Retinal image analytics detects white matter hyperintensities in healthy adults

Lau, Alexander; Mok, Vincent; Lee, Jack; Fan, Yuhua; Lam, Bonnie Yin Ka; Wong, Adrian; Kwok, Chloe; Lai, Manhong; Zee, Benny

doi:10.1002/acn3.688

Cited by 33 publications

(25 citation statements)

References 23 publications

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“…From the initial search, 162 articles were retrieved with 38 of them being analyzed 19,21–57 . All included studies were prospective; five had an intraindividual study design with two 21,24,31,40 or three study groups 19 that were examined separately. In those cases, study groups were considered to be independent, resulting in a total of 44 study groups from the 38 articles.…”

Section: Resultsmentioning

confidence: 99%

White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta‐Analysis

Melazzini

Vitali

Olivieri

et al. 2020

Magnetic Resonance Imaging

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Background Although white matter hyperintensities (WMH) volumetric assessment is now customary in research studies, inconsistent WMH measures among homogenous populations may prevent the clinical usability of this biomarker. Purpose To determine whether a point estimate and reference standard for WMH volume in the healthy aging population could be determined. Study Type Systematic review and meta‐analysis. Population In all, 9716 adult subjects from 38 studies reporting WMH volume were retrieved following a systematic search on EMBASE. Field Strength/Sequence 1.0T, 1.5T, or 3.0T/fluid‐attenuated inversion recovery (FLAIR) and/or proton density/T2‐weighted fast spin echo sequences or gradient echo T1‐weighted sequences. Assessment After a literature search, sample size, demographics, magnetic field strength, MRI sequences, level of automation in WMH assessment, study population, and WMH volume were extracted. Statistical Tests The pooled WMH volume with 95% confidence interval (CI) was calculated using the random‐effect model. The I2 statistic was calculated as a measure of heterogeneity across studies. Meta‐regression analysis of WMH volume on age was performed. Results Of the 38 studies analyzed, 17 reported WMH volume as the mean and standard deviation (SD) and were included in the meta‐analysis. Mean and SD of age was 66.11 ± 10.92 years (percentage of men 50.45% ± 21.48%). Heterogeneity was very high (I2 = 99%). The pooled WMH volume was 4.70 cm3 (95% CI: 3.88–5.53 cm3). At meta‐regression analysis, WMH volume was positively associated with subjects' age (β = 0.358 cm3 per year, P < 0.05, R2 = 0.27). Data Conclusion The lack of standardization in the definition of WMH together with the high technical variability in assessment may explain a large component of the observed heterogeneity. Currently, volumes of WMH in healthy subjects are not comparable between studies and an estimate and reference interval could not be determined. Level of Evidence 1 Technical Efficacy Stage 1

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Section: Resultsmentioning

confidence: 99%

White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta‐Analysis

Melazzini

Vitali

Olivieri

et al. 2020

Magnetic Resonance Imaging

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“…Finally, they concluded 59% hypertension, 26% hyperlipidemia and severe WMH of 36%. The correlation coefficient was of 0.897 [12].…”

Section: Literature Surveymentioning

confidence: 91%

Machine Learning Techniques to Identify Dementia

Mathkunti¹,

Rangaswamy²

2020

SN COMPUT. SCI.

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The challenge to clinician is to diagnosis the complex disease such as Alzheimer, disease, frontotemporal dementia and Parkinson. The diseases are bit complex to diagnose in terms of symptoms as they overlap in many aspects. So, it is necessary to investigate the process of diagnostic with more accuracy with different parameters of the disease. The paper presents the implementation of machine learning algorithms to get more accuracy to identify the disease Parkinson. The data set referred is from Online-based machine learning repository also recognized as UCI. Support vector machine, K-nearest neighbor and linear discriminant analysis algorithms are used to calculate accuracy, recall and confusion matrix. The outcome of this implementation has given the accuracy of 100% for SVM and KNN algorithms and 80% for LDA. The size of the data set is 196 entries, and a number of parameters for each row are 22. The main parameters which are significant for the disease are 5, and implementation has done with 2 different versions. In first version, all the parameters are included, and in the second version, only 5 main parameters included. In both cases, training set is 70% and test set is 30%. SVM shows more accuracy in both versions of implementation. This helps to find the diagnosis and also to judge the significance of parameters with real data set.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…[ 30 ] We recently showed that a retinal imaging analysis powered with machine learning technology achieved good performance in detecting CSVD in the community. [ 31 ]…”

Section: In Vivo Detectionmentioning

confidence: 99%

Pandemic of the aging society — sporadic cerebral small vessel disease

Lau

et al. 2021

Chinese Medical Journal

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Age-related sporadic cerebral small vessel disease (CSVD) has gained increasing attention over the past decades because of its increasing prevalence associated with an aging population. The widespread application of and advances in brain magnetic resonance imaging in recent decades have significantly increased researchers’ understanding in the in vivo evolution of CSVD, its impact upon the brain, its risk factors, and the mechanisms that explain the various clinical manifestation associated with sporadic CSVD. In this review, we aimed to provide an update on the pathophysiology, risk factors, biomarkers, and the determinants and spectrum of the clinical manifestation of sporadic CSVD.

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Retinal image analytics detects white matter hyperintensities in healthy adults

Cited by 33 publications

References 23 publications

White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta‐Analysis

White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta‐Analysis

Machine Learning Techniques to Identify Dementia

Pandemic of the aging society — sporadic cerebral small vessel disease

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