Development of automated analytical capability for the early detection of diabetes mellitus

The total profile of volatile metabolites in urine of patients with diabetes mellitus was studied. Because of the drastic abnormalities in the metabolism of carbohydrates, lipids, and proteins connected with diabetes it was expected that apart from acetone further characteristic abnormalities occur in the profiles if volatile urinary metabolites in cases of diabetes mellitus. Quantitative and qualitative changes were found in these urines as compared to the urines of normal subjects

A Survey on Detection of Macular Retinal Edema

Retinal images of humans play an main role in the detection and diagnosis of many eye diseases for ophthalmologists. Diabetic Retinopathy is a severe and largely spread eye disease which can be regarded as manifestation of diabetes on retina. Retinopathy exactly means damage to retina. There are two types of retinopathy.The most common type is background or non proliferative diabetic retinopathy.A feature extraction technique is introduced to capture the global characteristics of the fundus images and inequity the normal from DME images.Exudates are the primary sign of diabetic retinopathy.So detection of exudates is very important in diagnosis of diabetic retinopathy.While detect the exudates, segmentation of blood vessels in retinal images is necessary

Automated Diagnostic System for Grading of Diabetic Retinopathy Stages from Fundus Images Using Texture Features

Computational methodologies and medical imaging are become an important part of real time applications. These techniques transform medicine by providing effective health care diagnosis in all major disease areas. This will allow the clinicians to understand life-saving information using less invasive techniques. Diabetes is a rapidly increasing worldwide disease that occurs when the body is unable to metabolize glucose. It increases the risk of a range of eye diseases, but the main cause of blindness associated with diabetes is Diabetic retinopathy (DR). A new feature based automated technique for diagnosis and grading of normal, Nonproliferative diabetic retinopathy (NPDR) and Proliferative diabetic retinopathy (PDR) is proposed in this paper. This method involves preprocessing of retinal images, detection of lesions, extraction of blood vessels and extraction of texture features such as local binary pattern, Laws texture energy and Fractal Dimension. These features were used for classification of DR stages by means of supervised classifiers namely Support vector machine (SVM) and Extreme Learning Machine (ELM). In this work, in addition to morphological features, statistically significant texture features were also used for classification. It was found that the average classification accuracy of 98.88%, sensitivity and specificity of 100% respectively achieved using ELM classifier with texture features. The results were validated by comparing with expert ophthalmologists. This proposed automated diagnostic system reduces the work of professionals during mass screening of DR stages

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

Corneal confocal microscopy for diagnosis of diabetic peripheral neuropathy: an analysis of patients with diabetes screened as part of the South Manchester Diabetic Retinopathy Screening Service

Background and Aims: Quantitative assessment of small nerve fibre damage is key to the early diagnosis of diabetic peripheral neuropathy (DPN) and assessment of its progression. Corneal confocal microscopy (CCM) is a non-invasive, in-vivo diagnostic technique that provides an accurate surrogate biomarker for small fibre neuropathy. Its diagnostic efficacy has been previously validated in several studies. This thesis uses CCM images obtained, for the first time, in a large cohort of patients whose CCM examinations were undertaken during retinopathy screening in primary care. The following were the primary aims of the study: 1. To determine the prevalence of diabetic peripheral neuropathy, as defined by CCM parameters in a cohort of people with diabetes 2.To assess whether abnormalities in corneal nerve fibre morphology are present during the first two years following diabetes diagnosis. 3. To assess whether abnormalities in corneal nerve morphology are present prior to any retinopathy, defined as grade 1 or more. 4. To assess whether abnormalities in corneal nerve morphology are present prior to clinical evidence of diabetic neuropathy, as defined by diabetic neuropathic symptom (DNS) scoring of 1 or more The hypotheses for these main aims were that firstly, the prevalence of diabetic peripheral neuropathy, defined using CCM parameters would be lower in this population in comparison to previous CCM studies using patients under the hospital eye service to determine prevalence of DPN. There will be evidence of abnormalities in corneal nerve fibre morphology in some, but not all, patients with diabetic disease duration of less than or equal to 2 years, patients with retinopathy and maculopathy grade 0 and patients with a DNS score of 0. Methods: In this retrospective, primary care, cross-sectional study, 427 patients with diabetes (18 T1DM, 407 T2DM, 2 unknown) and 40 healthy controls underwent quantification of corneal nerve parameters using both automated and semi-automated analysis software. Clinical levels of neuropathy were assessed via diabetic neuropathy symptom score (DNS). Diabetic Retinopathy (DR) was graded using the Early Treatment Diabetic Retinopathy Study (ETDRS) grading scale. Results: Patients with diabetes demonstrated significant differences in all nerve parameters in comparison to healthy control subjects (p0.05). There was no significant difference in any CCM parameters between white and black patients with diabetes (p>0.05). Automated software showed poor agreement with semi-automated results, with a general underestimation for CNFD, CNFL and CNBD. Conclusion: In patients attending primary care screening, CCM in a sensitive biomarker for DPN. Semi-automated CCM quantification reliably detected corneal nerve abnormalities soon after diagnosis of diabetes. Changes in corneal nerve morphology were present prior to any neuropathy symptoms or retinopathy. CCM measured using automatic software requires development to improve agreement with semi-automated analysis

Diabetic retinopathy screening: global and local perspective

Diabetes mellitus has become a global epidemic. It causes significant macrovascular complications such as coronary artery disease, peripheral artery disease, and stroke; as well as microvascular complications such as retinopathy, nephropathy, and neuropathy. Diabetic retinopathy is known to be the leading cause of blindness in the working-age population and may be asymptomatic until vision loss occurs. Screening for diabetic retinopathy has been shown to reduce blindness by timely detection and effective laser treatment. Diabetic retinopathy screening is being done worldwide either as a national screening programme or hospital-based project or as a community-based screening programme. In this article, we review different methods of screening including grading used to detect the severity of sight-threatening retinopathy and the newer screening methods. This review also includes the method of systematic screening being carried out in Hong Kong, a system that has helped to identify diabetic retinopathy among all attendees in public primary care clinics using a Hong Kong–wide public patients’ database.published_or_final_versio

Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images

Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed

Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge.

Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many image-driven clinical disciplines, including ophthalmology. A combination of the increasing availability of large datasets and computing power with revolutionary progress in deep learning has created unprecedented opportunities for major breakthrough improvements in the performance and accuracy of automated diagnoses that primarily focus on image recognition and feature detection. Such an automated disease classification would significantly improve the accessibility, efficiency, and cost-effectiveness of eye care systems where it is less dependent on human input, potentially enabling diagnosis to be cheaper, quicker, and more consistent. Although this technology will have a profound impact on clinical flow and practice patterns sooner or later, translating such a technology into clinical practice is challenging and requires similar levels of accountability and effectiveness as any new medication or medical device due to the potential problems of bias, and ethical, medical, and legal issues that might arise. The objective of this review is to summarize the opportunities and challenges of this transition and to facilitate the integration of artificial intelligence (AI) into routine clinical practice based on our best understanding and experience in this area