scholarly journals A Novel Retinal Vascular Feature and Machine Learning-based Brain White Matter Lesion Prediction Model

2021 ◽  
Author(s):  
Alauddin Bhuiyan ◽  
Pallab Kanti Roy ◽  
Tasin Bhuiyan ◽  
Elsdon Storey ◽  
Walter P Abhayaratna ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Model ◽  
Vascular System ◽  
White Matter Lesion ◽  
Early Stage ◽  
Volume Load ◽  
Brain White Matter ◽  
Regular Screening ◽  
Proposed Model ◽  
Severity Prediction

White matter lesion (WML) is one of the common cerebral abnormalities, it indicates changes in the white matter of human brain and have shown significant association with stroke, dementia and deaths. Magnetic resonance imaging (MRI) of the brain is frequently used to diagnose white matter lesion (WML) volume. Regular screening can detect WML in early stage and save from severe consequences. Current option of MRI based diagnosis is impractical for regular screening because of its high expense and unavailability. Thus, earlier screening and prediction of the WML volume/load specially in the rural and remote areas becomes extremely difficult. Research has shown that changes in the retinal micro vascular system reflect changes in the cerebral micro vascular system. Using this information, we have proposed a retinal image based WML volume and severity prediction model which is very convenient and easy to operate. Our proposed model can help the physicians to detect the patients who need immediate and further MRI based detail diagnosis of WML. Our model uses quantified measurement of retinal micro-vascular signs (such as arteriovenular nicking (AVN), Opacity (OP) and focal arteriolar narrowing (FAN)) as input and estimate the WML volume/load and classify its severity. We evaluate our proposed model on a dataset of 111 patients taken from the ENVISion study which have retinal and MRI images for each patient. Our model shows high accuracy in estimating the WML volume, mean square error (MSE) between our predicted WML load and manually annotated WML load is 0.15. The proposed model achieves an F1 score of 0.92 in classifying the patients having mild and severe WML load. The preliminary results of our study indicate that quantified measurement of retinal micro-vascular features (AVN, OP and FAN) can more accurately identify the patients who have high risk of cardio-vascular diseases and dementia.

scholarly journals Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1620 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Norma Latif Fitriyani ◽  
Muhammad Anshari ◽  
Pavel Stasa ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Prediction Model ◽  
Deep Neural Network ◽  
Early Stage ◽  
Individual Risk ◽  
Proposed Model ◽  
Individual Risk Factors

Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

scholarly journals Evolution of Cortical and White Matter Lesion Load in Early-Stage Multiple Sclerosis: Correlation With Neuroaxonal Damage and Clinical Changes

2020 ◽  
Vol 11 ◽  
Author(s):  
Ramona-Alexandra Todea ◽  
Po-Jui Lu ◽  
Mario Joao Fartaria ◽  
Guillaume Bonnier ◽  
Renaud Du Pasquier ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
White Matter Lesion ◽  
Early Stage ◽  
Lesion Load ◽  
Clinical Changes

Brain white-matter lesion and mental function in cerebral infarction

1990 ◽  
Vol 11 (3) ◽  
pp. 291
Author(s):  
S. Kobayshi ◽  
J. Fukuda ◽  
K. Okada ◽  
H. Koide
Keyword(s):  
White Matter ◽  
Cerebral Infarction ◽  
White Matter Lesion ◽  
Mental Function ◽  
Brain White Matter

scholarly journals Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability

2013 ◽  
Vol 7 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Christos P. Loizou ◽  
Efthyvoulos C. Kyriacou ◽  
Ioannis Seimenis ◽  
Marios Pantziaris ◽  
Styliani Petroudi ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
White Matter Lesion ◽  
Brain White Matter ◽  
Lesion Classification

scholarly journals Prediction Model Design for Vibration Severity of Rotating Machine Based on Sequence-to-Sequence Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Zhiqiang Wang ◽  
Hong Qian ◽  
Dongliang Zhang ◽  
Yingchen Wei
Keyword(s):  
Prediction Model ◽  
Steam Turbine ◽  
Prediction Method ◽  
Power Production ◽  
Turbine Rotor ◽  
Steam Turbine Rotor ◽  
Rotating Machine ◽  
Proposed Model ◽  
Severity Prediction ◽  
Efficient Power

Steam turbine rotor system is a main part of the power production process. Accurate prediction of the turbine rotor operation state leads to timely detection of the hidden danger and accordingly ensures the efficient power production. The vibration severity reflects the vibration intensity and the working condition as well. Since the accuracy of the normal prediction method is not enough, a new model is proposed in this paper that combines the sequence prediction model with the gated recurrent unit (GRU). According to the obtained results, the accuracy is improved through the proposed model. To verify the effectiveness of the model, simulations are performed on the steam turbine rotor unbalance fault data. The experimental results demonstrate that the proposed approach could be utilized for vibration severity prediction as well as state warning of the steam turbine.

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