Convolutional Neural Network Based Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Technique using Hippocampus Extracted from MRI

Mukhtar, G.; Farhan, Saima

doi:10.4316/aece.2020.02013

Cited by 14 publications

(6 citation statements)

References 40 publications

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“…Figure 3 shows the architecture of a typical CNN. Mukhtar et al 31 used CNN to predict MCIc or MCInc and achieved a prediction accuracy of 94% based on the left hippocampal image of sMRI. Pan et al 32 developed an ensemble classifier combining CNN and ensemble learning, and they used a set of lateral, coronal, or sagittal MRI data.…”

Section: Advanced Methods For Predicting Admentioning

confidence: 99%

Prediction of Alzheimer’s Disease Progression Based on Magnetic Resonance Imaging

Zhou

Song

Han

et al. 2021

ACS Chem. Neurosci.

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The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.

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Section: Advanced Methods For Predicting Admentioning

confidence: 99%

Prediction of Alzheimer’s Disease Progression Based on Magnetic Resonance Imaging

Zhou

Song

Han

et al. 2021

ACS Chem. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Mukhtar and Farhan [ 9 ] proposed a CNN-based deep learning approach for predicting the conversion from MCI to AD using various features, including MRI, genetics, and neuropsychological assessment scores. The CNN model achieved an accuracy of 94 % for predicting MCI to AD conversion.…”

Section: Related Workmentioning

confidence: 99%

“…AD For fMRI Dataset: 99.95 %. For PET Dataset: 73.46 % [ 9 ] MRI CNN MCIc vs. MCInc 94 % [ 10 ] MRI 3D-CNN MCI vs. CN vs. AD 96.66 % [ 11 ] MRI MLP MCI vs. CN vs.…”

Section: Related Workmentioning

confidence: 99%

“…While Sarraf and Tofighi [ 6 ] and Janghel and Rathore [ 8 ] utilized Convolutional Neural Networks (CNNs) for AD classification, our study distinguishes itself by integrating the hippocampus approach, a region crucially impacted in the early stages of AD, enhancing the interpretability of our model's predictions. Additionally, unlike Mukhtar and Farhan [ 9 ], who focused on predicting the conversion from Mild Cognitive Impairment (MCI) to AD using diverse features, our model contributes by providing accurate classification into three distinct classes: cognitively normal (CN), MCI, and AD. Furthermore, in contrast to the patch-based method implemented by Raju et al [ 10 ], our study employs advanced image preprocessing techniques, ensuring a streamlined and efficient process for early AD detection.…”

Section: Related Workmentioning

confidence: 99%

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AI-based tool for early detection of Alzheimer's disease

Ul Rehman,

Tarek,

Magdy

et al. 2024

Heliyon

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“…Furthermore, follow-up readings of all biomarkers are subjected to a stage transition. Auto regression Y Y 84.29 [14] PCA and posterior probability Y 84 [15] LR, extrapolation, and SVM Y 77.87 [18] LR, NB, EN, KNN, KNN, GTB, and SVM Y Y 84 [20] Deep sequential NN Y 83 [21] Rad-sig and SVM Y Y Y 80 [22] DNN Y Y Y 63.30 [23] CNN Y Y Y 94 [24] Regression and SVM Y 71.16 [25] WT and SVM Y 84.13 [ To choose the most suitable future value from the two possible values recorded, proximity measures are used. Let i be the sample having two known consecutive annual values whose next value, v 3 is to be forecasted.…”

Section: Marker Selection and Normalization Sperling Et Al's Researchmentioning

confidence: 99%

A Deep Longitudinal Model for Mild Cognitive Impairment to Alzheimer’s Disease Conversion Prediction in Low-Income Countries

Akhtar

Minhas

Sabahat

et al. 2022

Applied Computational Intelligence and Soft Computing

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Alzheimer’s disease (AD) is a progressive and fatal disease, due to the nonavailability of any permanent cure. Some treatments are under experimentation that can slow down and possibly pause the progression of the disease only if the disease is diagnosed earlier. The onset of AD can only be detected at the mild cognitive impairment (MCI) stage in which slight memory loss is observed but daily routine functions are intact. A small fraction of the patient progresses from MCI to AD. In this research, we have designed a cascaded deep neural network model to identify those MCI subjects who will progress to AD in the following year. The analysis and experimentation have been performed using twenty longitudinal neuropsychological measures (NMs) provided by Alzheimer’s Disease Neuroimaging Initiative (ADNI). After normalization and ranking of longitudinal data, the deep neural network regression model is trained and tuned to forecast the next in-sequence biomarker value using two previous follow-up readings for each marker. Then, the three time-domain window samples are fed into another deep neural network classifier model for the classification of MCI progressor (MCIp) and MCI stables (MCIs). Our model presented regression forecasting MAE of 0.13 and classification accuracy of 86.9% with AUC of 92.1% (Sensitivity: 67.7%, specificity: 92.3%) over 5-fold cross-validation. We conclude that time-domain measures of NM alone can deliver comparable MCI to AD conversion prediction performance without leveraging more expensive and invasive counterparts such as MR imaging, PET scans, and CSF measures. Middle and low-income countries will benefit from such cheap and effective solutions greatly.

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Convolutional Neural Network Based Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Technique using Hippocampus Extracted from MRI

Cited by 14 publications

References 40 publications

Prediction of Alzheimer’s Disease Progression Based on Magnetic Resonance Imaging

Prediction of Alzheimer’s Disease Progression Based on Magnetic Resonance Imaging

AI-based tool for early detection of Alzheimer's disease

A Deep Longitudinal Model for Mild Cognitive Impairment to Alzheimer’s Disease Conversion Prediction in Low-Income Countries

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