Leading edge of the a-wave of the electroretinogram and sodium iodate-induced age-related macular degeneration: A model.

BACKGROUND: Iron-induced oxidative stress was thought to be the reason why the a-wave amplitude of the electroretinogram (ERG) dropped when iron ions were present. It is assumed that reactive oxygen species (ROS) are generated in the presence of iron ions, and this leads to a decrease in hyperpolarization of the photoreceptor. It is known that in age-related macular degeneration (AMD), sodium iodate can induce oxidative stress, apoptosis, and retinal damage, which mimic the effects of clinical AMD. Here, the reduction of the a-wave amplitude in mice with sodium iodate-induced age-related macular degeneration is explained. METHODS: The leading edge of the a-wave is divided into voltages developed by cones and rods. The same oxidative stress model is applied here since sodium iodate causes the creation of ROS in a manner similar to that caused by iron ions, with the exception that the retina is treated as a circuit of various resistances when computing the photoresponse. Moreover, sodium iodate also leads to apoptosis and, hence, may cause misalignment in cones (not in rods) during the initial stage of apoptosis in AMD. To include the effects of apoptosis and shortening in cones and rods, we have used a factor representing the fraction of total cones and rods that are alive. To include the effect of misalignment of cones on the reduction of the a-wave amplitude, we have used the Stiles-Crawford function to calculate the number of photoisomerizations occurring in a photoreceptor misaligned at an angle θ. The results are compared with experimental data. RESULTS: In sodium iodate-treated eyes, the ROS produced can attract calcium ions in the photoreceptor, which increases the calcium influx. In the case of the cones, the inclusion of the misalignment angle in the phototransduction process helps in determining the voltage and slope of the voltage vs. time graph.The smaller the fraction of active photoreceptors, the smaller the amplitude of the a-wave. The calcium influx, misaligned photoreceptors, and total photoreceptor loss all cause the amplitude of the a-wave to decrease, and at any time from the beginning of phototransduction cascade, the calcium influx causes the slope of the a-wave to increase. CONCLUSION: The reduction in the a-wave amplitude in the eyes of sodium iodate-treated mice is attributed to oxidative stress in both cones and rods and cone misalignment, which ultimately lead to apoptosis and vision loss in AMD.

Gelation Kinetics-Structure Analysis of pH-triggered Low Molecular Weight Hydrogelators.

Properties such as shear modulus, gelation time, structure of supramolecular hydrogels are strongly dependent on self-assembly, gelation triggering mechanism and processes used to form the gel. In our work we extend reported rheology analysis methodologies to pH-triggered supramolecular gels to understand structural insight using a model system based on N-N' Dibenzoyl-L-Cystine pH-triggered hydrogelator and Glucono-δ-Lactone as the trigger. We observed that Avrami growth model when applied to time-sweep rheological data of gels formed at lower trigger concentrations provide estimates of fractal dimension which agree well compared with visualization of the microstructure as seen via Confocal Laser Scanning Microscopy, for a range of gelator concentrations.

Rapid classification of glaucomatous fundus images.

We propose a new method for training convolutional neural networks (CNNs) and use it to classify glaucoma from fundus images. This method integrates reinforcement learning along with supervised learning and uses it for transfer learning. The training method uses hill climbing techniques via two different climber types, namely, "random movement" and "random detection," integrated with a supervised learning model through a stochastic gradient descent with momentum model. The model was trained and tested using the Drishti-GS and RIM-ONE-r2 datasets having glaucomatous and normal fundus images. The performance for prediction was tested by transfer learning on five CNN architectures, namely, GoogLeNet, DenseNet-201, NASNet, VGG-19, and Inception-Resnet v2. A five-fold classification was used for evaluating the performance, and high sensitivities while maintaining high accuracies were achieved. Of the models tested, the DenseNet-201 architecture performed the best in terms of sensitivity and area under the curve. This method of training allows transfer learning on small datasets and can be applied for tele-ophthalmology applications including training with local datasets.

The [Formula: see text]-Wave of the Electroretinogram and Iron-Induced Oxidative Stress: A Model.

In photoreceptors of a dark adapted eye, the inward flux of sodium and calcium ions in the outer segment is balanced by the outward flux of potassium ions. But in the presence of light the creation of cyclic guanosine monophosphate in the outer segment decreases. Due to low concentration of cG (cyclic GMP) the channels in the outer segment open relatively less and thus the influx of calcium ion decreases, leading finally to hyperpolarization of the photoreceptors. We have analyzed theoretically the effect of oxidizing iron ions on the photoreceptors. In order to explain the effects of iron-induced oxidative stress, the different molecules and ions involved in phototransduction are quantified leading to a differential equation for calculating the electroretinogram a-wave voltage. The theoretical results are compared with published experimental data. In the presence of light, the iron ions could push outward the similarly charged calcium ions resulting in a small increase in the amount of inward calcium flux. Again, the presence of iron ions generates Reactive Oxygen Species, and ROS could attract the calcium ions which also increases the calcium flux. This will result in a reduction in the amplitude and slope of the a-wave voltage in the electroretinogram. These results are parametrized in terms of calcium ion concentrations. As the amplitude of the a-wave shows how much electrical signal is produced, its reduction indicates reduction in the visual signal. Thus, the increase in iron ions could explain the reduction in the electrical signal due to iron-induced oxidative stress.

Agreement among ophthalmologists in marking the optic disc and optic cup in fundus images.

The purpose of this paper was to study the agreement between six ophthalmologists who manually marked the optic nerve head using fundus images. Four different parameters were considered from the manual marking images: (1) disc (area and centroid), (2) cup (area and centroid), (3) horizontal and vertical cup-to-disc ratios, and (4) including the previous two parameters for both horizontal and vertical cup-to-disc ratios, and investigated the comprehensive agreement and accuracy among all the ophthalmologists. The best agreement percentage for all the parameters combined was between ophthalmologists number one and three for 44 % of images, and the best accuracy was for ophthalmologist number one with 77.4 % of 315 total tested images. Our analysis shows that more than half of the images in the dataset were not agreed upon when considering all the parameters together.

Holographic imaging through a scattering medium by diffuser-aided statistical averaging.

We introduce a practical digital holographic method capable of imaging through a diffusive or scattering medium. The method relies on statistical averaging from a rotating ground glass diffuser to negate the adverse effects caused by speckle introduced by a static diffuser or scattering medium. In particular, a setup based on Fourier transform holography is used to show that an image can be recovered after scattering by introducing an additional diffuser in the optical setup. This method is capable of recovering object information from behind a scattering layer in biomedical or military imaging applications.

Gelation Landscape Engineering Using a Multi-Reaction Supramolecular Hydrogelator System.

Simultaneous control of the kinetics and thermodynamics of two different types of covalent chemistry allows pathway selectivity in the formation of hydrogelating molecules from a complex reaction network. This can lead to a range of hydrogel materials with vastly different properties, starting from a set of simple starting compounds and reaction conditions. Chemical reaction between a trialdehyde and the tuberculosis drug isoniazid can form one, two, or three hydrazone connectivity products, meaning kinetic gelation pathways can be addressed. Simultaneously, thermodynamics control the formation of either a keto or an enol tautomer of the products, again resulting in vastly different materials. Overall, this shows that careful navigation of a reaction landscape using both kinetic and thermodynamic selectivity can be used to control material selection from a complex reaction network.

Pupil scaling for the estimation of aberrations in natural pupils.

PURPOSE: This study aimed to validate the mathematical Zernike pupil size scaling from bigger pupils to smaller pupils, and vice versa, by comparing the estimates of the Zernike coefficients with corresponding clinical measurements obtained at different pupil sizes. METHODS: The i.Profiler Plus (Carl Zeiss Vision, Inc, USA) was used to obtain measures of wavefront aberrations for two pupil sizes (3 mm and the maximum natural pupil size) from the right eyes of 28 visually normal subjects (mean [±SD] age, 57 [±7] years) whose maximum pupil size was greater than or equal to 5 mm without pharmacological dilation. Zernike coefficients were estimated for a 3-mm pupil size scaling down from the measured data of the maximum natural pupil size and, similarly, for the maximum pupil size scaling up from the measured data of the 3-mm pupil. RESULTS: The differences between the estimated and measured values were not significantly different (repeated-measures analysis of variance; p > 0.05) over the range of pupil sizes examined, irrespective of whether the estimates were made by scaling up from a small pupil or scaling down from a large pupil. However, the difference between the measured and estimated coefficients was more variable and less systematic when scaling to a larger pupil size when compared with scaling to a smaller pupil size. CONCLUSIONS: Estimation of ocular wavefront aberration coefficients either scaling down from large to smaller pupils or scaling up from smaller to large pupils provides estimates that are not significantly different from clinically measured values. However, when scaling up to a larger pupil size, the estimates are more variable. These findings have implications for pupil scaling on an individual basis, such as in cases of refractive surgery or when using pupil scaling to examine a clinical cohort.

BraNet: a mobil application for breast image classification based on deep learning algorithms.

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named "BraNet" for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client-server architecture and was implemented in Python for iOS and Android devices. Then, two diagnostic radiologists were given a reading test of 290 total original RoI images to assign the perceived breast tissue type. The reader's agreement was assessed using the kappa coefficient. The BraNet App Mobil exhibited the highest accuracy in benign and malignant US images (94.7%/93.6%) classification compared to DM during training I (80.9%/76.9%) and training II (73.7/72.3%). The information contrasts with radiological experts' accuracy, with DM classification being 29%, concerning US 70% for both readers, because they achieved a higher accuracy in US ROI classification than DM images. The kappa value indicates a fair agreement (0.3) for DM images and moderate agreement (0.4) for US images in both readers. It means that not only the amount of data is essential in training deep learning algorithms. Also, it is vital to consider the variety of abnormalities, especially in the mammography data, where several BI-RADS categories are present (microcalcifications, nodules, mass, asymmetry, and dense breasts) and can affect the API accuracy model.

Are the fluctuations in dynamic anterior surface aberrations of the human eye chaotic?

The purpose of the study is to measure chaos in dynamic anterior surface aberrations and examine how it varies between the eyes of an individual. Noninvasive tear breakup time and dynamic corneal surface aberrations were measured for two open-eye intervals of 15 s. The maximal Lyapunov exponent (MLE) was calculated to test the nature of the fluctuations of the dynamic anterior surface aberrations. The average MLE for total higher-order aberration (HOA) was found to be small (+0.0102±0.0072) µm/s. No significant difference in MLE was found between the eyes for HOA (t-test; p=0.131). Data analysis was carried out for individual Zernike coefficients, including vertical prism as it gives a direct measure of the thickness of the tear film over time. The results show that the amount of chaos was small for each Zernike coefficient and not significantly correlated between the eyes.

OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution.

There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the Red, Green and Blue (RGB) value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images.

Outcome of Ivermectin in Cancer Treatment: An Experience in Loja-Ecuador.

(1) Background: Cancer is one of the leading causes of death worldwide, and trends in cancer incidence and mortality are increasing over last years in Loja-Ecuador. Cancer treatment is expensive because of social and economic issues which force the patients to look for other alternatives. One such alternative treatment is ivermectin-based antiparasitic, which is commonly used in treating cattle. This paper analyzed ivermectin use as cancer treatment in the rural area of the Loja province and the medical opinion regarding the use of ivermectin in humans. (2) Methods: The study used a mixed methodology using different sampling techniques such as observation, surveys, and interviews. (3) Results: The main findings show that 19% of the participants diagnosed with cancer take medicines based on ivermectin as alternative therapy to the cancer control and treatment without leaving treatment such as chemotherapy, radiotherapy, or immunotherapy, while 81% use it to treat other diseases. (4) Conclusions: Finally, we identify that the interviewed not only use IVM as anticancer treatment, but it is also used as a treatment against other diseases. Although the participants' opinions indicate that they feel improvements in their health after the third dose, the specialist considers that there is no authorization to prescribe these alternative treatments. In addition, they confirmed that currently, there is no scientific knowledge about the application of these treatments in humans and they do not recommend their application. Thus, the anticancer mechanism of ivermectin remains to be further investigated; therefore, we consider that it is important to continue with this research by proposing a new stage to evaluate and determine the pharmacological action of this type of drug through an in vitro study in different cultures of cancer cells.

Preliminary Report on Optical Coherence Tomography Angiography Biomarkers in Non-Responders and Responders to Intravitreal Anti-VEGF Injection for Diabetic Macular Oedema.

PURPOSE: To identify optical coherence tomography angiography (OCTA) biomarkers in patients who were treated for diabetic macular oedema (DME) with intravitreal anti-vascular endothelial growth factor (VEGF) injections and compare the OCTA parameters between responders and non-responders. METHODS: A retrospective cohort study of 61 eyes with DME who received at least one intravitreal anti-VEGF injection was included between July 2017 and October 2020. The subjects underwent a comprehensive eye examination followed by an OCTA examination before and after intravitreal anti-VEGF injection. Demographic data, visual acuity, and OCTA parameters were documented, and further analysis was performed pre- and post-intravitreal anti-VEGF injection. RESULTS: Out of 61 eyes which underwent intravitreal anti-VEGF injection for diabetic macular oedema, 30 were responders (group 1) and 31 were non-responders (group 2). We found that the responders (group 1) had a higher vessel density in the outer ring that was statistically significant (p = 0.022), and higher perfusion density was noted in the outer ring (p = 0.012) and full ring (p = 0.044) at levels of the superficial capillary plexus (SCP). We also observed a lower vessel diameter index in the deep capillary plexus (DCP) in responders when compared to non-responders (p < 0.00). CONCLUSION: The evaluation of SCP in OCTA in addition to DCP can result in a better prediction of treatment response and early management in diabetic macular oedema.

Frontiers in Retinal Image Processing.

Visual impairment is considered as a primary global challenge in the present era [...].

Relationship Between Posterior Vitreous Detachment and Macular Dimensions in Myopic Eyes.

Background: Posterior vitreous detachment (PVD) can result in serious pathologic events at the vitreoretinal interface. This study aims to assess the relationship between PVD, macular thickness (MT), and the foveal avascular zone (FAZ) in myopic eyes. Methods: This retrospective study evaluated 63 myopic subjects' data who were examined between January 1 and June 30, 2019. The myopes were grouped on a severity scale, namely mild, moderate, high, and very high using their spherical equivalent (SE). The PVD classification was based on OCT images. The status of the PVD and MT were evaluated with the Macular Cube 200×200 images, the FAZ with an OCTA Angioplex, and the dimensions were calculated using a customized algorithm. Results: The study population (114 eyes) had a median (range) age was 26.00 (22.00-28.00) years and the females constituted 62.3% of the dataset. In this population, 10 eyes had no PVD in any quadrant, and 73 eyes had incomplete PVD in all four quadrants. The inferior quadrant had the maximum rate of PVD occurrences and the nasal quadrant had the least number of occurrences. High myopic eyes exhibited significantly increased low foveal volume (p = <0.01). The inferior part of the para- and perimacular area showed a significant thinning in very high myopic eyes (p = <0.050). The very high myopic eyes showed a significant alteration of FAZ's circularity index (p = 0.002). Conclusion: In high and very high myopic eyes, an increasing trend of partial PVD is seen, most commonly in the inferior quadrant. A significant alteration in foveal volume and circularity index of the FAZ is seen in high and very high myopic eyes.

Detection and Classification of Knee Osteoarthritis.

Osteoarthritis (OA) affects nearly 240 million people worldwide. Knee OA is the most common type of arthritis, especially in older adults. Physicians measure the severity of knee OA according to the Kellgren and Lawrence (KL) scale through visual inspection of X-ray or MR images. We propose a semi-automatic CADx model based on Deep Siamese convolutional neural networks and a fine-tuned ResNet-34 to simultaneously detect OA lesions in the two knees according to the KL scale. The training was done using a public dataset, whereas the validations were performed with a private dataset. Some problems of the imbalanced dataset were solved using transfer learning. The model results average of the multi-class accuracy is 61%, presenting better performance results for classifying classes KL-0, KL-3, and KL-4 than KL-1 and KL-2. The classification results were compared and validated using the classification of experienced radiologists.

ODTiD: Optic Nerve Head SD-OCT Image Dataset.

INTRODUCTION: Optic disc tilt (ODT) or tilted optic disc is a common finding in the general population. It is due to anomalous development caused by the malclosure of the embryonic optic fissure. ODT is commonly associated with high myopia as well as other conditions. In recent days, the common method to image the optic disc (OD) is by optical coherence tomography (OCT). To the best of our knowledge, there are no datasets of ODT available in the public domain. This dataset aims to make open access raw ODT OCT images to test out new image processing segmentation algorithms. METHODS: This dataset of ODT images contains both horizontal and vertical cross-sectional images obtained using spectral-domain optical coherence tomography (SD-OCT, Cirrus 5000, Carl Zeiss Meditec Inc., Dublin, CA). The optic disc cube 200×200 program was used and all the images are aligned with the center of the optic nerve head. This dataset includes images from both clinically normal (20 eyes) and myopic subjects (101 eyes). RESULTS: The dataset consists of clear (121) and manually marked (121) images resulting in a total of 242 images. The age distribution for all subjects combined is 27.24 ± 9.28 (range, 11.0-69.0) years. For normal subjects mean ± SD age distribution is 32.40 ± 17.23 years. Similarly, the myopia age distribution is 26.22 ± 6.37 years. Ground truth images, ie, manually segmented by a clinical expert are provided along with other meta-data includes age, gender, laterality, refractive error classification, spherical equivalent (SE), best-corrected visual acuity (BCVA), intraocular pressure (IOP), and axial length (AXL). CONCLUSION: This open, public database is online at the ICPSR website of the University of Michigan. The dataset can be used to test and validate newly developed automated segmentation algorithms.

FAZSeg: A New Software for Quantification of the Foveal Avascular Zone.

INTRODUCTION: Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ's deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation). METHODS: Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420×420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth. RESULTS: The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0-66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98. CONCLUSION: A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device's inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online.

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016-2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.

Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis.

BACKGROUND: The lack of explanations for the decisions made by deep learning algorithms has hampered their acceptance by the clinical community despite highly accurate results on multiple problems. Attribution methods explaining deep learning models have been tested on medical imaging problems. The performance of various attribution methods has been compared for models trained on standard machine learning datasets but not on medical images. In this study, we performed a comparative analysis to determine the method with the best explanations for retinal OCT diagnosis. METHODS: A well-known deep learning model, Inception-v3 was trained to diagnose 3 retinal diseases - choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen. The explanations from 13 different attribution methods were rated by a panel of 14 clinicians for clinical significance. Feedback was obtained from the clinicians regarding the current and future scope of such methods. RESULTS: An attribution method based on Taylor series expansion, called Deep Taylor, was rated the highest by clinicians with a median rating of 3.85/5. It was followed by Guided backpropagation (GBP), and SHapley Additive exPlanations (SHAP). CONCLUSION: Explanations from the top methods were able to highlight the structures for each disease - fluid accumulation for CNV, the boundaries of edema for DME, and bumpy areas of retinal pigment epithelium (RPE) for drusen. The most suitable method for a specific medical diagnosis task may be different from the one considered best for conventional tasks. Overall, there was a high degree of acceptance from the clinicians surveyed in the study.