcomes of TAO. OCTA assessments have also been applied to evaluate the effectiveness of TAO treatment approaches, including systemic corticosteroid therapy and surgical decompression. [ABSTRACT FROM AUTHOR]

ObjectiveMethodsResultsConclusionTo dynamically track the publications on central serous chorioretinopathy (CSC) and depict the research status and hot spots to guide future research.Gather all papers published in this area between 2004 and 2024 in the WOSCC databases comprehensively, assess their trends, and characterize the contributions of various nations, authors, institutions, and journals. In addition, VOSviewer, CiteSpace, and R software are used to obtain the most popular keywords for the topic.A total of 2,203 papers were published across 1,863 institutions in 59 countries. Among these, 6,907 authors contributed to publications in 300 journals and generated a total of 35,638 citations. The number of publications continues to grow steadily. Notably, Jay Chhablani’s team/Lab stands out as the leading contributor with ownership of 84 publications. Through keyword network analysis and clustering techniques, risk factor-related clustering, imaging-related clustering, pathogenesis-related clustering, and treatment-related clustering were identified. Furthermore, keyword analysis has unveiled emerging frontier areas including pachychoroid disease, choroidal vasculature abnormalities, PDT therapy, and optical coherence tomography that have garnered increasing interest.This study presents a comprehensive review of central serous retinopathy research conducted in the past two decades, highlighting key trends and exploring emerging research frontiers within this field. As such, it provides valuable references and suggestions for researchers engaged in studying this topic. [ABSTRACT FROM AUTHOR]

Objectives. Alport syndrome (AS) is a severe, rare hereditary disorder that can lead to end-stage renal disease, auditory degeneration, and ocular abnormalities. Despite extensive research on AS in relation to auditory and renal disorders, more research is needed on the ocular presentations of AS. This systematic review aims to summarize the common ocular abnormalities in patients with AS and to explore the potential treatment options for these irregularities. Methods. The PubMed, MEDLINE, and EMBASE databases were systematically searched from January 1977 to April 2022. Only papers that were published in the English language and explored the ocular abnormalities in AS patients were selected. We manually searched reference lists of included papers for additional studies. Results. A total of 23 articles involving 195 patients were included in this review. The common ocular manifestations in AS patients are lenticonus, macular holes, fleck retinopathy, and thinning of the macula. Although published literature has described the use of cataract surgeries and vitrectomies as standard surgical techniques to alleviate ocular abnormalities in non-AS patients, it must be noted that surgical techniques have not been evaluated in a large research study as a solution for AS abnormalities. Another prospective treatment for AS is gene therapy through the reversion of causative COL4 variants to wild type or exon-skipping therapy for X-linked AS with COL4A5 truncating mutations. Gene therapy, however, remains unable to treat alterations that occur in the fetal and early development phase of the disease. Conclusions. The review found no definitive conclusions regarding the efficacy and safety of surgical techniques and gene therapy in AS patients. Recognition of ocular abnormalities through an ophthalmic examination with an optical coherence tomography (OCT) and slit-lamp examination is critical to the medical field, as ophthalmologists can aid nephrologists and other physicians in diagnosing AS. Early diagnosis and care can minimize the risk of detrimental ocular outcomes, such as blindness and retinal detachment. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) attenuation imaging is a technique that uses the optical attenuation coefficient (OAC) to distinguish the types or pathological states of tissues and has been increasingly used in basic research and clinical diagnosis. With the increasing application of swept-source OCT, scholars are increasingly inclined to explore deep tissues. Unfortunately, the accuracy of OAC calculation when exploring deep tissues has yet to be improved. Existing methods generally have the following problems: overestimation error, underestimation error, severe fluctuation, or stripe artifacts in the OAC calculation of the OCT tail signal. The main reason for this is that the influence of the noise floor on the OCT weak signal at the tail-end is not paid enough attention. The noise floor can change the attenuation pattern of the OCT tail signal, which can lead to severe errors in the OAC. In this paper, we proposed a Kalman filter-based OAC optimal algorithm to solve this problem. This algorithm can not only eliminate the influence of the noise floor, but can also effectively protect the weak signal at the tail-end from being lost. The OAC of deep tissues can be calculated accurately and stably. Numerical simulation, phantom, and in vivo experiments were tested to verify the algorithm's effectiveness in this paper. This technology is expected to play an essential role in disease diagnosis and in the evaluation of the effectiveness of treatment methods. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases. [ABSTRACT FROM AUTHOR]

Until recently, the study of drawings by old masters has been confined to the art history conservation field. More specifically, scientific investigations of Leonardo's drawings are still very few, possibly due to the latter's extreme fragility and artistic value. However, analytical data are crucial to develop a solid knowledge base of the drawing materials and techniques used by artists in the past. In this work, we report on the application of non-invasive optical techniques on a double-sided drawing by Leonardo belonging to the Uffizi Gallery (8P). We used multispectral reflectography in the visible (Vis) and near-infrared (NIR) regions to obtain a spectral mapping of the drawing materials, to be subsequently integrated with technical information provided by art historians and conservators. Morphological analysis by microprofilometry allowed for the identification of the typical wave-like texture impressed in the paper during the sheet's manufacture, as well as of further paper-impressed traits attributable to the drawing transfer method used by Leonardo. Optical coherence tomography revealed a set of micrometric engraved details in the blank background, which lack any trace of colored material, nor display any apparent relation to the drawn landscape. The disclosure of hidden technical features allowed us to offer new insights into Leonardo's still under-investigated graphic practices. [ABSTRACT FROM AUTHOR]

This paper presents new perspectives on photonic technologies for capsule endoscopy. It first presents a review of conventional endoscopy (upper endoscopy and colonoscopy), followed by capsule endoscopy (CE), as well as their techniques, advantages, and drawbacks. The technologies for CEs presented in this paper include integration with the existing endoscopic systems that are commercially available. Such technologies include narrow-band imaging (NBI), photodynamic therapy (PDT), confocal laser endomicroscopy (CLE), optical coherence tomography (OCT), and spectroscopy in order to improve the performance of the gastrointestinal (GI) tract examination. In the context of NBI, two optical filters were designed and fabricated for integration into endoscopic capsules, allowing for the visualization of light centered at the 415 nm and 540 nm wavelengths. These optical filters are based on the principle of Fabry-Perot and were made of thin films of titanium dioxide (TiO2) and silicon dioxide (SiO2). Moreover, strategies and solutions for the adaptation of ECs for PDT are also discussed. [ABSTRACT FROM AUTHOR]

Failure to recognize the different possible clinical presentations of ocular toxoplasmosis may delay diagnosis and treatment, compromising visual prognosis. The aim of this paper is to describe an atypical pattern of ocular toxoplasmosis, not yet described. Five Brazilian patients, from 4 different referral centers, presented similar atypical pattern of ocular toxoplasmosis characterized by mild vitritis, foveal cavitation involving predominantly all retinal layers associated with adjacent inner retinal necrosis (a necrotizing retinitis with a persisting inner retinal tissue bridge and loss of subjacent retinal layers). The appearance of the OCT image resembling a "rift", led the authors to define this pattern as a Recurrent Inner Foveal Toxoplasmic Retinitis (RIFTER), which can be considered as a new description of an atypical pattern of toxoplasma retinochoroiditis, and clinicians should be aware of it and consider testing for toxoplasmosis in patients with similar findings. [ABSTRACT FROM AUTHOR]

Manual detection of eye diseases using retina Optical Coherence Tomography (OCT) images by Ophthalmologists is time consuming, prone to errors and tedious. Previous researchers have developed a computer aided system using deep learning-based convolutional neural networks (CNNs) to aid in faster detection of the retina diseases. However, these methods find it difficult to achieve better classification performance due to noise in the OCT image. Moreover, the pooling operations in CNN reduce resolution of the image that limits the performance of the model. The contributions of the paper are in two folds. Firstly, this paper makes a comprehensive literature review to establish current-state-of-act methods successfully implemented in retina OCT image classifications. Additionally, this paper proposes a capsule network coupled with contrast limited adaptive histogram equalization (CLAHE-CapsNet) for retina OCT image classification. The CLAHE was implemented as layers to minimize the noise in the retina image for better performance of the model. A three-layer convolutional capsule network was designed with carefully chosen hyperparameters. The dataset used for this study was presented by University of California San Diego (UCSD). The dataset consists of 84,495 X-Ray images (JPEG) and 4 categories (NORMAL, CNV, DME, and DRUSEN). The images went through a grading system consisting of multiple layers of trained graders of expertise for verification and correction of image labels. Evaluation experiments were conducted and comparison of results was done with state-of-the-art models to find out the best performing model. The evaluation metrics; accuracy, sensitivity, precision, specificity, and AUC are used to determine the performance of the models. The evaluation results show that the proposed model achieves the best performing model of accuracies of 97.7%, 99.5%, and 99.3% on overall accuracy (OA), overall sensitivity (OS), and overall precision (OP), respectively. The results obtained indicate that the proposed model can be adopted and implemented to help ophthalmologists in detecting retina OCT diseases. [ABSTRACT FROM AUTHOR]

To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a comprehensive comparison with the literature. The accuracies of classifying the OCT images of the OCTID dataset into two and five classes were 100 % and 82.5 % , respectively. We achieved an accuracy of 96.6 % in detecting Diabetic Macular Edema from Normal ones using the TOPCON device-based dataset. Heidelberg and Duke datasets contain DME, Age-related Macular Degeneration, and Normal classes, in which we achieved accuracy of 97.1 % and 94.4 % , respectively. A comparison of our results with the state-of-the-art models shows that our model outperforms these models for some assessments or achieves nearly the best results reported so far while having a much smaller computational complexity. [ABSTRACT FROM AUTHOR]

Optical Coherence Tomography (OCT) is an optical imaging technology occupying a unique position in the resolution vs. imaging depth spectrum. It is already well established in the field of ophthalmology, and its application in other fields of medicine is growing. This is motivated by the fact that OCT is a real-time sensing technology with high sensitivity to precancerous lesions in epithelial tissues, which can be exploited to provide valuable information to clinicians. In the prospective case of OCT-guided endoscopic laser surgery, these real-time data will be used to assist surgeons in challenging endoscopic procedures in which high-power lasers are used to eradicate diseases. The combination of OCT and laser is expected to enhance the detection of tumors, the identification of tumor margins, and ensure total disease eradication while avoiding damage to healthy tissue and critical anatomical structures. Therefore, OCT-guided endoscopic laser surgery is an important nascent research area. This paper aims to contribute to this field with a comprehensive review of state-of-the-art technologies that may be exploited as the building blocks for achieving such a system. The paper begins with a review of the principles and technical details of endoscopic OCT, highlighting challenges and proposed solutions. Then, once the state of the art of the base imaging technology is outlined, the new OCT-guided endoscopic laser surgery frontier is reviewed. Finally, the paper concludes with a discussion on the constraints, benefits and open challenges associated with this new type of surgical technology. [ABSTRACT FROM AUTHOR]

Among various eye diseases, glaucoma is one of the leading causes of blindness. Glaucoma is also one of the most common eye diseases in Taiwan. Glaucoma screenings can use optical coherence tomography (OCT) to locate areas in which the retinal nerve fiber layer is thinning. However, because OCT equipment is costly, only large hospitals with well-equipped facilities will have OCT, and regular eye clinics cannot afford such expensive equipment. This has caused many glaucoma patients to worsen because they cannot get an early diagnosis in regular eye clinics in time. This paper proposes a method of using a generative adversarial network (GAN) to generate corresponding OCT images from fundus images to assist family doctors in judging whether further examination is needed based on the generated OCT images to achieve early detection and treatment of glaucoma. In addition, in order to improve the classification accuracy of the system deployed in different hospitals or clinics, this paper also proposes to use the incremental training method to fine-tune the model. The model can be quickly applied by adding a small number of images from a specific clinic or hospital. Experimental results show that the cosine similarity between the generated OCT image and the real OCT image is 97.8%. Combined with the proposed transfer learning method, the classification accuracy of the classification model reaches 83.17%. As well as the use of the incremental method, the accuracy of identifying glaucoma is approximately 78.94%, which is 8.77% higher than the 70.17% accuracy of the initial model. Experimental results show the effectiveness and feasibility of our proposed method. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to assess the time impact of ARGOS® (image-guided swept-source optical coherence tomography biometer integrated with operating room (OR) technologies (SS-OCT w/ORT)) compared to LENSTAR LS 900 (optical low-coherence reflectometry (OLCR)), IOLMaster 500 (partial coherence interferometry (PCI)), and IOLMaster 700 (SS-OCT) on efficiency in the cataract evaluation and surgery. Patients and Methods: Data from 212 patients (two study sites) who underwent evaluation and/or cataract surgery were collected. The primary objective was to compare the performance of four biometers; statistical analyses were conducted to compare 1) biometer measurement times for all patients (ANOVA w/post-hoc Dunnett's test) and stratified by cataract density (ANOVA) and 2) rate of biometer acquisition failure (Chi-square test w/post-hoc Bonferroni correction). Real-world observational data collected were then used to develop a practice-based time-efficiency model to demonstrate the combined effect that adopting an SS-OCT w/ORT has on a practice's cataract workflow. Real-world data inputs included assessment of patient's eyes' cataract grade density, time taken for optical biometry, Manual A-scan (ultrasound biometer) when acquisition failed, and measurement times associated with other devices used in cataract evaluation and surgery. Results: For 208 patients (56% non-dense, 44% dense), the SS-OCT w/ORT biometer had a 0% acquisition failure (SS-OCT: 3% (p = 0.05); OLCR: 5% (p = 0.004); PCI: 15% (p < 0.0001)) and an average time savings of 30 seconds/patient compared to the other biometers in this study (p < 0.05). When acquisition failed, ultrasound biometry resulted in an additional 2.5 minutes/patient. For a cohort of 1000 patients, an SS-OCT w/ORT and an image-guidance system adopted at a practice using an SS-OCT, femtosecond laser, and intraoperative aberrometer offer up to 58% efficiency gain across the cataract workflow. Conclusion: Results from this study demonstrate an SS-OCT w/ORT's efficiencies in cataract evaluation and surgery driven by faster measurement times, reducing the need for ultrasound biometry, and its integration benefits with other devices. [ABSTRACT FROM AUTHOR]

Diabetic macular edema (DME) is a typical fundus disease that can cause blindness in severe cases. The morphology of the inner limiting membrane (ILM) to the retinal pigment epithelium (RPE) layer in the retina and the macular edema (ME) area are important features for the diagnosis of DME. Doctors usually use non‐invasive and high‐resolution optical coherence tomography (OCT) to examine the fundus of the patient. However, manual diagnosis has low efficiency and strong subjectivity. Realizing the automatic segmentation of the ILM‐RPE layer and ME is extremely important for the early diagnosis of DME. In this paper, the attention mechanism based on residual convolution module U‐Net (RCU‐Net) is proposed for the automatic segmentation of the retinal layer and cystoid edema lesions. Through the fusion of the residual structure and CBAM for feature extraction, the useful features in the channel and space are effectively strengthened, and the network can better learn different levels of information. The proposed network is combined with the Lovász‐softmax loss, which can better target the correlation between targets to obtain the optimal segmentation model during training. Finally, this paper compares the proposed method with several other segmentation methods. The experimental results show that the MIoU of the method in this model reaches 88.595%, and the Accuracy reaches 99.171%. The RCU‐Net proposed in this paper is used to segment the ILM‐RPE layer and ME region in the retina OCT B‐scan images, and its overall performance is better than other networks. [ABSTRACT FROM AUTHOR]

Retinal image registration is a critical task in the diagnosis and treatment of various eye diseases. And as a relatively new imaging method, optical coherence tomography (OCT) has been widely used in the diagnosis of retinal diseases. This paper is devoted to retinal OCT image registration methods and their clinical applications. Registration methods including volumetric transformation-based registration methods and image features-based registration methods are systematically reviewed. Furthermore, to better understanding these methods, their applications in correcting scanning artifacts, reducing speckle noise, fusing and splicing images and evaluating longitudinal disease progression are studied as well. At the end of this paper, registration of retina with serious pathology and registration with deep learning technique are also discussed. [ABSTRACT FROM AUTHOR]

Accurate retinal fluid segmentation on Optical Coherence Tomography (OCT) images plays an important role in diagnosing and treating various eye diseases. The art deep models have shown promising performance on OCT image segmentation given pixel-wise annotated training data. However, the learned model will achieve poor performance on OCT images that are obtained from different devices (domains) due to the domain shift issue. This problem largely limits the real-world application of OCT image segmentation since the types of devices usually are different in each hospital. In this paper, we study the task of cross-domain OCT fluid segmentation, where we are given a labeled dataset of the source device (domain) and an unlabeled dataset of the target device (domain). The goal is to learn a model that can perform well on the target domain. To solve this problem, in this paper, we propose a novel Structure-guided Cross-Attention Network (SCAN), which leverages the retinal layer structure to facilitate domain alignment. Our SCAN is inspired by the fact that the retinal layer structure is robust to domains and can reflect regions that are important to fluid segmentation. In light of this, we build our SCAN in a multi-task manner by jointly learning the retinal structure prediction and fluid segmentation. To exploit the mutual benefit between layer structure and fluid segmentation, we further introduce a cross-attention module to measure the correlation between the layer-specific feature and the fluid-specific feature encouraging the model to concentrate on highly relative regions during domain alignment. Moreover, an adaptation difficulty map is evaluated based on the retinal structure predictions from different domains, which enforces the model focus on hard regions during structure-aware adversarial learning. Extensive experiments on the three domains of the RETOUCH dataset demonstrate the effectiveness of the proposed method and show that our approach produces state-of-the-art performance on cross-domain OCT fluid segmentation. [ABSTRACT FROM AUTHOR]