Deep Learning for Predicting Cognitive Gap as a Reliable Biomarker of Dementia

Habuza, Tetiana; Zaki, Nazar; Statsenko, Yauhen; Alnajjar, Fady; Elyassami, Sanaa

doi:10.1101/2021.01.24.21249625

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…A multicenter worldwide study reported an increased risk of intraparenchymal hemorrhage (IPH) within a few hours of exposure to a very low ambient temperature (AT) (20). To advance the forecast the disease course, various risk factors should be analyzed in combination with medical findings using multimodal machine learning algorithms (21,22,23,24,25,26,27,28,29,30).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Predicting Incidence, Severity, and Early Outcomes of Hemorrhagic Stroke from Weather Parameters and Individual Risk Factors

Statsenko,

Fursa,

Laver

et al. 2024

Preprint

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Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Models Predicting Incidence, Severity, and Early Outcomes of Hemorrhagic Stroke from Weather Parameters and Individual Risk Factors

Statsenko,

Fursa,

Laver

et al. 2024

Preprint

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“…The approach enables us to quantify the hypermetabolic state in acute respiratory failure which is a contributing factor to the extraordinary ventilatory and oxygenation demands in the infected patients. Finally, applying artificial intelligence to the consecutive studies of the laboratory findings and medical imaging dwells promises to combine the evidence coming from the diagnostic modalities based on conceptually different methodologies [34]- [36]. This will help to cover all the known pathophysiologic mechanisms of atypical pneumonia caused by SARS-CoV-2.…”

Section: Introductionmentioning

confidence: 99%

Reliability of Machine Learning in Eliminating Data Redundancy of Radiomics and Reflecting Pathophysiology in COVID-19 Pneumonia: Impact of CT Reconstruction Kernels on Accuracy

et al. 2022

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Background: Radiomical data are redundant but they might serve as a tool for lung quantitative assessment reflecting disease severity and actual physiological status of COVID-19 patients. Objective: Test the effectiveness of machine learning in eliminating data redundancy of radiomics and reflecting pathophysiologic changes in patients with COVID-19 pneumonia. Methods: We analyzed 605 cases admitted to Al Ain Hospital from 24 February to 1 July, 2020. They met the following inclusion criteria: age 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We categorized cases into 4 classes: mild 25% of pulmonary parenchymal involvement, moderate -25-50%, severe -50-75%, and critical -over 75%. We used CT scans to build regression models predicting the oxygenation level, respiratory and cardiovascular functioning. Results: Radiomical findings are a reliable source of information to assess the functional status of patients with COVID-19. Machine learning models can predict the oxygenation level, respiratory and cardiovascular functioning from a set of demographics and radiomics data regardless of the settings of reconstruction kernels. The regression models can be used for scoring lung impairment and comparing disease severity in follow up studies. The most accurate prediction we achieved was 6.454±3.715% of mean absolute error/range for all the features and 7.069±4.17% for radiomics. Conclusion: The models may contribute to the proper risk evaluation and disease management especially when the oxygen therapy impacts the actual values of the functional findings. Still, the structural assessment of an acute lung injury reflects the severity of the disease.

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“…Neuroscience lacks a reliable means for screening patients in the early stages of dementia (Habuza et al, 2021a , d ). Furthermore, the accuracy of clinical diagnostics of the disease is also limited (see Table 1 ).…”

Section: Introductionmentioning

confidence: 99%

Proportional Changes in Cognitive Subdomains During Normal Brain Aging

Statsenko

Habuza

Gorkom

et al. 2021

Front. Aging Neurosci.

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Background: Neuroscience lacks a reliable method of screening the early stages of dementia.Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains.Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes.Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age.Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

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Deep Learning for Predicting Cognitive Gap as a Reliable Biomarker of Dementia

Cited by 5 publications

References 27 publications

Machine Learning Models Predicting Incidence, Severity, and Early Outcomes of Hemorrhagic Stroke from Weather Parameters and Individual Risk Factors

Machine Learning Models Predicting Incidence, Severity, and Early Outcomes of Hemorrhagic Stroke from Weather Parameters and Individual Risk Factors

Reliability of Machine Learning in Eliminating Data Redundancy of Radiomics and Reflecting Pathophysiology in COVID-19 Pneumonia: Impact of CT Reconstruction Kernels on Accuracy

Proportional Changes in Cognitive Subdomains During Normal Brain Aging

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