Ranking convolutional neural network for Alzheimer’s disease mini-mental state examination prediction at multiple time-points

Qiao, Hezhe; Chen, Lin; Zhu, Fan

doi:10.1016/j.cmpb.2021.106503

Cited by 23 publications

(11 citation statements)

References 38 publications

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“…On the one hand, due to the extension of time interval, the exposure of some variables changes, leading to the weakening of the influence of baseline variables on outcome. 33 Therefore, future studies can further incorporate temporal or dynamic information to improve prediction performance. On the other hand, some included variables like sociodemographic, lifestyle, and family socioeconomic factors, may require a long time to have influence on disability, 34 which may also explain the reason why some models in this study have better performance in 6-year prediction than that in 4-year prediction.…”

Section: Discussionmentioning

confidence: 99%

“…However, in 10‐year risk prediction, the performance of the model in LASSO set improved relatively less, and even decreased in some models. The possible explanation is that the collinearity and nonlinear problems involved in long‐term prediction are more complex, 33 and LASSO method based on the linear model is limited when dealing with these problems.…”

Section: Discussionmentioning

confidence: 99%

“…From the perspective of different time intervals, the performance of 4‐year and 6‐year risk prediction models was generally similar when predicting non‐disability, while performance of the 10‐year risk prediction was significantly reduced, but this trend was not obvious in predicting other disability categories. On the one hand, due to the extension of time interval, the exposure of some variables changes, leading to the weakening of the influence of baseline variables on outcome 33 . Therefore, future studies can further incorporate temporal or dynamic information to improve prediction performance.…”

Section: Discussionmentioning

confidence: 99%

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Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study

Wu,

Xiang,

Wang

et al. 2024

Psychogeriatrics

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BackgroundOlder adults with hypertension have a high risk of disability, while an accurate risk prediction model is still lacking. This study aimed to construct interpretable disability prediction models for older Chinese with hypertension based on multiple time intervals.MethodsData were collected from the Chinese Longitudinal Healthy Longevity and Happy Family Study for 2008–2018. A total of 1602, 1108, and 537 older adults were included for the periods of 2008–2012, 2008–2014, and 2008–2018, respectively. Disability was measured by basic activities of daily living. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning algorithms combined with LASSO set and full‐variable set were used to predict 4‐, 6‐, and 10‐year disability risk, respectively. Area under the receiver operating characteristic curve was used as the main metric for selection of the optimal model. SHapley Additive exPlanations (SHAP) was used to explore important predictors of the optimal model.ResultsRandom forest in full‐variable set and XGBoost in LASSO set were the optimal models for 4‐year prediction. Support vector machine was the optimal model for 6‐year prediction on both sets. For 10‐year prediction, deep neural network in full variable set and logistic regression in LASSO set were optimal models. Age ranked the most important predictor. Marital status, body mass index, score of Mini‐Mental State Examination, and psychological well‐being score were also important predictors.ConclusionsMachine learning shows promise in screening out older adults at high risk of disability. Disability prevention strategies should specifically focus on older patients with unfortunate marriage, high BMI, and poor cognitive and psychological conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study

Wu,

Xiang,

Wang

et al. 2024

Psychogeriatrics

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“…3D images of the whole brain also served as an input to 3D subject-level CNNs [36], [41]- [47]. Qiao et al used a 3DCNN with sharing weights to extract the features from MRI, followed by multiple sub-networks which transformed the MMSE regression models into a series of binary classification models [46]. All the methods discussed are summarized in Table 1.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests

et al. 2022

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Background. Psychophysiological and cognitive tests as well as other functional studies can detect pre-symptomatic stages of dementia. When assembled with structural data, cognitive tests diagnose NDs more reliably thus becoming a multimodal diagnostic tool. Objective. Our main goal is to improve screening for dementia by studying an association between the brain structure and its function. Hypothetically, the brain structure-function association has features specific for either diseaserelated cognitive deterioration or normal neurocognitive slowing while aging. Materials and methods. We studied a total number of 287 cognitively normal cases, 646 of mild cognitive impairment, and 369 of Alzheimer's disease. To work out a new marker of neurodegeneration, we created a convolutional neural network-based regression model and predicted the cognitive status of the cognitively preserved examinee from the brain MRI data. This was a model of normal aging. A big deviation from the model suggests a high risk of accelerated cognitive decline. Results. The deviation from the model of normal aging can accurately distinguish cognitively normal subjects from MCI patients (AUC = 0.9957). We also achieved creditable performance in the MCI-versus-AD classification (AUC = 0.9793). We identified a considerable difference in the MMSE test between A-positive and A-negative demented individuals according to ATNcriteria (6.27±1.82 vs 5.32±1.9; p < 0.05). Conclusion. The deviation from the model of normal aging can be potentially used as a marker of dementia and as a tool for differentiating Alzheimer's disease from non-Alzheimer's dementia. To find and justify a reliable threshold levels, further research is required.

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“…Qiao and others believe that MMSE prediction plays an important role in the early detection of Alzheimer's disease. They use the convolutional neural network to predict MMSE more effectively [ 1 ]. Solovyev and others believe that the decline of cognitive ability is related to Alzheimer's disease and capillary stagnation and the convolutional neural network should be used to detect capillaries, which achieves good results [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate 3D Reconstruction of White Matter Hyperintensities Based on Attention-Unet

Wang

Jian-jun

et al. 2022

Computational and Mathematical Methods in Medicine

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White matter hyperintensities (WMH), also known as white matter osteoporosis, have been clinically proven to be associated with cognitive decline, the risk of cerebral infarction, and dementia. The existing computer automatic measurement technology for the segmentation of patients’ WMH does not have a good visualization and quantitative analysis. In this work, the author proposed a new WMH quantitative analysis and 3D reconstruction method for 3D reconstruction of high signal in white matter. At first, the author using ResUnet achieves the high signal segmentation of white matter and adds the attention mechanism into ResUnet to achieve more accurate segmentation. Afterwards, this paper used surface rendering to reconstruct the accurate segmentation results in 3D. Data experiments are conducted on the dataset collected from Shandong Province Third Hospital. After training, the Attention-Unet proposed in this paper is superior to other segmentation models in the segmentation of high signal in white matter and Dice coefficient and MPA reached 92.52% and 92.43%, respectively, thus achieving accurate 3D reconstruction and providing a new idea for quantitative analysis and 3D reconstruction of WMH.

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Ranking convolutional neural network for Alzheimer’s disease mini-mental state examination prediction at multiple time-points

Cited by 23 publications

References 38 publications

Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study

Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study

Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests

Accurate 3D Reconstruction of White Matter Hyperintensities Based on Attention-Unet

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