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نتیجه جستجو - Diabetic retinopathy

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 Updating Diagnoses for Speed and Accuracy: Using AI, Cameras, Assays, and More
به روزرسانی تشخیص ها برای سرعت و دقت: استفاده از هوش مصنوعی ، دوربین ها ، سنجش ها و موارد دیگر-2020
When it comes to their health, people want answers right now. But clinicians cannot always make snap judgments about ailments or injuries. One way to help both general practitioners and patients is to introduce technologies that deliver quick and accurate diagnoses in a standard clinical setting. Here, IEEE Pulse features three examples of recently U.S. Food and Drug Administration (FDA)-approved diagnostic approaches that give patients fast responses about their conditions from a simple trip to their doctor, and without the need to see a specialist first. They include: • an autonomous artificial intelligence (AI) algorithm to diagnose diabetic retinopathy (DR); • an assay to spot infection with Mycoplasma genitalium, which can cause a sexually transmitted disease (STD) • an eye-tracking strategy to identify concussion.
مقاله انگلیسی
2 Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process
درسهایی که درباره هوش مصنوعی مستقل آموخته اند: یافتن راهی ایمن ، کارآمد و اخلاقی از طریق فرایند توسعه-2020
Artificial intelligence (AI) describes systems capable of making decisions of high cognitive complexity; autonomous AI systems in healthcare are AI systems that make clinical decisions without human oversight. Such rigorously validated medical diagnostic AI systems hold great promise for improving access to care, increasing accuracy, and lowering cost, while enabling specialist physicians to provide the greatest value by managing and treating patients whose outcomes can be improved. Ensuring that autonomous AI provides these benefits requires evaluation of the autonomous AI’s effect on patient outcome, design, validation, data usage, and accountability, from a bioethics and accountability perspective. We performed a literature review of bioethical principles for AI, and derived evaluation rules for autonomous AI, grounded in bioethical principles. The rules include patient outcome, validation, reference standard, design, data usage, and accountability for medical liability. Application of the rules explains successful US Food and Drug Administration (FDA) de novo authorization of an example, the first autonomous point-of-care diabetic retinopathy examination de novo authorized by the FDA, after a preregistered clinical trial. Physicians need to become competent in understanding the potential risks and benefits of autonomous AI, and understand its design, safety, efficacy and equity, validation, and liability, as well as how its data were obtained. The autonomous AI evaluation rules introduced here can help physicians understand limitations and risks as well as the potential benefits of autonomous AI for their patients. (Am J Ophthalmol 2020;214:134–142.
مقاله انگلیسی
3 Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening
ارزیابی تشخیصی الگوریتم های یادگیری عمیق برای غربالگری رتینوپاتی دیابتی-2019
Diabetic retinopathy (DR), the leading cause of blindness for working-age adults, is gen- erally intervened by early screening to reduce vision loss. A series of automated deep- learning-based algorithms for DR screening have been proposed and achieved high sensi- tivity and specificity ( > 90%). However, these deep learning models do not perform well in clinical applications due to the limitations of the existing publicly available fundus im- age datasets. In order to evaluate these methods in clinical situations, we collected 13,673 fundus images from 9598 patients. These images were divided into six classes by seven graders according to image quality and DR level. Moreover, 757 images with DR were se- lected to annotate four types of DR-related lesions. Finally, we evaluated state-of-the-art deep learning algorithms on collected images, including image classification, semantic seg- mentation and object detection. Although we obtain an accuracy of 0.8284 for DR classi- fication, these algorithms perform poorly on lesion segmentation and detection, indicating that lesion segmentation and detection are quite challenging. In summary, we are provid- ing a new dataset named DDR for assessing deep learning models and further exploring the clinical applications, particularly for lesion recognition.
Keywords: Diabetic retinopathy | Fundus image | Deep learning | Image classification | Semantic segmentation
مقاله انگلیسی
4 Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography
طبقه بندی بیماریهای شبکیه مرتبط با دیابت با استفاده از یک روش یادگیری عمیق در توموگرافی انسجام نوری-2019
Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically inter- pretable information in relevant images from a volume to classify diabetes-related retinal diseases. Methods: This article presents a new deep learning model, OCT-NET, which is a customized convolutional neural network for processing scans extracted from optical coherence tomography volumes. OCT-NET is applied to the classification of three conditions seen in SD-OCT volumes. Additionally, the proposed model includes a feedback stage that highlights the areas of the scans to support the interpretation of the results. This information is potentially useful for a medical specialist while assessing the prediction produced by the model. Results: The proposed model was tested on the public SERI-CUHK and A2A SD-OCT data sets containing healthy, diabetic retinopathy, diabetic macular edema and age-related macular degeneration. The experi- mental evaluation shows that the proposed method outperforms conventional convolutional deep learn- ing models from the state of the art reported on the SERI+CUHK and A2A SD-OCT data sets with a preci- sion of 93% and an area under the ROC curve (AUC) of 0.99 respectively. Conclusions: The proposed method is able to classify the three studied retinal diseases with high accuracy. One advantage of the method is its ability to produce interpretable clinical information in the form of highlighting the regions of the image that most contribute to the classifier decision.
Keywords: Optical coherence tomography | Deep learning models | Interpretability | Retinal diseases | Medical findings
مقاله انگلیسی
5 Multi-parametric optic disc segmentation using superpixel based feature classification
تقسیم بندی دیسک نوری چند پارامتری با استفاده از طبقه بندی ویژگی های مبتنی بر superpixel-2019
Glaucoma along with diabetic retinopathy is a major cause of vision blindness and is projected to affect over 80 million people by 2020. Recently, expert systems have matched human performance in disease diagnosis and proven to be highly useful in assisting medical experts in the diagnosis and detection of diseases. Hence, automated optic disc detection through intelligent systems is vital for early diagnosis and detection of Glaucoma. This paper presents a multi-parametric optic disk detection and localization method for retinal fundus images using region-based statistical and textural features. Highly discrimina- tive features are selected based on the mutual information criterion and a comparative analysis of four benchmark classifiers: Support Vector Machine, Random Forest (RF), AdaBoost and RusBoost is presented. The results of the proposed RF classifier based pipeline demonstrate its highly competitive performance (accuracies of 0.993, 0.988 and 0.993 on the DRIONS, MESSIDOR and ONHSD databases) with the state- of-the-art, thus making it a suitable candidate for patient management systems for early diagnosis of the Glaucoma.
Keywords: AdaBoostM1 | Glaucoma | RusBoost | Random forest | Support vector machine
مقاله انگلیسی
6 A deep learning interpretable classifier for diabetic retinopathy disease grading
طبقه بندی تفسیر آمیز عمیق برای درجه بندی بیماری رتینوپاتی دیابتی-2019
In this paper we present a diabetic retinopathy deep learning interpretable classifier. On one hand, it classifies retina images into different levels of severity with good performance. On the other hand, this classifier is able of explaining the classification results by assigning a score for each point in the hidden and input spaces. These scores indicate the pixel contribution to the final classification. To obtain these scores, we propose a new pixel-wise score propagation model that for every neuron, divides the observed output score into two components. With this method, the generated visual maps can be easily interpreted by an ophthalmologist in order to find the underlying statistical regularities that help to the diagnosis of this eye disease.
Keywords: Deep learning | Classification | Explanations | Diabetic retinopathy | Model interpretation
مقاله انگلیسی
7 Comorbidity network for chronic disease: A novel approach to understand type 2 diabetes progression
شبکه همبستگی برای بیماری مزمن: روش جدید برای درک پیشرفت نوع 2 دیابت-2018
Background: Chronic diseases management outside expensive hospital settings has become a major target for governments, funders and healthcare service providers. It is well known that chronic diseases such as Type 2 Diabetes (T2D) do not occur in isolation, and has a shared aetiology common to many other diseases and disorders. Diabetes Australia reports that it is associated with a myriad of complications, which affect the feet, eyes, kidneys, and cardiovascular health. For instance, nerve damage in the lower limbs affects around 13% of Australians with diabetes, diabetic retinopathy occurs in over 15% of Australians with diabetes, and diabetes is now the leading cause of end-stage kidney disease. Our research focus is therefore to understand the comorbidity pattern, which in turn can enhance our understanding of the multifactorial risk factors of chronic diseases like Type 2 Diabetes. Our research approach is based on utilising valuable indicators present in pre-existing administrative healthcare data, which are routinely collected but often neglected in health research. One such administrative healthcare data is the hospital admission and discharge data that carries information about diagnoses, which are represented in the form of ICD-10 diagnosis codes. Analysis of diagnoses codes and their relationships helps us construct comorbidity networks which can provide insights that can be used to understand chronic disease progression pattern and comorbidity network at a population level. This understanding can subsequently enable healthcare providers to formulate appropriate preventive health policies targeted to address high-risk chronic conditions. Methods and findings: The research utilises network theory principles applied to administrative healthcare data. Given the high rate of prevalence, we selected Type 2 Diabetes as the exemplar chronic disease. We have developed a research framework to understand and represent the progression of Type 2 diabetes, utilising graph theory and social network analysis techniques. We propose the concept of a ‘comorbidity network’ that can ef fectively model chronic disease comorbidities and their transition patterns, thereby representing the chronic disease progression. We further take the attribution effect of the comorbidities into account while generating the network; that is, we not only look at the pattern of disease in chronic disease patients, but also compare the disease pattern with that of non-chronic patients, to understand which comorbidities have a higher influence on the chronic disease pathway. The research framework enables us to construct a baseline comorbidity network for each of the two cohorts. It then compares and merges these two networks into single comorbidity network to discover the comorbidities that are exclusive to diabetic patients. This framework was applied on administrative data drawn from the Australian healthcare context. The overall dataset contained approximately 1.4 million admission records from 0.75 million patients, from which we filtered and sampled the records of 2300 diabetics and 2300 non-diabetic patients. We found significant difference in the health trajectory of diabetic and non-diabetic cohorts. The diabetic cohort exhibited more comorbidity prevalence and denser network properties. For example, in the diabetic cohort, heart and liver-related disorders, cataract etc. were more prevalent. Over time, the prevalence of diseases in the health trajectory of diabetic cohorts were almost double of the prevalence in the non-diabetic cohort, indicating entirely different ways of disease progression. Conclusions: The paper presents a research framework based on network theory to understand chronic disease progression along with associated comorbidities that manifest over time. The analysis methods provide insights that can enable healthcare providers to develop targeted preventive health management programs to reduce hospital admissions and associated hi
مقاله انگلیسی
8 A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis
ابزار اتوماتیک شده پیشگیری ا از رتینوپاتی دیابتی بر اساس تجزیه و تحلیل کامپیوتری تصویر شبکیه-2017
Aim: This paper presents a methodology and first results of an automatic detection system of first signs of Diabetic Retinopathy (DR) in fundus images, developed for the Health Ministry of the Andalusian Regional Government (Spain). Material and methods: The system detects the presence of microaneurysms and haemorrhages in retinography by means of techniques of digital image processing and supervised classification. Evaluation was conducted on 1058 images of 529 diabetic patients at risk of presenting evidence of DR (an image of each eye is provided). To this end, a ground-truth diagnosis was created based on gradations performed by 3 independent ophthalmology specialists. Results: The comparison between the diagnosis provided by the system and the reference clinical diagnosis shows that the system can work at a level of sensitivity that is similar to that achieved by experts (0.9380 sensitivity per patient against 0.9416 sensitivity of several specialists). False negatives have proven to be mild cases. Moreover, while the specificity of the system is significantly lower than that of human graders (0.5098), it is high enough to screen more than half of the patients unaffected by the disease. Conclusion: Results are promising in integrating this system in DR screening programmes. At an early stage, the system could act as a pre-screening system, by screening healthy patients (with no obvious signs of DR) and identifying only those presenting signs of the disease.
Keywords:Diabetic retinopathy | Early detection system | Automated screening | Retinal image processing | Computer-aided diagnosis
مقاله انگلیسی
9 سیستم مبتنی بر اثر کلی برای غربالگری اتوماتیک رتینوپاتی دیابتی
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 35 - تعداد صفحات فایل doc فارسی: 37
در این مقاله، روش مبتنی بر اثر کلی برای غربالگری رتینوپاتی دیابتی (DR) پیشنهاد می¬شود. این روش براساس ویژگی¬های برگرفته از نتایج چندین الگوریتم¬ پردازش تصویر شبکیه¬ای چشم، از قبیل سطح-تصویر (ارزیابی کیفیت، پیش¬غربالگری، AM/FM)، ضایعه خاص (میکروآنوریسم¬¬ها، ترشحات التهابی) و اجزای تشریحی چشم (ماکولا (لکه)، نقطه کور (دیسک چشمی)) است. بنابراین با استفاده از اثر کلی دسته¬بندی کننده¬های فراگیری ماشین، تصمیم واقعی درباره وجود بیماری گرفته می¬شود. ما روش خود را بر روی پایگاه داده Messidor که بصورت عمومی در دسترس است تست نموده¬ایم. نتایج به دست آمده نشان می¬دهند که روش پیشنهادی دارای 90% میزان حساسیت، 91% عملکرد اختصاصی و 90% دقت و 0.989 AUC در وضعیت بیماری/غیربیماری است. این نتایج در این حوزه بسیار رقابت آمیز هستند و نشان می¬دهند که پردازش تصویر شبکیه¬ای چشم روش معتبری برای غربالگری اتوماتیک DR است.
کلمات کلیدی: رتینوپاتی دیابتی | یادگیری اثر کلی | تصمیم گیری | فراگیری ماشین
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