Industrial Internet of Things (IIoT) with Cloud Teleophthalmology-Based Age-Related Macular Degeneration (AMD) Disease Prediction Model

2021 ◽  
pp. 161-172
Author(s):  
R. J. Kavitha ◽  
T. Avudaiyappan ◽  
T. Jayasankar ◽  
J. Arputha Vijaya Selvi
Keyword(s):  
Internet Of Things ◽  
Prediction Model ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Disease Prediction ◽  
Industrial Internet Of Things ◽  
Age Related ◽  
Industrial Internet

Major Predictive Factors for Progression of Early to Late Age-Related Macular Degeneration

2020 ◽  
Vol 243 (6) ◽  
pp. 444-452 ◽  
Author(s):  
Vasilena Sitnilska ◽  
Eveline Kersten ◽  
Lebriz Altay ◽  
Tina Schick ◽  
Philip Enders ◽  
...  
Keyword(s):  
Prediction Model ◽  
Macular Degeneration ◽  
Area Under The Curve ◽  
Age Related Macular Degeneration ◽  
Advanced Age ◽  
Fundus Photography ◽  
Imaging Biomarkers ◽  
Nucleotide Polymorphisms ◽  
Phenotypic Profile ◽  
Age Related

<b><i>Introduction:</i></b> We present a prediction model for progression from early/intermediate to advanced age-related macular degeneration (AMD) within 5.9 years. <b><i>Objectives:</i></b> To evaluate the combined role of genetic, nongenetic, and phenotypic risk factors for conversion from early to late AMD over ≥5 years. <b><i>Methods:</i></b> Baseline phenotypic characteristics were evaluated based on color fundus photography, spectral-domain optical coherence tomography, and infrared images. Genotyping for 36 single-nucleotide polymorphisms as well as systemic lipid and complement measurements were performed. Multivariable backward logistic regression resulted in a final prediction model. <b><i>Results and Conclusions:</i></b> During a mean of 5.9 years of follow-up, 22.4% (<i>n</i> = 52) of the patients (<i>n</i> = 232) showed progression to late AMD. The multivariable prediction model included age, <i>CFH</i> variant rs1061170, pigment abnormalities, drusenoid pigment epithelial detachment (DPED), and hyperreflective foci (HRF). The model showed an area under the curve of 0.969 (95% confidence interval 0.948–0.990) and adequate calibration (Hosmer-Lemeshow test, <i>p</i> = 0.797). In addition to advanced age and carrying a <i>CFH</i> variant, pigment abnormalities, DPED, and HRF are relevant imaging biomarkers for conversion to late AMD. In clinical routine, an intensified monitoring of patients with a high-risk phenotypic profile may be suitable for the early detection of conversion to late AMD.

scholarly journals Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 454 ◽  
Author(s):  
Jung-Hyok Kwon ◽  
Eui-Jik Kim
Keyword(s):  
Feature Selection ◽  
Internet Of Things ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Model Building ◽  
Failure Prediction ◽  
Support Vector ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
New Feature

This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.

Risk Prediction Model for Progression of Age-Related Macular Degeneration

2016 ◽  
Vol 57 (1) ◽  
pp. 32-36 ◽  
Author(s):  
Ko Un Shin ◽  
Su Jeong Song ◽  
Jeong Hun Bae ◽  
Mi Yeon Lee
Keyword(s):  
Prediction Model ◽  
Macular Degeneration ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Age Related Macular Degeneration ◽  
Age Related

INVESTIGATING A GROWTH PREDICTION MODEL IN ADVANCED AGE-RELATED MACULAR DEGENERATION WITH SOLITARY GEOGRAPHIC ATROPHY USING QUANTITATIVE AUTOFLUORESCENCE

Retina ◽  
2020 ◽  
Vol 40 (9) ◽  
pp. 1657-1664 ◽  
Author(s):  
Gregor S. Reiter ◽  
Reinhard Told ◽  
Lukas Baumann ◽  
Stefan Sacu ◽  
Ursula Schmidt-Erfurth ◽  
...  
Keyword(s):  
Prediction Model ◽  
Macular Degeneration ◽  
Geographic Atrophy ◽  
Age Related Macular Degeneration ◽  
Advanced Age ◽  
Growth Prediction ◽  
Age Related

Age related macular degeneration (ARMD) – pathophysiological and clinical features and therapeutic concepts

2001 ◽  
Vol 58 (1) ◽  
pp. 28-35 ◽  
Author(s):  
Ursula Körner-Stiefbold
Keyword(s):  
Macular Degeneration ◽  
Clinical Features ◽  
Altersbedingte Makuladegeneration ◽  
Age Related Macular Degeneration ◽  
Genetische Faktoren ◽  
Age Related ◽  
Therapeutic Concepts

Die altersbedingte Makuladegeneration (AMD) ist eine der häufigsten Ursachen für einen irreversiblen Visusverlust bei Patienten über 65 Jahre. Nahezu 30% der über 75-Jährigen sind von einer AMD betroffen. Trotz neuer Erkenntnisse in der Grundlagenforschung ist die Ätiologie, zu der auch genetische Faktoren gehören, noch nicht völlig geklärt. Aus diesem Grund sind die Behandlungsmöglichkeiten zum jetzigen Zeitpunkt noch limitiert, so dass man lediglich von Therapieansätzen sprechen kann. Die derzeit zur Verfügung stehenden Möglichkeiten wie medikamentöse, chirurgische und laser- und strahlentherapeutische Maßnahmen werden beschrieben.

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