Diabetes-related retinal conditions can be detected by examining the posterior of the eye. By contrast, examining the anterior of the eye can reveal conditions affecting the front of the eye, such as changes to the eyelids, cornea, or crystalline lens. In this work, we studied whether external photographs of the front of the eye can reveal insights into both diabetic retinal diseases and blood glucose control. We developed a deep learning system (DLS) using external eye photographs of 145,832 patients with diabetes from 301 diabetic retinopathy (DR) screening sites in one US state, and evaluated the DLS on three validation sets containing images from 198 sites in 18 other US states. In validation set A (n=27,415 patients, all undilated), the DLS detected poor blood glucose control (HbA1c > 9%) with an area under receiver operating characteristic curve (AUC) of 70.2; moderate-or-worse DR with an AUC of 75.3; diabetic macular edema with an AUC of 78.0; and vision-threatening DR with an AUC of 79.4. For all 4 prediction tasks, the DLS's AUC was higher (p<0.001) than using available self-reported baseline characteristics (age, sex, race/ethnicity, years with diabetes). In terms of positive predictive value, the predicted top 5% of patients had a 67% chance of having HbA1c > 9%, and a 20% chance of having vision threatening diabetic retinopathy. The results generalized to dilated pupils (validation set B, 5,058 patients) and to a different screening service (validation set C, 10,402 patients). Our results indicate that external eye photographs contain information useful for healthcare providers managing patients with diabetes, and may help prioritize patients for in-person screening. Further work is needed to validate these findings on different devices and patient populations (those without diabetes) to evaluate its utility for remote diagnosis and management.
Diabetes is a global epidemic and it is increasing at an alarming rate. The International Diabetes Federation (IDF) projected that the total number of people with diabetes globally may increase by 48%, from 425 million (year 2017) to 629 million (year 2045). Moreover, diabetes had caused millions of deaths and the number is increasing drastically. Therefore, this paper addresses the background of diabetes and its complications. In addition, this paper investigates innovative applications and past researches in the areas of diabetes management system with applied eye fundus and tongue digital i…
Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms
Islamic Azad University（伊斯兰阿扎德大学）；University of Tehran（德黑兰大学）
Google Health（谷歌健康）；Advanced Clinical（Advanced Clinical公司）；EyePACS Inc（EyePACS公司）；Los Angeles County Department of Health Services（洛杉矶县卫生服务部）；University of Southern California Keck School of Medicine（南加州大学凯克医学院）；Roski Eye Institute（罗斯基眼科研究所）；Emory University School of Medicine（埃默里大学医学院）；Aravind Eye Hospital（阿拉文德眼科医院）；Chang Gung Memorial Hospital（长庚纪念医院）
External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about additional systemic medical conditions. We developed a deep learning system (DLS) that takes external eye photos as input and predicts multiple systemic parameters, such as those related to the liver (albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH); and blood count (Hgb, WBC, platelets). Development leveraged 151,237 i…
Detecting diabetic retinopathy severity through fundus images using an ensemble of classifiers
Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）；“Iuliu Hatieganu” University of Medicine and Pharmacy（“尤利乌·哈采加努”医药大学）；Emergency County Hospital（急诊县医院）
Purpose: To demonstrate that retinal microvasculature per se is a reliable biomarker for Diabetic Retinopathy (DR) and, by extension, cardiovascular diseases. Methods: Deep Learning Convolutional Neural Networks (CNN) applied to color fundus images for semantic segmentation of the blood vessels and severity classification on both vascular and full images. Vessel reconstruction through harmonic descriptors is also used as a smoothing and de-noising tool. The mathematical background of the theory is also outlined. Results: For diabetic patients, at least 93.8% of DR No-Refer vs. Refer classifica…
Sirindhorn International Institute of Technology, Thammasat University（泰国国立法政大学诗琳通国际技术学院）
Diabetic retinopathy is an eye-related pathology creating abnormalities and causing visual impairment, proper treatment of which requires identifying irregularities. This research uses a hemorrhage detection method and compares classification of conventional and deep features. Especially, method identifies hemorrhage connected with blood vessels or reside at retinal border and reported challenging. Initially, adaptive brightness adjustment and contrast enhancement rectify degraded images. Prospective locations of hemorrhages are estimated by a Gaussian matched filter, entropy thresholding, and…
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）
Saratoga High School（萨拉托加高中）；BRAC University JPG School of Public Health（BRAC大学JPG公共卫生学院）
Diabetes is one of the most prevalent chronic diseases in Bangladesh, and as a result, Diabetic Retinopathy (DR) is widespread in the population. DR, an eye illness caused by diabetes, can lead to blindness if it is not identified and treated in its early stages. Unfortunately, diagnosis of DR requires medically trained professionals, but Bangladesh has limited specialists in comparison to its population. Moreover, the screening process is often expensive, prohibiting many from receiving timely and proper diagnosis. To address the problem, we introduce a deep learning algorithm which screens f…
Eindhoven University of Technology（埃因霍温理工大学）；Maastricht University Medical Center+（马斯特里赫特大学医学中心+）
Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employed to differentiate between fundus images from individuals with and without T2D. We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC). A multi-target learning approach to simultaneously output r…
Universidad Nacional de Colombia（哥伦比亚国立大学）；Fundación Oftalmológica Nacional（国家眼科基金会）；Universidad del Rosario（罗萨里奥大学）
Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the diagnosis by screening each patient and analyzing the retinal lesions via ocular imaging. In practice, such analysis is time-consuming and cumbersome to perform. This paper presents a model for automatic DR classification on eye fundus images. The approach identifies the main ocular lesions related to DR and subsequently diagnoses the illness. The proposed method follows the same workflow as the clinicians, provid…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Indiana University-Purdue University（印第安纳大学-普渡大学）
Diabetes Mellitus (DM) can lead to significant microvasculature disruptions that eventually causes diabetic retinopathy (DR), or complications in the eye due to diabetes. If left unchecked, this disease can increase over time and eventually cause complete vision loss. The general method to detect such optical developments is through examining the vessels, optic nerve head, microaneurysms, haemorrhage, exudates, etc. from retinal images. Ultimately this is limited by the number of experienced ophthalmologists and the vastly growing number of DM cases. To enable earlier and efficient DR diagnosi…
Netaji Subhas Institute of Technology（内塔吉·苏巴斯技术学院）；Pandit Deendayal Energy University（潘迪特·迪恩达亚尔能源大学）；National Institute of Technology Delhi（德里国立理工学院）；AMC MET Medical College（AMC MET医学院）
Patients with long-standing diabetes often fall prey to Diabetic Retinopathy (DR) resulting in changes in the retina of the human eye, which may lead to loss of vision in extreme cases. The aim of this study is two-fold: (a) create deep learning models that were trained to grade degraded retinal fundus images and (b) to create a browser-based application that will aid in diagnostic procedures by highlighting the key features of the fundus image. In this research work, we have emulated the images plagued by distortions by degrading the images based on multiple different combinations of Light Tr…
Google Health, Google LLC（谷歌有限责任公司谷歌健康）；Advanced Clinical；Google Research, Google LLC（谷歌有限责任公司谷歌研究院）；Adecco Staffing（德科 staffing）；EyePACS LLC（EyePACS有限责任公司）；Rajavithi Hospital（拉贾维蒂医院）；College of Medicine, Rangsit University（兰实大学医学院）
Diabetic retinopathy (DR) screening is instrumental in preventing blindness, but faces a scaling challenge as the number of diabetic patients rises. Risk stratification for the development of DR may help optimize screening intervals to reduce costs while improving vision-related outcomes. We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse ("Mild+") DR in diabetic patients undergoing DR screening. The two versions used either three-fields or a single field of color fundus photographs (CFPs) as input. The training set was derived fro…
Deploying and Evaluating Multiple Deep Learning Models on Edge Devices for Diabetic Retinopathy Detection
Ghana Communication Technology University（加纳通信技术大学）；Council for Scientific and Industrial Research（科学与工业研究委员会）；Shenzhen Technology University（深圳技术大学）；Kwame Nkrumah University of Science and Technology（夸梅·恩克鲁玛科技大学）
FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy
Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, West Bengal（计算机科学与工程系，穆拉纳·阿布·卡尔·拉马纳特技术大学，西孟加拉邦）；Department of Computer Science and Engineering, MCKV Institute of Engineering, West Bengal（计算机科学与工程系，MCKV工程学院，西孟加拉邦）；Department of Computer Science and Engineering, OmDayal Group of Institutions, West Bengal（计算机科学与工程系，OmDayal学院集团，西孟加拉邦）
King Saud University（沙特国王大学）；Imam Mohammad Ibn Saud Islamic University (IMSIU)（伊玛目穆罕默德·本·沙特伊斯兰大学）；Princess Nourah bint Abdulrahman University（努拉·宾特·阿卜杜勒·拉赫曼公主大学）；King Faisal University（费萨尔国王大学）；King Khaled Eye Specialist Hospital（哈立德国王眼科专科医院）
The prevalence of diabetic retinopathy (DR) has reached 34.6% worldwide and is a major cause of blindness among middle-aged diabetic patients. Regular DR screening using fundus photography helps detect its complications and prevent its progression to advanced levels. As manual screening is time-consuming and subjective, machine learning (ML) and deep learning (DL) have been employed to aid graders. However, the existing CNN-based methods use either pre-trained CNN models or a brute force approach to design new CNN models, which are not customized to the complexity of fundus images. To overcome…
