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  1. Mookiah MR, Acharya UR, Koh JE, Chandran V, Chua CK, Tan JH, et al.
    Comput Biol Med, 2014 Oct;53:55-64.
    PMID: 25127409 DOI: 10.1016/j.compbiomed.2014.07.015
    Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.
    Matched MeSH terms: Macular Degeneration/diagnosis*
  2. Mookiah MR, Acharya UR, Fujita H, Koh JE, Tan JH, Noronha K, et al.
    Comput Biol Med, 2015 Aug;63:208-18.
    PMID: 26093788 DOI: 10.1016/j.compbiomed.2015.05.019
    Age-related Macular Degeneration (AMD) is an irreversible and chronic medical condition characterized by drusen, Choroidal Neovascularization (CNV) and Geographic Atrophy (GA). AMD is one of the major causes of visual loss among elderly people. It is caused by the degeneration of cells in the macula which is responsible for central vision. AMD can be dry or wet type, however dry AMD is most common. It is classified into early, intermediate and late AMD. The early detection and treatment may help one to stop the progression of the disease. Automated AMD diagnosis may reduce the screening time of the clinicians. In this work, we have introduced LCP to characterize normal and AMD classes using fundus images. Linear Configuration Coefficients (CC) and Pattern Occurrence (PO) features are extracted from fundus images. These extracted features are ranked using p-value of the t-test and fed to various supervised classifiers viz. Decision Tree (DT), Nearest Neighbour (k-NN), Naive Bayes (NB), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to classify normal and AMD classes. The performance of the system is evaluated using both private (Kasturba Medical Hospital, Manipal, India) and public domain datasets viz. Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) using ten-fold cross validation. The proposed approach yielded best performance with a highest average accuracy of 97.78%, sensitivity of 98.00% and specificity of 97.50% for STARE dataset using 22 significant features. Hence, this system can be used as an aiding tool to the clinicians during mass eye screening programs to diagnose AMD.
    Matched MeSH terms: Macular Degeneration/diagnosis*
  3. Aslam TM, Zaki HR, Mahmood S, Ali ZC, Ahmad NA, Thorell MR, et al.
    Am J Ophthalmol, 2018 Jan;185:94-100.
    PMID: 29101008 DOI: 10.1016/j.ajo.2017.10.015
    PURPOSE: To develop a neural network for the estimation of visual acuity from optical coherence tomography (OCT) images of patients with neovascular age-related macular degeneration (AMD) and to demonstrate its use to model the impact of specific controlled OCT changes on vision.

    DESIGN: Artificial intelligence (neural network) study.

    METHODS: We assessed 1400 OCT scans of patients with neovascular AMD. Fifteen physical features for each eligible OCT, as well as patient age, were used as input data and corresponding recorded visual acuity as the target data to train, validate, and test a supervised neural network. We then applied this network to model the impact on acuity of defined OCT changes in subretinal fluid, subretinal hyperreflective material, and loss of external limiting membrane (ELM) integrity.

    RESULTS: A total of 1210 eligible OCT scans were analyzed, resulting in 1210 data points, which were each 16-dimensional. A 10-layer feed-forward neural network with 1 hidden layer of 10 neurons was trained to predict acuity and demonstrated a root mean square error of 8.2 letters for predicted compared to actual visual acuity and a mean regression coefficient of 0.85. A virtual model using this network demonstrated the relationship of visual acuity to specific, programmed changes in OCT characteristics. When ELM is intact, there is a shallow decline in acuity with increasing subretinal fluid but a much steeper decline with equivalent increasing subretinal hyperreflective material. When ELM is not intact, all visual acuities are reduced. Increasing subretinal hyperreflective material or subretinal fluid in this circumstance reduces vision further still, but with a smaller gradient than when ELM is intact.

    CONCLUSIONS: The supervised machine learning neural network developed is able to generate an estimated visual acuity value from OCT images in a population of patients with AMD. These findings should be of clinical and research interest in macular degeneration, for example in estimating visual prognosis or highlighting the importance of developing treatments targeting more visually destructive pathologies.

    Matched MeSH terms: Wet Macular Degeneration/diagnosis*
  4. Cheung CMG, Ong PG, Neelam K, Tan PC, Shi Y, Mitchell P, et al.
    Ophthalmology, 2017 09;124(9):1305-1313.
    PMID: 28501376 DOI: 10.1016/j.ophtha.2017.03.056
    PURPOSE: To determine the 6-year incidence of early and late age-related macular degeneration (AMD) in a Singaporean Malay population and to validate the Age-Related Eye Disease Study (AREDS) simplified severity scale in Asians.

    DESIGN: Prospective, population cohort study.

    PARTICIPANTS: The Singapore Malay Eye Study baseline participants (age, ≥40 years; 2006-2008) were followed up in 2011 through 2013, and 1901 of 3280 of eligible participants (72.1%) took part.

    METHODS: Fundus photographs were graded using the Wisconsin AMD grading system.

    MAIN OUTCOME MEASURES: Incidence of early and late AMD.

    RESULTS: Gradable fundus photographs were available for 1809 participants who attended both baseline and 6-year follow-up examinations. The age-standardized incidences of early and late AMD were 5.89% (95% confidence interval [CI], 4.81-7.16) and 0.76% (95% CI, 0.42-1.29), respectively. The 5-year age-standardized incidence of early AMD (calculated based on the 6-year incidence) was lower in our population (5.58%; 95% CI, 4.43-7.01) compared with the Beaver Dam Eye Study population (8.19%). The incidence of late AMD in our population was similar to that of the Beaver Dam Eye Study population (0.98% [95% CI, 0.49-1.86] vs. 0.91%), the Blue Mountains Eye Study population (1.10% [95% CI, 0.52-9.56] vs. 1.10%), and the Hisayama Study population (1.09% [95% CI, 0.54-4.25] vs. 0.84%). The incidence of late AMD increased markedly with increasing baseline AREDS score (step 0, 0.23%; step 4, 9.09%).

    CONCLUSIONS: This study documented the incidence of early and late AMD in a Malay population. The AREDS simplified severity scale is useful in predicting the risk of late AMD development in Asians.

    Matched MeSH terms: Macular Degeneration/diagnosis
  5. Eldem B, Lai TYY, Ngah NF, Vote B, Yu HG, Fabre A, et al.
    Graefes Arch Clin Exp Ophthalmol, 2018 May;256(5):963-973.
    PMID: 29502232 DOI: 10.1007/s00417-017-3890-8
    PURPOSE: To describe intravitreal ranibizumab treatment frequency, clinical monitoring, and visual outcomes (including mean central retinal thickness [CRT] and visual acuity [VA] changes from baseline) in neovascular age-related macular degeneration (nAMD) in real-world settings across three ranibizumab reimbursement scenarios in the Middle East, North Africa, and the Asia-Pacific region.

    METHODS: Non-interventional multicenter historical cohort study of intravitreal ranibizumab use for nAMD in routine clinical practice between April 2010 and April 2013. Eligible patients were diagnosed with nAMD, received at least one intravitreal ranibizumab injection during the study period, and had been observed for a minimum of 1 year (up to 3 years). Reimbursement scenarios were defined as self-paid, partially-reimbursed, and fully-reimbursed.

    RESULTS: More than three-fourths (n = 2521) of the analysis population was partially-reimbursed for ranibizumab, while 16.4% (n = 532) was fully-reimbursed, and 5.8% was self-paid (n = 188). The average annual ranibizumab injection frequency was 4.1 injections in the partially-reimbursed, 4.7 in the fully-reimbursed and 2.6 in the self-paid populations. The average clinical monitoring frequency was estimated to be 6.7 visits/year, with similar frequencies observed across reimbursement categories. On average, patients experienced VA reduction of -0.7 letters and a decrease in CRT of -44.4 μm. The greatest mean CRT change was observed in the self-paid group, with -92.6 μm.

    CONCLUSIONS: UNCOVER included a large, heterogeneous ranibizumab-treated nAMD population in real-world settings. Patients in all reimbursement scenarios attained vision stability on average, indicating control of disease activity.

    Matched MeSH terms: Wet Macular Degeneration/diagnosis
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