Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration
Imperial College London（帝国理工学院）；Medical University of Vienna（维也纳医科大学）；Moorfields Eye Hospital（摩尔菲尔德眼科医院）；Institute of Molecular and Clinical Ophthalmology Basel（巴塞尔分子与临床眼科研究所）；University of Southampton（南安普顿大学）；Technical University of Munich（慕尼黑工业大学）；University of Michigan（密歇根大学）；Universitat Basel（巴塞尔大学）
Robbie Holland, Oliver Leingang, Christopher Holmes, Philipp Anders, Rebecca Kaye, Sophie Riedl, Johannes C. Paetzold, Ivan Ezhov, Hrvoje Bogunović, Ursula Schm… 展开作者
Robbie Holland, Oliver Leingang, Christopher Holmes, Philipp Anders, Rebecca Kaye, Sophie Riedl, Johannes C. Paetzold, Ivan Ezhov, Hrvoje Bogunović, Ursula Schmidt-Erfurth, Lars Fritsche, Hendrik P. N. Scholl, Sobha Sivaprasad, Andrew J. Lotery, Daniel Rueckert, Martin J. Menten
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically discover biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space built with contrastive learning. Then, individual trajectories are partitioned into atomic sub-sequences that encode transitions between disease states. These are clustered using a newly introduced distance metric. In quantitative experiments we found our method yields temporal biomarkers that are predictive of conversion to late AMD. Furthermore, these clusters were highly interpretable to ophthalmologists who confirmed that many of the clusters represent dynamics that have previously been linked to the progression of AMD, even though they are currently not included in any clinical grading system.
Age related macular degeneration (AMD) is one of the major causes for blindness in the elderly population. In this report, we propose deep learning based methods for retinal analysis using color fundus images for computer aided diagnosis of AMD. We leverage the recent state of the art deep networks for building a single fundus image based AMD classification pipeline. We also propose methods for the other directly relevant and auxiliary tasks such as lesions detection and segmentation, fovea detection and optic disc segmentation. We propose the use of generative adversarial networks (GANs) for …
School of Advanced Technologies in Medicine（医学先进技术学院）；Isfahan University of Medical Sciences（伊斯法罕医科大学）
Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudate are the most significant signs of these diseases. With the aim of automatic classification of DME, AMD and normal subjects from Optical Coherence Tomography (OCT) images, we proposed a classification algorithm. The two important issues intended in this approach are, not utilizing retinal layer segmentation which by itself is a challenging task and attempting to identify diseases in their early stages,…
Kaunas University of Technology（考纳斯理工大学）；Lithuanian University of Health Sciences（立陶宛健康科学大学）
This work aims to research an automatic method for detecting Age-related Macular Degeneration (AMD) lesions in RGB eye fundus images. For this, we align invasively obtained eye fundus contrast images (the "golden standard" diagnostic) to the RGB ones and use them to hand-annotate the lesions. This is done using our custom-made tool. Using the data, we train and test five different convolutional neural networks: a custom one to classify healthy and AMD-affected eye fundi, and four well-known networks: ResNet50, ResNet101, MobileNetV3, and UNet to segment (localize) the AMD lesions in the affect…
University of California, Berkeley（加利福尼亚大学伯克利分校）；International Computer Science Institute（国际计算机科学研究所）；University of California, Davis（加利福尼亚大学戴维斯分校）
Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-intensive annotations and are restricted to the specific trained task. Here, we employed an unsupervised network with Non-Parametric Instance Discrimination (NPID) to grade age-related macular degeneration (AMD) severity using fundus photographs from the Age-Related Eye Disease Study (AREDS). Our unsupervised algorithm demonstrated versatility across different AMD classification schemes without retraining, and achiev…
Sun Yat-sen University（中山大学）；Baidu Inc.（百度公司）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（国家科学技术研究委员会）；Medical University of Vienna（维也纳医科大学）；VUNO Inc.（VUNO公司）；Inserm（法国国家健康与医学研究院）；Univ Bretagne Occidentale（西布列塔尼大学）；Zasti（Zasti公司）
Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Au…
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；National Eye Institute (NEI), National Institutes of Health (NIH)（美国国立卫生研究院国家眼科研究所）
By 2040, age-related macular degeneration (AMD) will affect approximately 288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown promise in diagnosing/screening AMD using color fundus photographs, it remains difficult to predict individuals' risks of late AMD accurately. For both tasks, these initial deep learning attempts have remained largely unvalidated in independent cohorts. Here, we demonstrate how d…
Multiscale Color Guided Attention Ensemble Classifier for Age-Related Macular Degeneration using Concurrent Fundus and Optical Coherence Tomography Images
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；School of Medical Science and Technology, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校医学科学与技术学院）；Department of Mathematics, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校数学系）
Monta Vista High School（蒙塔维斯塔高中）；Hanoi-Amsterdam High School（河内-阿姆斯特丹高中）；Churchill Fulshear High School（丘吉尔·福尔瑟高中）；Norfolk Academy（诺福克学院）；John H. Guyer High School（约翰·H·盖尔高中）；Collège Sainte-Anne（圣安妮学院）
Today, more than 12 million people over the age of 40 suffer from ocular diseases. Most commonly, older patients are susceptible to age related macular degeneration, an eye disease that causes blurring of the central vision due to the deterioration of the retina. The former can only be detected through complex and expensive imaging software, markedly a visual field test; this leaves a significant population with untreated eye disease and holds them at risk for complete vision loss. The use of machine learning algorithms has been proposed for treating eye disease. However, the development of th…
Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)
BioMedIA, Imperial College London（伦敦帝国理工学院BioMedIA）；University of Southampton（南安普顿大学）；University College London（伦敦大学学院）；Moorfields Eye Hospital（摩尔菲尔德眼科医院）；Medical University of Vienna（维也纳医科大学）；Christian Doppler Forschungsgesellschaft（克里斯蒂安多普勒研究协会）；University of Basel（巴塞尔大学）；Technical University Munich（慕尼黑工业大学）
Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection
Faculty of Biomedical Engineering, Technion-IIT（技术学院生物医学工程系）；Taub Faculty of Computer Science, Technion-IIT（技术学院计算机科学系）；Faculty of Mathematics, Technion-IIT（技术学院数学系）；Department of Ophthalmology, Hillel Yaffe Medical Center（希勒尔·亚法医疗中心眼科部）；The Ruth and Rappaport Faculty of Medicine, Technion-IIT（技术学院医学院）；Ophthalmology Department, São Paulo Federal University（圣保罗联邦大学眼科部）；Medical Engineering and Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学系）；The Adelson School of Medicine, Ariel University（阿里尔大学阿德勒医学学校）
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；National Eye Institute, National Institutes of Health（美国国立卫生研究院国家眼科研究所）；University of Wisconsin（威斯康星大学）；Erasmus Medical Center（伊拉斯谟医学中心）；Uniformed Services University of the Health Sciences（美国卫生 uniformed 大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心）
Objective Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autofluorescence (FAF). The objective was to develop and evaluate the performance of a novel 'M3' deep learning framework on RPD detection. Materials and Methods A deep learning framework M3 was developed to detect RPD presence accurately using CFP alone, FAF alone, or both, employing >8000 CFP-FAF image pairs obtained prospectively (Age-Related Eye Diseas…
We propose a method to predict severity of age related macular degeneration (AMD) from input optical coherence tomography (OCT) images. Although there is no standard clinical severity scale for AMD, we leverage deep learning (DL) based image registration and clustering methods to identify diseased cases and predict their severity. Experiments demonstrate our approach's disease classification performance matches state of the art methods. The predicted disease severity performs well on previously unseen data. Registration output provides better explainability than class activation maps regarding…
RETINA-AI Health, Inc.（RETINA-AI健康公司）；MD Anderson Cancer Center（MD安德森癌症中心）
In this paper, we seek a clinically-relevant latent code for representing the spectrum of macular disease. Towards this end, we construct retina-VAE, a variational autoencoder-based model that accepts a patient profile vector (pVec) as input. The pVec components include clinical exam findings and demographic information. We evaluate the model on a subspectrum of the retinal maculopathies, in particular, exudative age-related macular degeneration, central serous chorioretinopathy, and polypoidal choroidal vasculopathy. For these three maculopathies, a database of 3000 6-dimensional pVecs (1000 …
AMD-Mamba: A Phenotype-Aware Multi-Modal Framework for Robust AMD Prognosis
Department of Population Health Sciences, Weill Cornell Medicine（人口健康科学系，韦尔·柯林斯医学中心）；Department of Surgery, University of Minnesota（外科系，明尼苏达大学）；Department of Biomedical Informatics and Data Science, Yale School of Medicine（生物医学信息学与数据科学系，耶鲁医学院）；National Eye Institute, National Institutes of Health（国家眼科研究所，国立卫生研究院）；National Library of Medicine, National Institutes of Health（国家医学图书馆，国立卫生研究院）
University of Nevada, Reno（内华达大学里诺分校）；Houston Eye Associates, Inc.（休斯顿眼科协会公司）
Age-related Macular Degeneration (AMD) is a progressive visual impairment affecting millions of individuals. Since there is no current treatment for the disease, the only means of improving the lives of individuals suffering from the disease is via assistive technologies. In this paper we propose a novel and effective methodology to accurately generate a parametric model for the perceptual deficit caused by the physiological deterioration of a patient's retina due to AMD. Based on the parameters of the model, a mechanism is developed to simulate the patient's perception as a result of the dise…
Centro de Investigación CITIC（CITIC研究中心）；Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所（INIBIC））；Hospital Clínico San Carlos（圣卡洛斯临床医院）；Instituto de Investigación Sanitaria (IdISSC)（健康研究所（IdISSC））；Centro Internacional de Oftalmología Avanzada（国际高级眼科中心）
Age-related Macular Degeneration (AMD) is the predominant cause of blindness in developed countries, specially in elderly people. Moreover, its prevalence is increasing due to the global population ageing. In this scenario, early detection is crucial to avert later vision impairment. Nonetheless, implementing large-scale screening programmes is usually not viable, since the population at-risk is large and the analysis must be performed by expert clinicians. Also, the diagnosis of AMD is considered to be particularly difficult, as it is characterized by many different lesions that, in many case…
Massachusetts Institute of Technology（麻省理工学院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Tufts Medical Center（塔夫茨医疗中心）
Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal diseases. Newly visible features are, for example, small hyperreflective specks in age-related macular degeneration. Identifying these new markers is crucial to investigate potential association with disease progression and treatment outcomes. Therefore, it is necessary to reliably detect these features in 3D volumetric scans. Because manual labeling of entire volumes is infeasible a nee…
FaraAI Company（法拉人工智能公司）；University of Tehran（德黑兰大学）
Optical coherence tomography (OCT) imaging is a well-known technology for visualizing retinal layers and helps ophthalmologists to detect possible diseases. Accurate and early diagnosis of common retinal diseases can prevent the patients from suffering critical damages to their vision. Computer-aided diagnosis (CAD) systems can significantly assist ophthalmologists in improving their examinations. This paper presents a new enhanced deep ensemble convolutional neural network for detecting retinal diseases from OCT images. Our model generates rich and multi-resolution features by employing the l…
Beijing University of Posts and Telecommunications（北京邮电大学）；Hebei Eye Hospital（河北省眼科医院）
Automated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal Choroidal Vessels (PCV). However, due to the difficulty in data collection and the similarity between images, most studies have only achieved the coarse-grained classification of wet-AMD rather than a finer-grained one of wet-AMD subtypes. To solve this issue, in this paper we propose a Knowledge-driven Fine-grained Wet-AMD Classification Model (KFWC), to classify fine-grained diseas…
Medical University of Vienna（维也纳医科大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye and Ear Hospital（皇家维多利亚眼耳医院）；The University of Melbourne（墨尔本大学）
Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression. Self-supervised learning allows to learn new representation from available large unlabelled data without any expert knowledge. We propose a deep learning self-supervised approach to model disease progression from longitudinal retinal optical coherence tomography (OCT). Our self-supervised model takes benefit from a generic time-related task, by learning to estimate the time interval between pairs of scans acquired from the sa…
Faculty of Informatics, Kaunas University of Technology（信息学院，卡斯图技术大学）
Age-related macular degeneration (AMD) is one of the leading causes of irreversible vision impairment in people over the age of 60. This research focuses on semantic segmentation for AMD lesion detection in RGB fundus images, a non-invasive and cost-effective imaging technique. The results of the ADAM challenge - the most comprehensive AMD detection from RGB fundus images research competition and open dataset to date - serve as a benchmark for our evaluation. Taking the U-Net connectivity as a base of our framework, we evaluate and compare several approaches to improve the segmentation model's…
Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所）；Hospital Clínico San Carlos（圣卡洛斯临床医院）；Instituto de Investigación Sanitaria (IdISSC)（圣卡洛斯健康研究所）；Centro Internacional de Oftalmología Avanzada（国际高级眼科中心）
Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, it is the most frequent cause of blindness in developed countries. Although some promising treatments have been developed, their effectiveness is low in advanced stages. This emphasizes the importance of large-scale screening programs. Nevertheless, implementing such programs for AMD is usually unfeasible, since the population at risk is large and the diagnosis is challenging. All this motivates the development of automatic methods. In this sense, severa…
University of Liverpool（利物浦大学）；Royal Liverpool University Hospital（皇家利物浦大学医院）
Prognostic models aim to predict the future course of a disease or condition and are a vital component of personalized medicine. Statistical models make use of longitudinal data to capture the temporal aspect of disease progression; however, these models require prior feature extraction. Deep learning avoids explicit feature extraction, meaning we can develop models for images where features are either unknown or impossible to quantify accurately. Previous prognostic models using deep learning with imaging data require annotation during training or only utilize a single time point. We propose …
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT
Medical University of Vienna（维也纳医科大学）；Imperial College London（伦敦帝国学院）；Technical University Munich（慕尼黑工业大学）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS基金会信托）；University of Basel（巴塞尔大学）；University of Southampton（南安普顿大学）
