Current and future roles of artificial intelligence in retinopathy of prematurity

Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face challenges like accuracy and overfitting. Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs), have significantly improved ROP detection and classification. The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential. This research comprehensively examines the contemporary progress and challenges associated with using retinal imaging and artificial intelligence (AI) to detect ROP, offering valuable insights that can guide further investigation in this domain. Based on 89 original studies in this field (out of 1487 studies that were comprehensively reviewed), we concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions. AI holds great promise for improving ROP management. This review explores AI's potential in ROP detection, classification, diagnosis, and prognosis.Comment: 28 pages, 8 figures, 2 tables, 235 references, 1 supplementary tabl

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care

Automated classification of retinopathy of prematurity in newborns

La Retinopatia de l'Prematur (ROP) és una malaltia que afecta els nadons prematurs mostrant-se com un subdesenvolupament dels vasos retinians. El diagnòstic precoç d'aquesta malaltia és un tot un repte ja que requereix de professionals altament qualificats amb coneixements molt específics. Actualment a Espanya, només uns pocs hospitals compten amb els equipaments especialitzats per al tractament i diagnòstic d'aquesta patologia. Aquest projecte final de màster, té com a objectiu final desenvolupar una eina preliminar per a la classificació de l'extensió aquesta malaltia. Aquesta applicació, ha estat disenyada per a ser integrada en una plataforma de suport a la diagnosi de la Retinopatia i poder evaluar la malaltia, proporcionant informació detallada sobre les imatge analitzades. Aquest projecte, també estableix les bases per a la comparació entre l'enfocament clínic, que utilitzen els metges, i la naturalesa "Black-Box" natural de la Xarxa Neuronal Artificial per classificar l'extensió de la malaltia. L'algoritme desenvolupat és capaç de: segmentar els vasos oculars utilitzant una xarxa neuronal convolucional U-Net; extreure les característiques representatives de la malaltia a partir de la segmentació; i classificar aquestes característiques en casos ROP i casos ROP Plus, mitjançant l'ús d'una gamma de classificadors. Les principals característiques analitzades són la tortuositat i el gruix dels vasos, indicadors de la malaltia emprats pels patolegs experts. La xarxa de segmentació ha obtingut una precisió global de l'96,15%. Els resultats dels diferents classificadors indiquen un trade-off entre la precisió i el volum d'imatges analitzades. S'ha obtingut una precisió de l'100% emprant un classificador de doble threshold en el analisis de l'12,5% de les imatges. En canvi, mitjançant l'ús d'un classificador "decision tree", s'ha obtingut una precisió del 70,8% analitzant el 100% de les imatges.La Retinopatía del Prematuro (ROP) es una enfermedad que afecta a los bebés prematuros mostrándose como el subdesarrollo de los vasos retinianos. El diagnóstico precoz de dicha enfermedad es un desafío ya que requiere de profesionales altamente capacitados con conocimientos muy específicos. Actualmente en España, solo unos pocos hospitales están dotados con los equipamientos especializados para el tratamiento y diagnóstico de esta patología Este proyecto final de master, tiene como objetivo final desarrollar una herramienta preliminar para la clasificación de la extensión dicha enfermedad. Esta aplicación, ha sido diseñada para ser integrada en una plataforma de soporte al diagnóstico de la Retinopatía y evaluar la enfermedad, proporcionando información detallada sobre las imágenes analizadas. Este proyecto también sienta las bases para la comparación entre el enfoque clínico, que utilizan los médicos, y la naturaleza "Black-Box" natural de la Red Neuronal Artificial para clasificar la extensión de la enfermedad. El algoritmo desarrollado es capaz de: segmentar los vasos oculares utilizando una red neuronal convolucional U-Net; extraer las características representativas de la enfermedad a partir de la segmentación; y clasificar estas características en casos ROP y casos ROP Plus, mediante el empleo de una gama de clasificadores. Las principales características analizadas son la tortuosidad y el grosor de los vasos, indicadores cauterizantes de la enfermedad empleados por los patólogos expertos. La red de segmentación ha logrado una precisión global del 96,15%. Los resultados de los diferentes clasificadores indican un trade-off entre la precisión y el volumen de imágenes analizadas. Se ha obtenido una precisión del 100% empleando un clasificador de doble threshold en el análisis del 12,5% de las imágenes. En cambio, mediante el uso de un clasificador “decision tree”, se ha obtenido una precisión del 70,8% analizando el 100% de las imágenes.Retinopathy of Prematurity (ROP) is a disease in preterm babies with underdevelopment in retinal vessels. Early diagnosis of the disease is challenging and requires skilled professionals with very specific knowledge. Currently, in Spain, only a few hospitals have departments specialized in this pathology and, therefore, are able to diagnose and treat it accordingly. This master project aims to develop the first preliminary instrument for the classification of the extent of Retinopathy disease. This tool has been built to be integrated into a diagnostic support platform to detect the presence of retinopathy and evaluate the sickness, providing insightful information regarding the specific image. This project also lays the base for the comparison between the clinical approach that the doctors use and the “black box” approach the Artificial Neural Network uses to predict the extent of the disease. The developed algorithm is able to: segment ocular vessels using a U-Net Convolutional Neural Network; extract the critical features from the segmentation; and classify those features into ROP cases and ROP Plus cases by employing a range of different classifiers. The main features analyzed by the related specialists and thus selected are tortuosity and thickness of the vessels. The segmentation Network achieved a global accuracy of 96.15%. The results of the different classifiers indicate a trade-off between accuracy and the volume of computed images. An accuracy of 100% was achieved with a Double Threshold classifier on 12.5% of the images. Instead, by using a Decision tree classifier, an accuracy of 70.8% was achieved when computing 100% of the images

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research

With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization