Addressing Artificial Intelligence Bias in Retinal Disease Diagnostics
Applied Physics Laboratory, The Johns Hopkins University（约翰斯·霍普金斯大学应用物理实验室）；Retina Division, Wilmer Eye Institute, The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院威尔玛眼科研究所视网膜科）；Retina Division, Department of Ophthalmology, Brazilian Center of Vision (CBV) Eye Hospital（巴西视觉中心（CBV）眼科医院眼科视网膜科）
Philippe Burlina, Neil Joshi, William Paul, Katia D. Pacheco, Neil M. Bressler
备注：
Accepted for publication at journal of Translational Vision Science and Technology
This study evaluated generative methods to potentially mitigate AI bias when diagnosing diabetic retinopathy (DR) resulting from training data imbalance, or domain generalization which occurs when deep learning systems (DLS) face concepts at test/inference time they were not initially trained on. The public domain Kaggle-EyePACS dataset (88,692 fundi and 44,346 individuals, originally diverse for ethnicity) was modified by adding clinician-annotated labels and constructing an artificial scenario of data imbalance and domain generalization by disallowing training (but not testing) exemplars for images of retinas with DR warranting referral (DR-referable) and from darker-skin individuals, who presumably have greater concentration of melanin within uveal melanocytes, on average, contributing to retinal image pigmentation. A traditional/baseline diagnostic DLS was compared against new DLSs that would use training data augmented via generative models for debiasing. Accuracy (95% confidence intervals [CI]) of the baseline diagnostics DLS for fundus images of lighter-skin individuals was 73.0% (66.9%, 79.2%) vs. darker-skin of 60.5% (53.5%, 67.3%), demonstrating bias/disparity (delta=12.5%) (Welch t-test t=2.670, P=.008) in AI performance across protected subpopulations. Using novel generative methods for addressing missing subpopulation training data (DR-referable darker-skin) achieved instead accuracy, for lighter-skin, of 72.0% (65.8%, 78.2%), and for darker-skin, of 71.5% (65.2%,77.8%), demonstrating closer parity (delta=0.5%) in accuracy across subpopulations (Welch t-test t=0.111, P=.912). Findings illustrate how data imbalance and domain generalization can lead to disparity of accuracy across subpopulations, and show that novel generative methods of synthetic fundus images may play a role for debiasing AI.
Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models
Monash University（莫纳什大学）；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）；The Hong Kong Polytechnic University（香港理工大学）；Centre for Eye and Vision Research（眼与视觉研究中心）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Airdoc-Monash Research Lab（Airdoc-莫纳什研究实验室）
RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework
East West University（东西大学）；American International University-Bangladesh（孟加拉国美国国际大学）；Bangladesh Army University of Engineering and Technology（孟加拉国陆军工程技术大学）
OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation
Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared to traditional fundus photography. Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities, or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection. We first improve on the s…
Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present …
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）
Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide. Automated multi-disease detection models offer great potential to address this problem via clinical decision support in diagnosis. In this work, we proposed an innovative multi-disease detection pipeline for retinal imaging which utilizes ensemble learning to combine the predictive capabilities of several heterogeneous deep convolutional neural network models. Our pipeline includes state-of-the-art strategies like transfer learning, class weighting, real-time image augmentation and Focal loss utiliza…
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases
Department of Electrical Engineering, Yuan Ze University（电气工程系，元智大学）；Department of Electrical Engineering, National Taiwan Normal University（电气工程系，国立台湾师范大学）；Department of Ophthalmology, Far Eastern Memorial Hospital（眼科部，东部医疗院）
A Disease-Specific Foundation Model Using Over 100K Fundus Images: Release and Validation for Abnormality and Multi-Disease Classification on Downstream Tasks
Seoul National University（首尔大学）；Seoul National University Hospital（首尔大学附属医院）
Artificial intelligence techniques in inherited retinal diseases: A review
The University of Western Australia（西澳大利亚大学）；School of Allied Health（联合健康学院）；School of Physics, Maths and Computing（物理、数学与计算学院）；Centre for Ophthalmology and Visual Sciences (Lions Eye Institute)（眼科与视觉科学中心（狮子眼科研究所））
Purpose: To create and evaluate the accuracy of an artificial intelligence Deep learning platform (ORAiCLE) capable of using only retinal fundus images to predict both an individuals overall 5 year cardiovascular risk (CVD) and the relative contribution of the component risk factors that comprise this risk. Methods: We used 165,907 retinal images from a database of 47,236 patient visits. Initially, each image was paired with biometric data age, ethnicity, sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD event wtihin 5 years of the retinal image acquisition. A risk sco…
Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trust
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Yong Loo Lin School of Medicine, NUS（新加坡国立大学杨潞龄医学院）
National Taiwan University（台湾大学）；King Abdullah University of Science and Technology（沙特阿拉伯国王阿卜杜拉科技大学）；Georgia Institute of Technology（佐治亚理工学院）；Bern University Hospital（伯尔尼大学医院）；Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology（沙特阿拉伯国王阿卜杜拉科技大学生物与环境科学与工程部门、计算机、电气与数学科学与工程部门）；Department of Ophthalmology, Bern University Hospital（伯尔尼大学医院眼科）
Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease which may result in loss of vision. There is limited access to high-quality relevant retinal images and poor understanding of the features defining sub-classes of this disease. Motivated by recent advances in machine learning we specifically explore the potential of generative modeling, using Generative Adversarial Networks (GANs) and style transferring, to facilitate clinical diagnosis and disease understanding by feature extraction. We design an analytic pipeline which first generates synthetic retinal images from clin…
LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis
School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast（贝尔法斯特女王大学电子、电气工程与计算机科学学院）；Abasyn University（阿巴辛大学）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
Renmin University of China（中国人民大学）；Beijing Visionary Intelligence Ltd.（北京维视智联科技有限公司）；Peking Union Medical College Hospital（北京协和医院）
This paper attacks an emerging challenge of multi-modal retinal disease recognition. Given a multi-modal case consisting of a color fundus photo (CFP) and an array of OCT B-scan images acquired during an eye examination, we aim to build a deep neural network that recognizes multiple vision-threatening diseases for the given case. As the diagnostic efficacy of CFP and OCT is disease-dependent, the network's ability of being both selective and interpretable is important. Moreover, as both data acquisition and manual labeling are extremely expensive in the medical domain, the network has to be re…
A BERT-Style Self-Supervised Learning CNN for Disease Identification from Retinal Images
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；McKeley School of Engineering, Washington University in St. Louis（工程学院，华盛顿大学圣路易斯分校）；Department of Neurology, Mayo Clinic（神经病学部，梅奥诊所）
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Disease Detection from Retinal Fundus Images
Hertie Institute for AI in Brain Health, University of Tübingen, Germany（人工智能与脑健康赫尔特研究所，图宾根大学，德国）；Tübingen AI Center, University of Tübingen, Germany（图宾根人工智能中心，图宾根大学，德国）
Full end-to-end diagnostic workflow automation of 3D OCT via foundation model-driven AI for retinal diseases
Sun Yat-sen University（中山大学）；Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；The Hong Kong Polytechnic University（香港理工大学）；Zhongshan City People's Hospital（中山市人民医院）；Guangdong Medical University（广东医科大学）；The Third Affiliated Hospital of Sun Yat-sen University（中山大学附属第三医院）；Zhaoqing Gaoyao People’s Hospital（肇庆高要人民医院）；Tianjin Medical University Eye Hospital（天津医科大学眼科医院）；Capital Medical University（首都医科大学）；Beijing Tongren Hospital（北京同仁医院）
REVEAL++: Differentiable Phenotypic Grouping for Vision-Language Retinal Modeling of Alzheimer's Disease Risk
University of Virginia（弗吉尼亚大学）；J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida（佛罗里达大学赫伯特·韦特海姆工程学院J. Crayton Pruitt家庭生物医学工程系）
RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School of Computing, Clemson University（克莱姆森大学计算机学院）；Washington University School of Medicine in St. Louis（圣路易斯华盛顿大学医学院）；The Keck School of Medicine of the University of Southern California（南加州大学凯克医学院）；Mayo Clinic（梅奥诊所）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）
Georgia Institute of Technology（佐治亚理工学院）；University of Cambridge（剑桥大学）；Bern University Hospital（伯尔尼大学医院）；National Taiwan University（台湾大学）；University of California, Berkeley（加州大学伯克利分校）；Tri-Service General Hospital（三军总医院）；Beijing Friendship Hospital（北京友谊医院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；University of Amsterdam（阿姆斯特丹大学）
In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologis…
Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Stanford University School of Medicine（斯坦福大学医学院）
REVEAL: Multimodal Vision-Language Alignment of Retinal Morphometry and Clinical Risks for Incident AD and Dementia Prediction
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（佛罗里达大学J. Crayton Pruitt家族生物医学工程系）；Research Institute of Electrical Communication, Tohoku University（东北大学电气通信研究所）；Department of Applied Mathematics & Statistics, Stony Brook University（石溪大学应用数学与统计学系）；Department of Computer Science, Stony Brook University（石溪大学计算机科学系）
Automated Multi-label Classification of Eleven Retinal Diseases: A Benchmark of Modern Architectures and a Meta-Ensemble on a Large Synthetic Dataset
Department of Cell Biology, University of Virginia, Charlottesville, VA, USA（细胞生物学系，弗吉尼亚大学）；Department of Ophthalmology, University of Virginia, Charlottesville, VA, USA（眼科学系，弗吉尼亚大学）；Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA（生物医学工程系，弗吉尼亚大学）；Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA（生物统计学系，北卡罗来纳大学教堂山分校）；Department of Oncology, People’s Hospital of Chongqing, Hechuan, Chongqing, China（肿瘤科，重庆人民医院）；Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA（电气与计算机工程系，旧 Dominion 大学）
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT
Medical University of Vienna（维也纳医科大学）；University of Basel（巴塞尔大学）；University of Michigan（密歇根大学）；NIHR Moorfields Biomedical Research Centre（NIHR穆尔菲尔德生物医学研究中心）；Technical University Munich（慕尼黑工业大学）；University of Southampton（南安普顿大学）
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has been hypothesized as a diagnostic site for AD detection owing to its anatomical connection with the brain. Developed AI models for this purpose have yet to provide a rational explanation about the decision and neither infer the stage of disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granular Neuron-level Explainer (LAVA)…
