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Artificial intelligence in diabetic retinopathy: A natural step to the future.
Padhy, SK, Takkar, B, Chawla, R, Kumar, A
Indian journal of ophthalmology. 2019;(7):1004-1009
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Abstract
Use of artificial intelligence in medicine in an evolving technology which holds promise for mass screening and perhaps may even help in establishing an accurate diagnosis. The ability of complex computing is to perform pattern recognition by creating complex relationships based on input data and then comparing it with performance standards is a big step. Diabetic retinopathy is an ever-increasing problem. Early screening and timely treatment of the same can reduce the burden of sight threatening retinopathy. Any tool which can aid in quick screening of this disorder and minimize requirement of trained human resource for the same would probably be a boon for patients and ophthalmologists. In this review we discuss the current status of use of artificial intelligence in diabetic retinopathy and few other common retinal disorders.
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Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.
Asiri, N, Hussain, M, Al Adel, F, Alzaidi, N
Artificial intelligence in medicine. 2019;:101701
Abstract
Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation and classification of lesions in fundus images. Many traditional machine-learning (ML) techniques based on hand-engineered features have been introduced. The recent emergence of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated the researchers to employ it for DR diagnosis, and many deep-learning-based methods have been introduced. In this paper, we review these methods, highlighting their pros and cons. In addition, we point out the challenges to be addressed in designing and learning about efficient, effective and robust deep-learning algorithms for various problems in DR diagnosis and draw attention to directions for future research.
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Deep learning in ophthalmology: a review.
Grewal, PS, Oloumi, F, Rubin, U, Tennant, MTS
Canadian journal of ophthalmology. Journal canadien d'ophtalmologie. 2018;(4):309-313
Abstract
Deep learning is an emerging technology with numerous potential applications in Ophthalmology. Deep learning tools have been applied to different diagnostic modalities including digital photographs, optical coherence tomography, and visual fields. These tools have demonstrated utility in assessment of various disease processes including cataracts, glaucoma, age-related macular degeneration, and diabetic retinopathy. Deep learning techniques are evolving rapidly, and will become more integrated into ophthalmic care. This article reviews the current evidence for deep learning in ophthalmology, and discusses future applications, as well as potential drawbacks.
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Automated Screening for Diabetic Retinopathy - A Systematic Review.
Nørgaard, MF, Grauslund, J
Ophthalmic research. 2018;(1):9-17
Abstract
PURPOSE Worldwide ophthalmologists are challenged by the rapid rise in the prevalence of diabetes. Diabetic retinopathy (DR) is the most common complication in diabetes, and possible consequences range from mild visual impairment to blindness. Repetitive screening for DR is cost-effective, but it is also a costly and strenuous affair. Several studies have examined the application of automated image analysis to solve this problem. Large populations are needed to assess the efficacy of such programs, and a standardized and rigorous methodology is important to give an indication of system performance in actual clinical settings. METHODS In a systematic review, we aimed to identify studies with methodology and design that are similar or replicate actual screening scenarios. A total of 1,231 publications were identified through PubMed, Cochrane Library, and Embase searches. Three manual search strategies were carried out to identify publications missed in the primary search. Four levels of screening identified 7 studies applicable for inclusion. RESULTS Seven studies were included. The detection of DR had high sensitivities (87.0-95.2%) but lower specificities (49.6-68.8%). False-negative results were related to mild DR with a low risk of progression within 1 year. Several studies reported missed cases of diabetic macular edema. A meta-analysis was not conducted as studies were not suitable for direct comparison or statistical analysis. CONCLUSION The study demonstrates that despite limited specificity, automated retinal image analysis may potentially be valuable in different DR screening scenarios with a relatively high sensitivity and a substantial workload reduction.
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Acute and training effects of resistance exercise on heart rate variability.
Kingsley, JD, Figueroa, A
Clinical physiology and functional imaging. 2016;(3):179-87
Abstract
Heart rate variability (HRV) has been used as a non-invasive method to evaluate heart rate (HR) regulation by the parasympathetic and sympathetic divisions of the autonomic nervous system. In this review, we discuss the effect of resistance exercise both acutely and after training on HRV in healthy individuals and in those with diseases characterized by autonomic dysfunction, such as hypertension and fibromyalgia. HR recovery after exercise is influenced by parasympathetic reactivation and sympathetic recovery to resting levels. Therefore, examination of HRV in response to acute exercise yields valuable insight into autonomic cardiovascular modulation and possible underlying risk for disease. Acute resistance exercise has shown to decrease cardiac parasympathetic modulation more than aerobic exercise in young healthy adults suggesting an increased risk for cardiovascular dysfunction after resistance exercise. Resistance exercise training appears to have no effect on resting HRV in healthy young adults, while it may improve parasympathetic modulation in middle-aged adults with autonomic dysfunction. Acute resistance exercise appears to decrease parasympathetic activity regardless of age. This review examines the acute and chronic effects of resistance exercise on HRV in young and older adults.
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A survey on computer aided diagnosis for ocular diseases.
Zhang, Z, Srivastava, R, Liu, H, Chen, X, Duan, L, Kee Wong, DW, Kwoh, CK, Wong, TY, Liu, J
BMC medical informatics and decision making. 2014;:80
Abstract
BACKGROUND Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients. METHOD We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed. RESULT We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively. CONCLUSION While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.