Precision medicine to prevent glaucoma-related blindness.

PURPOSE OF REVIEW: Approximately 10% of patients become blind despite using evidence-based guidelines developed from clinical trials and epidemiology studies. Our purpose is to review opportunities to decrease glaucoma-related blindness using the emerging principles of precision medicine. RECENT FINDINGS: The current review focuses on three topics: first, candidate biomarkers for angle-based surgeries, second, head-mounted display (HMD) technology for vision and testing, and third, glaucoma risk alleles discovered by genome-wide association studies. First, in angle-based surgeries, tracers injected into the anterior chamber or Schlemm's canal have allowed visualization of aqueous veins. We describe an innovative use of optical coherence tomography angiography to visualize aqueous veins in a case with 6-year successful outcome following catheter-based trabeculotomy. Second, HMD technology can augment perceived vision and can be used for perimetry testing. Third, developing genetic risk scores that characterize patients who are at highest risk for blindness is a priority. Such biomarker risk scores will integrate genome-wide association study-based risk alleles for glaucoma along with well known demographic and clinical risk factors. SUMMARY: As we gain more knowledge, precision medicine will enable clinicians to decrease glaucoma-related blindness by providing more timely interventions to those patients who are at highest risk for progression to blindness. VIDEO ABSTRACT: http://links.lww.com/COOP/A29.

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.

Segmentation of Ocular Thermogram Using Level-set Algorithm for Analysis of Contralateral Portions in Healthy Eyes

OBJECTIVE: This work aimed to evaluate the level set segmentation algorithm on ocular surface thermograms. In addition, the vascularity functioning between the contralateral portions of two eyes (right and left) was identified using statistical analysis methods. METHODS: A total of 25 healthy participants with an average age of 35 years (20 men and 5 women) were selected in April 2022. Thermogram images were captured using a FLIR T series thermal camera. Conventional image processing techniques, such as filtering and edge detection, were used to preprocess thermograms. Next, the level set approach was used with the edge-detected pattern as an input to an automated segmented region of interest (ROI). RESULTS: Five metrics, namely Dice Coefficient, Tanimoto Index, Jaccard Index, Volume Similarity, and Structural Similarity, were used to assess the performance of the segmentation technique compared to ground truth, which showed 97.5%, 92.5%, 94.5%, 96.5%, and 96.5% correlation, respectively, between the segmented and the ground truth images with average values for both the eyes. Statistical analysis demonstrated that the contralateral portions of the ocular thermograms were significantly different in terms of vascular distribution between the left and right eyes (p < 0.005) CONCLUSION: The level set method efficiently segmented the ROI in ocular thermograms with maximum correlation. According to the segmentation's results, the model showed the dissimilarity between the contralateral parts of the left and right eyes in healthy cases.

Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images.

Deep learning and image processing are used to classify and segment breast tumor images, specifically in ultrasound (US) modalities, to support clinical decisions and improve healthcare quality. However, directly using US images can be challenging due to noise and diverse imaging modalities. In this study, we developed a three-step image processing scheme involving speckle noise filtering using a block-matching three-dimensional filtering technique, region of interest highlighting, and RGB fusion. This method enhances the generalization of deep-learning models and achieves better performance. We used a deep learning model (VGG19) to perform transfer learning on three datasets: BUSI (780 images), Dataset B (162 images), and KAIMRC (5693 images). When tested on the BUSI and KAIMRC datasets using a fivefold cross-validation mechanism, the model with the proposed preprocessing step performed better than without preprocessing for each dataset. The proposed image processing approach improves the performance of the breast cancer deep learning classification model. Multiple diverse datasets (private and public) were used to generalize the model for clinical application.

Visual Functions, Seatbelt Usage, Speed, and Alcohol Consumption Standards for Driving and Their Impact on Road Traffic Accidents.

Purpose:  The incidence of road traffic accidents (RTAs) is dramatically increasing worldwide. Consequently, driving and licensing authorities have instituted strict rules and regulations, such as vision standards, restrictions on drunk driving, seat belt usage, and speeding, for driving safety. This study aimed to summarize the global visual standards for driving license issuing and renewal and investigate the effect of driving safety laws on RTA-related death rates in different countries. Methods:  The study gathered data on visual standards for driving licenses from reliable sources and extracted enforcement scores (drunk driving, seat belt usage, and speeding) and RTA-related death rates from the World Health Organization status report on road safety. The Wilcoxon test explored the association between visual standards and RTA-related death rates, while the Kruskal-Wallis test analyzed the relationship between visual functions and death rates, as well as driving safety enforcement scores and RTA-related death rates. Results:  The analysis was conducted on 71 countries and 50 states within the United States out of the 193 countries listed by the United Nations. It was found that 116 countries and states required a minimum VA range of 6/6-6/18, while 91 countries and states mandated a similar range for one-eyed drivers. VF testing for driving licenses was necessary in 77 countries and states. No significant association was observed between VA or VF testing and RTA-related death rates. However, countries that conducted more visual function tests demonstrated lower rates of RTA-related fatalities. Furthermore, RTA-related death rates were significantly associated with speeding, drunk driving, and seat belt laws. Conclusion: Implementing clear policies regarding vision requirements, maintaining strict rules, and promoting law enforcement on speeding, drunk driving, and seat belt usage are crucial for improving road safety. These measures should be prioritized by driving and licensing authorities worldwide to mitigate the escalating incidence of RTAs.

The Patients' Perspective for the Impact of Late Detection of Ocular Diseases on Quality of Life: A Cross-Sectional Study.

Background: Late detection of ocular diseases negatively affects patients' quality of life (QoL), encompassing health status, psychological, financial, and social aspects. However, the early detection of eye conditions leads to rapid intervention and avoiding complications, thus preserving the QoL. This study assessed the impact of ocular diseases late detection on patients' QoL at multi-eye clinics based on questionnaire responses. Methods: We developed an original Arabic-English questionnaire to assess the QoL of patients with ocular diseases referred from primary and secondary healthcare centers to tertiary hospitals. It covered preliminary data, patient perspectives on having lately detected ocular disease and treatment costs, and the impact of late detection on finances, social life, psychology, health status, and awareness of current initiatives. Logistic regression analysis was used to explore the associations between patient perspectives on having ocular diseases detected at a late stage and its impact on different domains. Multivariate logistic regression was applied with impact types of health status, psychological, financial, and social (dependent variables) and age, income levels, and hospital type (independent variables). Results: Three hundred and eighty-eight responded, with 50% experiencing psychological effects, 27% health issues, 23% social impacts, and 23% financial burdens. Two hundred seventeen patients (56%) reported having ocular condition detected in late stage. Logistic regression analysis showed positive association with health status, social well-being, and financial effects (p < 0.05). Multivariate analysis revealed pronounced effects in patients ≤ 50 years, with income \< 5000 SAR, and those visiting private clinics (p < 0.05). The social impact was greater in patients visiting private hospitals. Ninety percent of all patients emphasized the importance of increasing awareness for better QoL. Conclusion: Significant associations were found between the late detection of eye diseases and their impact on QoL. Therefore, early detection and increasing patients' awareness of ocular diseases and treatment are essential.

Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation - A Review.

Background: Globally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs. Methods: A systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed. Results: Eight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation. Conclusion: We provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.

Ocular transmission and manifestation for coronavirus disease: a systematic review.

BACKGROUND: There is currently a lack of information regarding ocular tropism and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Globally, the cumulative number of coronavirus disease 2019 (COVID-19) cases is increasing daily. Thus the potential for ocular transmission and manifestations of SARS-CoV-2 requires more investigation. METHODS: A systematic search of electronic databases for ocular transmission and manifestations of SARS-CoV-2 was performed. Pooled cross-sectional studies were used for conducting a meta-analysis to estimate the prevalence of ocular transmission of SARS-CoV-2 to the respiratory system and ocular manifestations (associated symptoms) of SARS-CoV-2. RESULTS: The highest prevalence of SARS-CoV-2-positive tears using reverse transcription polymerase chain reaction was found to be 7.5%. However, the highest prevalence of ocular conjunctivitis associated with SARS-CoV-2 was 32%. Thus, SARS-CoV-2 can evidently infect the eye, as revealed in the conjunctival secretions of COVID-19 patients. CONCLUSION: The available data reflect the influence of the ocular structure on SARS-CoV-2. The analysis showed that ocular manifestation is an indication for SARS-CoV-2, particularly conjunctivitis. Moreover, there is no evidence that the ocular structure can be an additional path of transmission for SARS-CoV-2, however, it warrants further investigation.

Safety Evaluation of Photoacoustic Tomography System for Intraocular Tumors.

Purpose: Photoacoustic tomography (PAT) has demonstrated the ability to characterize molecular components and architectural heterogeneities of intraocular tumors in enucleated human globes and in animals in vivo. Although laser safety levels have been established for illumination through the cornea, the safety limit for PAT illumination through the sclera has not been investigated. The purpose of this study is to examine if the energy level used in intraocular PAT results in ocular damage. Methods: Rabbit eyes were exposed to pulsed laser illumination at 20 mJ/cm2 at the scleral surface. Eyes were examined at 1, 7, and 28 days after the laser exposure. Examination procedures included white light and fluorescence fundus imaging, optical coherence tomography (OCT), electroretinography (ERG), and histology with hematoxylin and eosin (H&E) staining as well as terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick-end labeling (TUNEL staining). Results: Fundus imaging and OCT of rabbit eyes at 1, 7, and 28 days following exposure of the laser illumination of the PAT system did not reveal any damage to the retinal structures. ERG showed no significant difference between the experimental and control eyes. Similarly, H&E histology did not show abnormalities in either the scleral tissue where the laser illumination was delivered or in the retinal layers. No sign of apoptosis in the layers of the retina, choroid, or optic nerve was found on TUNEL staining. Conclusions: Similar to the application of PAT to other organs, the proposed laser illumination energy level at 20 mJ/cm2 does not impose detectable harm to the ocular tissue. Translational Relevance: This study addresses illumination safety issues for PAT.

Peripapillary atrophy classification using CNN deep learning for glaucoma screening.

Glaucoma is the second leading cause of blindness worldwide, and peripapillary atrophy (PPA) is a morphological symptom associated with it. Therefore, it is necessary to clinically detect PPA for glaucoma diagnosis. This study was aimed at developing a detection method for PPA using fundus images with deep learning algorithms to be used by ophthalmologists or optometrists for screening purposes. The model was developed based on localization for the region of interest (ROI) using a mask region-based convolutional neural networks R-CNN and a classification network for the presence of PPA using CNN deep learning algorithms. A total of 2,472 images, obtained from five public sources and one Saudi-based resource (King Abdullah International Medical Research Center in Riyadh, Saudi Arabia), were used to train and test the model. First the images from public sources were analyzed, followed by those from local sources, and finally, images from both sources were analyzed together. In testing the classification model, the area under the curve's (AUC) scores of 0.83, 0.89, and 0.87 were obtained for the local, public, and combined sets, respectively. The developed model will assist in diagnosing glaucoma in screening programs; however, more research is needed on segmenting the PPA boundaries for more detailed PPA detection, which can be combined with optic disc and cup boundaries to calculate the cup-to-disc ratio.

Sustainability of Ophthalmology Practice and Training During and Post the Pandemic of Coronavirus (COVID-19): A Review.

PURPOSE: To review and analyse the globally established ophthalmic practice protocols during the coronavirus disease (COVID-19). METHODS: A literature review using search strategy was conducted to identify appropriate publications relevant to COVID-19 and ophthalmology practice and training. The safety and feasibility of the protocols were illustrated and discussed. RESULTS: Challenges in different eye care settings at various international ophthalmology departments have identified and analysed to introduce solutions. Several clinical protocols were established and concerned for screening procedures, waiting area, clinical flow (ie, patients' registration, personal (patients and healthcare workers) protection), and equipment safety in the clinics and operation rooms. DISCUSSION: In the review of this protocol, the strategic and operational missions of the Academic Medical Centers (AMCs) are demonstrated and discussed. This is in addition to the sustainability of the established protocols for cataract surgeries and glaucoma clinics and training during and after COVID-19. CONCLUSION: All the protocols have established for temporary circumstances, such as postponing elective appointments and surgeries as well as applying the technology for regular follow-ups (transmission of image, video, and face-to-face interactions via widely available applications). Only, one protocol was stronger for the sustainability. Accordingly, recommendations are suggested for clinical sustainability during and after COVID-19.

Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification.

PURPOSE: Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes. METHODS: Three different datasets are used to develop a novel medical data classification model. The proposed model involved preprocessing, artificial flora algorithm (AFA)-based feature selection, and gradient boosted tree (GBT)-based classification. Then, the processing occurred in two steps, namely, format conversion and data transformation. AFA was applied for selecting features, such as demographics, vital signs, laboratory tests, medications, from the patients' electronic health records. Lastly, the GBT-based classification model was applied for classifying the patients' cases to type I, type II, or gestational diabetes mellitus. RESULTS: The effectiveness of the proposed AFA-GBT model was validated using three diabetes datasets to classify patient cases into one of the three different types of diabetes. The proposed model showed a maximum average precision of 91.64%, a recall of 97.46%, an accuracy of 99.93%, an F-score of 94.19%, and a kappa of 96.61%. CONCLUSION: The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. OBJECTIVE: The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). METHODS: Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. RESULTS: In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. CONCLUSION: The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients' prioritization in the current COVID-19 pandemic crisis.

The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System.

PURPOSE: The purpose of this study was to evaluate the ability to screen for glaucoma using a Food Drug Administration (FDA) Class II diagnostic digital fundus photography system used for diabetic retinopathy screening (DRS). METHODS: All research participants underwent a comprehensive eye examination as well as non-mydriatic 45°single photograph retinal imaging centered on the macula. Optic nerve images within the 45° non-mydriatic and non-stereo DRS image were evaluated by two methods: 1) grading by three glaucoma specialists, and 2) a computer-aided automated segmentation system to determine the vertical cup-to-disc ratio (VCDR). Using VCDR from clinical assessment as gold standard, VCDR results from two methods were compared to that from clinical assessment. Inter-grader agreement was assessed by computing intraclass correlation coefficient (ICC). In addition, sensitivity and specificity were calculated. RESULTS: Among 245 fundus photos, 166 images met quality specifications for analysis. Fifty images were not processed by the automated system due to the poor quality of the optic disc, and 29 images did not include the optic nerve head due to the patient movement during the photo acquisition. The ICC value for the VCDR between the gold standard clinical exam and the automated system was 0.41, indicating fair agreement. The ICC value between the three ophthalmologists and the gold standard was 0.51, 0.56, and 0.69, respectively, indicating fair to moderate agreement. DISCUSSION: Assessing the VCDR on non-mydriatic and non-stereo DRS fundus photographs by either the computer-aided automated segmentation system or by glaucoma specialists showed similar fair to moderate agreement. In summary, optic nerve assessment for glaucoma from these 45° non-mydriatic and non-stereo DRS images is not yet suitable for tele-glaucoma screening.

An Automatic Image Processing System for Glaucoma Screening.

Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma's population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while thresholding using Type-II fuzzy approach was developed for segmenting the cup. The results from the algorithms were verified using the manual markings of images from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by six ophthalmologists. The algorithm's accuracy for HCDR and VCDR combined was 74.2%. Only the accuracy of manual markings by one ophthalmologist was higher than the algorithm's accuracy. The algorithm's best agreement was with markings by ophthalmologist number 1 in 230 images (41.8%) of the total tested images.

Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction.

We introduce here a new technique for segmenting optic cup using two-dimensional fundus images. Cup segmentation is the most challenging part of image processing of the optic nerve head due to the complexity of its structure. Using the blood vessels to segment the cup is important. Here, we report on blood vessel extraction using first a top-hat transform and Otsu's segmentation function to detect the curves in the blood vessels (kinks) which indicate the cup boundary. This was followed by an interval type-II fuzzy entropy procedure. Finally, the Hough transform was applied to approximate the cup boundary. The algorithm was evaluated on 550 fundus images from a large dataset, which contained three different sets of images, where the cup was manually marked by six ophthalmologists. On one side, the accuracy of the algorithm was tested on the three image sets independently. The final cup detection accuracy in terms of area and centroid was calculated to be 78.2% of 441 images. Finally, we compared the algorithm performance with manual markings done by the six ophthalmologists. The agreement was determined between the ophthalmologists as well as the algorithm. The best agreement was between ophthalmologists one, two and five in 398 of 550 images, while the algorithm agreed with them in 356 images.

Optic disc segmentation for glaucoma screening system using fundus images.

Segmenting the optic disc (OD) is an important and essential step in creating a frame of reference for diagnosing optic nerve head pathologies such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of optic nerve head abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique was applied. As well an important contribution was to involve the variations in opinions among the ophthalmologists in detecting the disc boundaries and diagnosing the glaucoma. Most of the previous studies were trained and tested based on only one opinion, which can be assumed to be biased for the ophthalmologist. In addition, the accuracy was calculated based on the number of images that coincided with the ophthalmologists' agreed-upon images, and not only on the overlapping images as in previous studies. The ultimate goal of this project is to develop an automated image processing system for glaucoma screening. The disc algorithm is evaluated using a new retinal fundus image dataset called RIGA (retinal images for glaucoma analysis). In the case of low-quality images, a double level set was applied, in which the first level set was considered to be localization for the OD. Five hundred and fifty images are used to test the algorithm accuracy as well as the agreement among the manual markings of six ophthalmologists. The accuracy of the algorithm in marking the optic disc area and centroid was 83.9%, and the best agreement was observed between the results of the algorithm and manual markings in 379 images.

Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey.

Glaucoma is the second leading cause of loss of vision in the world. Examining the head of optic nerve (cup-to-disc ratio) is very important for diagnosing glaucoma and for patient monitoring after diagnosis. Images of optic disc and optic cup are acquired by fundus camera as well as Optical Coherence Tomography. The optic disc and optic cup segmentation techniques are used to isolate the relevant parts of the retinal image and to calculate the cup-to-disc ratio. The main objective of this paper is to review segmentation methodologies and techniques for the disc and cup boundaries which are utilized to calculate the disc and cup geometrical parameters automatically and accurately to help the professionals in the glaucoma to have a wide view and more details about the optic nerve head structure using retinal fundus images. We provide a brief description of each technique, highlighting its classification and performance metrics. The current and future research directions are summarized and discussed.