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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.
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MR spectroscopy of head and neck cancer.
Abdel Razek, AA, Poptani, H
European journal of radiology. 2013;(6):982-9
Abstract
The aim of this review is to discuss the technique and potential applications of magnetic resonance spectroscopy (MRS) in head and neck cancer. We illustrate the technical issues related to data acquisition, post processing and interpretation of MRS of head and neck lesions. MRS has been used for differentiation of squamous cell carcinoma from normal tissue. The main potential clinical application of proton MRS ((1)H-MRS) is monitoring patients with head and neck cancer undergoing therapy. Pretreatment prediction of response to therapy can be done with phosphorus MRS ((31)P-MRS). Although performance of MRS of head and neck is challenging, technological advances in both software and hardware has the potential to impact on the clinical management of patients with head and neck cancer.
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Computer-aided diagnosis of diabetic retinopathy: a review.
Mookiah, MR, Acharya, UR, Chua, CK, Lim, CM, Ng, EY, Laude, A
Computers in biology and medicine. 2013;(12):2136-55
Abstract
Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images. A computer aided diagnosis system can significantly reduce the burden on the ophthalmologists and may alleviate the inter and intra observer variability. This review discusses the available methods of various retinal feature extractions and automated analysis.
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Metabolic profiling in disease diagnosis, toxicology and personalized healthcare.
Kamleh, MA, Spagou, K, Want, EJ
Current pharmaceutical biotechnology. 2011;(7):976-95
Abstract
Metabolic profiling employs a combination of sophisticated analytical tools to obtain global "untargeted" metabolic profiles from tissues, cells or biofluids. The resulting complex multivariate data are then modeled statistically to reveal differences between classes (e.g. dosed vs. control) and identify discriminatory metabolites. Metabolic profiling has a wide range of applications, encompassing nutrition, disease diagnosis, epidemiology and toxicology, providing insights into altered biological pathways and offering fresh mechanistic perspectives. Further, the untargeted nature of metabolic profiling can allow for new biomarkers of disease or toxic effect to be uncovered. In this review, key metabolic profiling technologies will be introduced and data analysis approaches described briefly. The role of metabolic profiling in disease diagnosis, toxicology and personalized healthcare will be discussed.
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Estimation of renal function in patients with chronic kidney disease.
Kooman, JP
Journal of magnetic resonance imaging : JMRI. 2009;(6):1341-6
Abstract
The risk of nephrogenic systemic fibrosis after gadolinium exposure is inversely related to renal function. Various methods are available to assess the glomerular filtration rate (GFR). Prediction formulas based on serum creatinine, such as the abbreviated Modification of Diet in Renal Disease (MDRD) formula, are most commonly used and appear acceptable for clinical purposes in the majority of patients with chronic renal failure. However, especially in patients at the extremes of body composition, the results from creatinine-based equations should be interpreted with caution. In those patients, additional methods, such as timed urine collections, predictions based on cystatin, single-shot radiotracer methods, or, optimally, inulin clearance could be considered. In this review, the strengths and limitations of different methods to assess GFR are discussed. Apart from inulin clearance, no method can be considered the gold standard in the assessment of GFR. In cases of doubt, the decision to use gadolinium-enhanced magnetic resonance imaging should always be based on clinical risk-benefit judgment. J. Magn. Reson. Imaging 2009;30:1341-1346. (c) 2009 Wiley-Liss, Inc.