Clinical Applications of Artificial Intelligence in Glaucoma

Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AIenabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice

Computational methods for new clinical applications using imaging techniques

Esta tesis tiene por objetivo desarrollar diferentes métodos computacionales con aplicación clínica en varias enfermedades. De este modo, la investigación aquí presentada pretende aumentar el conocimiento sobre cómo el análisis y el estudio de los datos procedentes de técnicas de imagen pueden convertirse en un gran valor clínico para los profesionales de la medicina. Por lo tanto, dichos métodos pueden ser incorporados en la práctica clínica, lo que supone un beneficio para el paciente.Por un lado, la mejora de los diferentes dispositivos de imagen aumenta el abanico de posibilidades de análisis y presentación de los datos. Algunas técnicas de imagen arrojan directamente datos numéricos que tradicionalmente sólo se usaban para la monitorización de enfermedades. Sin embargo, dichos datos pueden ser empleados como biomarcadores tanto para el diagnóstico como para la predicción de enfermedades mediante la inteligencia artificial. Hoy en día, la inteligencia artificial se utiliza en muchos campos ya que todo lo que proporciona datos es abordable por estas nuevas tecnologías. Parece que no hay límite y se están desarrollando nuevas aplicaciones que hace sólo unas décadas parecían imposibles.Por otro lado, las técnicas de imagen nos permiten analizar diferentes partes del cuerpo humano en los respectivos pacientes y compararlas con controles sanos. Del mismo modo, con las imágenes se puede realizar el seguimiento de los tratamientos aplicados en dichos pacientes y, así, verificar su eficacia. Además, estas tecnologías, que proporcionan imágenes de alta resolución, son fáciles de usar, rentables y objetivas.Para resumir, esta tesis se ha centrado en desarrollar varias aplicaciones clínicas, basadas en los métodos numéricos descritos, que podrían ser una poderosa herramienta para aportar mayor información que ayude a los clínicos en la toma de decisiones.This thesis aims to develop different computational methods with clinical application in various diseases. In this way, the research presented here aims to increase knowledge on how the analysis and study of data from imaging techniques can be of great clinical value to medical professionals. Therefore, these methods can be incorporated into clinical practice, which is of benefit to the patient. On the one hand, the improvement of different imaging devices increases the range of possibilities for data analysis and presentation. Some imaging techniques directly yield numerical data that were traditionally only used for disease monitoring. However, these data can be used as biomarkers for both diagnosis and disease prediction using artificial intelligence. Today, artificial intelligence is used in many fields as everything that provides data can be addressed by these new technologies. There seems to be no limit and new applications are being developed that only a few decades ago seemed impossible. On the other hand, imaging techniques allow us to analyse different parts of the human body in the respective patients and compare them with healthy controls. In the same way, imaging can be used to monitor the treatments applied to these patients and, thus, verify their efficacy. Moreover, these technologies, which provide high-resolution images, are easy to use, cost-effective and objective. To summarise, this thesis has focused on developing several clinical applications, based on the described numerical methods, which could be a powerful tool to provide further information to help clinicians in decision making.<br /

Retinal correlates of neurological disorders

Considering the retina as an extension of the brain provides a platform from which to study diseases of the nervous system. Taking advantage of the clear optical media of the eye and ever-increasing resolution of modern imaging techniques, retinal morphology can now be visualized at a cellular level in vivo. This has provided a multitude of possible biomarkers and investigative surrogates that may be used to identify, monitor and study diseases until now limited to the brain. In many neurodegenerative conditions, early diagnosis is often very challenging due to the lack of tests with high sensitivity and specificity, but, once made, opens the door to patients accessing the correct treatment that can potentially improve functional outcomes. Using retinal biomarkers in vivo as an additional diagnostic tool may help overcome the need for invasive tests and histological specimens, and offers the opportunity to longitudinally monitor individuals over time. This review aims to summarise retinal biomarkers associated with a range of neurological conditions including Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and prion diseases from a clinical perspective. By comparing their similarities and differences according to primary pathological processes, we hope to show how retinal correlates can aid clinical decisions, and accelerate the study of this rapidly developing area of research

Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography

Tesis por compendio[ES] Esta tesis presenta soluciones de vanguardia basadas en algoritmos de computer vision (CV) y machine learning (ML) para ayudar a los expertos en el diagnóstico clínico. Se centra en dos áreas relevantes en el campo de la imagen médica: la patología digital y la oftalmología. Este trabajo propone diferentes paradigmas de machine learning y deep learning para abordar diversos escenarios de supervisión en el estudio del cáncer de próstata, el cáncer de vejiga y el glaucoma. En particular, se consideran métodos supervisados convencionales para segmentar y clasificar estructuras específicas de la próstata en imágenes histológicas digitalizadas. Para el reconocimiento de patrones específicos de la vejiga, se llevan a cabo enfoques totalmente no supervisados basados en técnicas de deep-clustering. Con respecto a la detección del glaucoma, se aplican algoritmos de memoria a corto plazo (LSTMs) que permiten llevar a cabo un aprendizaje recurrente a partir de volúmenes de tomografía por coherencia óptica en el dominio espectral (SD-OCT). Finalmente, se propone el uso de redes neuronales prototípicas (PNN) en un marco de few-shot learning para determinar el nivel de gravedad del glaucoma a partir de imágenes OCT circumpapilares. Los métodos de inteligencia artificial (IA) que se detallan en esta tesis proporcionan una valiosa herramienta de ayuda al diagnóstico por imagen, ya sea para el diagnóstico histológico del cáncer de próstata y vejiga o para la evaluación del glaucoma a partir de datos de OCT.[CA] Aquesta tesi presenta solucions d'avantguarda basades en algorismes de *computer *vision (CV) i *machine *learning (ML) per a ajudar als experts en el diagnòstic clínic. Se centra en dues àrees rellevants en el camp de la imatge mèdica: la patologia digital i l'oftalmologia. Aquest treball proposa diferents paradigmes de *machine *learning i *deep *learning per a abordar diversos escenaris de supervisió en l'estudi del càncer de pròstata, el càncer de bufeta i el glaucoma. En particular, es consideren mètodes supervisats convencionals per a segmentar i classificar estructures específiques de la pròstata en imatges histològiques digitalitzades. Per al reconeixement de patrons específics de la bufeta, es duen a terme enfocaments totalment no supervisats basats en tècniques de *deep-*clustering. Respecte a la detecció del glaucoma, s'apliquen algorismes de memòria a curt termini (*LSTMs) que permeten dur a terme un aprenentatge recurrent a partir de volums de tomografia per coherència òptica en el domini espectral (SD-*OCT). Finalment, es proposa l'ús de xarxes neuronals *prototípicas (*PNN) en un marc de *few-*shot *learning per a determinar el nivell de gravetat del glaucoma a partir d'imatges *OCT *circumpapilares. Els mètodes d'intel·ligència artificial (*IA) que es detallen en aquesta tesi proporcionen una valuosa eina d'ajuda al diagnòstic per imatge, ja siga per al diagnòstic histològic del càncer de pròstata i bufeta o per a l'avaluació del glaucoma a partir de dades d'OCT.[EN] This thesis presents cutting-edge solutions based on computer vision (CV) and machine learning (ML) algorithms to assist experts in clinical diagnosis. It focuses on two relevant areas at the forefront of medical imaging: digital pathology and ophthalmology. This work proposes different machine learning and deep learning paradigms to address various supervisory scenarios in the study of prostate cancer, bladder cancer and glaucoma. In particular, conventional supervised methods are considered for segmenting and classifying prostate-specific structures in digitised histological images. For bladder-specific pattern recognition, fully unsupervised approaches based on deep-clustering techniques are carried out. Regarding glaucoma detection, long-short term memory algorithms (LSTMs) are applied to perform recurrent learning from spectral-domain optical coherence tomography (SD-OCT) volumes. Finally, the use of prototypical neural networks (PNNs) in a few-shot learning framework is proposed to determine the severity level of glaucoma from circumpapillary OCT images. The artificial intelligence (AI) methods detailed in this thesis provide a valuable tool to aid diagnostic imaging, whether for the histological diagnosis of prostate and bladder cancer or glaucoma assessment from OCT data.García Pardo, JG. (2022). Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182400Compendi

Extending Bayesian network models for mining and classification of glaucoma

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process

Glaucoma Detection from Raw SD-OCT Volumes: a Novel Approach Focused on Spatial Dependencies

[EN] Background and objective:Glaucoma is the leading cause of blindness worldwide. Many studies based on fundus image and optical coherence tomography (OCT) imaging have been developed in the literature to help ophthalmologists through artificial-intelligence techniques. Currently, 3D spectral-domain optical coherence tomography (SD-OCT) samples have become more important since they could enclose promising information for glaucoma detection. To analyse the hidden knowledge of the 3D scans for glaucoma detection, we have proposed, for the first time, a deep-learning methodology based on leveraging the spatial dependencies of the features extracted from the B-scans. Methods:The experiments were performed on a database composed of 176 healthy and 144 glaucomatous SD-OCT volumes centred on the optic nerve head (ONH). The proposed methodology consists of two well-differentiated training stages: a slide-level feature extractor and a volume-based predictive model. The slide-level discriminator is characterised by two new, residual and attention, convolutional modules which are combined via skip-connections with other fine-tuned architectures. Regarding the second stage, we first carried out a data-volume conditioning before extracting the features from the slides of the SD-OCT volumes. Then, Long Short-Term Memory (LSTM) networks were used to combine the recurrent dependencies embedded in the latent space to provide a holistic feature vector, which was generated by the proposed sequential-weighting module (SWM). Results:The feature extractor reports AUC values higher than 0.93 both in the primary and external test sets. Otherwise, the proposed end-to-end system based on a combination of CNN and LSTM networks achieves an AUC of 0.8847 in the prediction stage, which outperforms other state-of-the-art approaches intended for glaucoma detection. Additionally, Class Activation Maps (CAMs) were computed to highlight the most interesting regions per B-scan when discerning between healthy and glaucomatous eyes from raw SD-OCT volumes. Conclusions:The proposed model is able to extract the features from the B-scans of the volumes and combine the information of the latent space to perform a volume-level glaucoma prediction. Our model, which combines residual and attention blocks with a sequential weighting module to refine the LSTM outputs, surpass the results achieved from current state-of-the-art methods focused on 3D deep-learning architectures.The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used here.This work has been funded by GALAHAD project [H2020-ICT-2016-2017, 732613], SICAP project (DPI2016-77869-C2-1-R) and GVA through project PROMETEO/2019/109. 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The application of artificial intelligence in glaucoma diagnosis and prediction

Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups

Analysis of Retinal Image Data to Support Glaucoma Diagnosis

Fundus kamera je široce dostupné zobrazovací zařízení, které umožňuje relativně rychlé a nenákladné vyšetření zadního segmentu oka – sítnice. Z těchto důvodů se mnoho výzkumných pracovišť zaměřuje právě na vývoj automatických metod diagnostiky nemocí sítnice s využitím fundus fotografií. Tato dizertační práce analyzuje současný stav vědeckého poznání v oblasti diagnostiky glaukomu s využitím fundus kamery a navrhuje novou metodiku hodnocení vrstvy nervových vláken (VNV) na sítnici pomocí texturní analýzy. Spolu s touto metodikou je navržena metoda segmentace cévního řečiště sítnice, jakožto další hodnotný příspěvek k současnému stavu řešené problematiky. Segmentace cévního řečiště rovněž slouží jako nezbytný krok předcházející analýzu VNV. Vedle toho práce publikuje novou volně dostupnou databázi snímků sítnice se zlatými standardy pro účely hodnocení automatických metod segmentace cévního řečiště.Fundus camera is widely available imaging device enabling fast and cheap examination of the human retina. Hence, many researchers focus on development of automatic methods towards assessment of various retinal diseases via fundus images. This dissertation summarizes recent state-of-the-art in the field of glaucoma diagnosis using fundus camera and proposes a novel methodology for assessment of the retinal nerve fiber layer (RNFL) via texture analysis. Along with it, a method for the retinal blood vessel segmentation is introduced as an additional valuable contribution to the recent state-of-the-art in the field of retinal image processing. Segmentation of the blood vessels also serves as a necessary step preceding evaluation of the RNFL via the proposed methodology. In addition, a new publicly available high-resolution retinal image database with gold standard data is introduced as a novel opportunity for other researches to evaluate their segmentation algorithms.

Structure and Function in Early Glaucoma

Glaucoma is a group of diseases, which exhibit a characteristic optic neuropathy and may result in progressive visual field loss. The most important risk factor is raised intraocular pressure (IOP) usually secondary to reduced aqueous outflow through the anterior chamber angle. It is the second leading cause of blindness globally. The diagnosis of glaucoma is difficult, as there is currently no widely-accepted “clinical standard” for diagnosis, although “progressive structural optic nerve and/or nerve fiber layer damage” is currently the most commonly accepted diagnostic criterion. Current treatments are to reduce the level of IOP, either by topical medication or surgery. Unfortunately, medical intervention frequently takes place after visual field loss has occurred. Consequently, much effort has been placed into the early diagnosis of glaucoma, in order to prevent damage. Visual field tests have been a popular clinical method to determine functional defects, and they are essential for managing and diagnosing glaucoma. Various methods and test strategies have been developed. Computerized threshold static perimetry involves determining the dimmest stimulus that can be seen at a number of pre-determined test point locations. An examiner can interpret the resulting pattern of defect; also, disease progress can be followed over time. Visual fields should not be interpreted in isolation but in conjunction with other clinical findings1. Standard automated perimetry (SAP) is our oldest and best documented, computerized, subjective visual function test. Threshold tests are commonly used for both detection and follow-up of glaucoma patients. Different testing strategies and different stimuli have been developed with expectations of raising the sensitivity for early detection of glaucoma-related functional change, such as short wavelength automated perimetry (SWAP), high-pass resolution perimetry (HRP), frequency doubling technology (FDT) and Flicker Defined Form (FDF). FDF is a temporally driven illusion in which background elements and stimulus elements are flickered in counterphase at a high temporal frequency, creating an illusory contour at the boundary between the background and the stimulus. It has been described to be a predominantly magnocellular-based stimulus due to its dependence on high temporal frequencies and its perceived low spatial frequency. The random flickering dots throughout the field of view and the complex nature of the stimulus, a phase-difference percept requiring higher order processing. Clinically, besides testing for deficits in function, measuring of retinal structure plays an important role in the diagnosis of early glaucoma. Damage results in characteristic signs in the retinal nerve fiber layer, the parapapillary retina and the optic nerve head, due to the oriented distribution of the nerve fiber in the retina. Scanning laser tomography (SLT; Heidelberg Retina Tomograph, Heidelberg, Germany) is a confocal scanning laser device that provides accurate and reproducible topographical information of the optic disc and peripapillary retina. Other methods such as optic disc photography, retinal nerve fiber layer photography, scanning laser polarimetry, and optical cohererence tomography are also designed to detect structural changes. By analyzing the neuroretinal rim within the optic disc, the SLT provides evidence of glaucoma related structural change, such as changes in the cup to disc ratio and notching and narrowing of the neuroretinal rim. Measurements were affected by age, but it is fairly robust to astigmatism and working distance. Studies have shown correlation between visual field test results and optic nerve head structural measurements. The correlation analysis of structure and function was performed to evaluate the spatial relationship. It has been proposed that both structural and functional diagnostic methods have unique value, but the combination of methods might provide early evidence for glaucoma diagnosis and management. The objectives of this thesis are: 1. To determine the normal sensitivity and confidence limits for FDF perimetry as a function of age; 2. To determine the test-retest repeatability of FDF perimetry for stable glaucoma; and 3. To investigate the structure function relationship in glaucoma using FDF perimetry and the HRT. Normative data for different perimeters are well established. It is critical to establish normal sensitivity for the FDF perimetry. Age related sensitivity loss throughout the visual field has been previously reported. Confidence limits for normality will be established in this thesis, as only then can we examine the ability of the new clinical test to detect early glaucoma. Measures of function and structure are both relevant and required for the early diagnosis of glaucoma. The relationship between the points tested in the visual field and corresponding positions at the optic nerve head have been previously described. Comparing the FDF perimetry results with the HRT optic nerve head results has the potential to be of significant value in the diagnosis of glaucoma