A novel conversion prediction method of MCI to AD based on longitudinal dynamic morphological features using ADNI structural MRIs

Guo, Man; Li, Yongchao; Zheng, Weihao; Huang, Keman; Zhou, Li; Hu, Xiping; Yao, Zhijun; Hu, Bin

doi:10.1007/s00415-020-09890-5

Cited by 16 publications

(13 citation statements)

References 79 publications

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“…The classification accuracy achieved by this method was 76.9%. Guo et al [ 25 ] proposed a robust, novel method using three-year follow-up data to mine a dynamic MRI morphological feature and based MCI-to-AD conversion prediction on it. The classification accuracy obtained by Guo et al was 84.62%.…”

Section: Introductionmentioning

confidence: 99%

Early MCI‐to‐AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

Minhas

Khanum

Alvi

et al. 2021

Computational Intelligence and Neuroscience

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In Alzheimer’s disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer’s disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction.

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Section: Introductionmentioning

confidence: 99%

Early MCI‐to‐AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

Minhas

Khanum

Alvi

et al. 2021

Computational Intelligence and Neuroscience

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“…Early mild cognitive impairment is the early stage of AD (MCI Screen, 2021;Guo et al, 2020). In this stage, the small changes in the cognitive normal are considered EMCI.…”

Section: • Early Mild Cognitive Impairment (Emci)mentioning

confidence: 99%

“…Late mild cognitive impairment is the next stage of EMCI (MCI Screen, 2021;Guo et al, 2020). Most patients in this stage will progress to AD.…”

Section: • Late Mild Cognitive Impairment (Lmci)mentioning

confidence: 99%

Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data

Rajayyan¹,

Mustafa²

2023

AIP

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Alzheimer's disease is a brain memory loss disease. Usually, it will affect persons over 60 years of age. The literature has revealed that it is quite difficult to diagnose the disease, so researchers are trying to predict the disease in the early stage. This paper proposes a framework to classify Alzheimer's patients and to predict the best classification algorithm. The Bestfirst and CfssubsetEval methods are used for feature selection. A multi-class classification is done using machine learning algorithms, namely the naïve Bayes algorithm, the logistic algorithm, the SMO/SMV algorithm and the random forest algorithm. The classification accuracy of the algorithms is 67.68%, 84.58%, 87.42%, and 88.90% respectively. The validation applied is 10-fold cross-validation. Then, a confusion matrix is generated and class-wise performance is analysed to find the best algorithm. The ADNI database is used for the implementation process. To compare the performance of the proposed model, the OASIS dataset is applied to the model with the same algorithms and the accuracy of the algorithms is 98%, 99%, 99% and 100% respectively. Also, the time for the model construction is compared for both datasets. The proposed work is compared with existing studies to check the efficiency of the proposed model.

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“…Guo et al [16] forecast the conversion from MCI to AD efficiently. Researchers proposed a cerebral similarity network containing more progression information.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Alzheimer's Disease Progression by Combining Multiple Measures

Zawawi¹,

Saber²,

Hashem³

et al. 2021

NLP Techniques and Applications

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Alzheimer's disease (AD) is a degenerative brain ailment that affects millions worldwide. It is the most common form of dementia. Patients with an early diagnosis of Alzheimer's disease have a strong chance of preventing additional brain damage by halting nerve cell death. At the same time, it begins to progress several years before any symptoms appear. The variety of data is the biggest problem encountered during diagnosis. Neurological examination, brain imaging, and often asked questions from his connected closed relatives are the three forms of data that a neurologist or geriatrics employs to diagnose patients. One of the biggest questions which need answering is the choice of a convenient feature. The main objective of this paper is to help neurologists or geriatricians diagnose patient conditions. It proposes a new hybrid model for features extracted from medical data. It discusses AD's early diagnosis and progression for all features considered in the diagnosis and their complex interactions. It proves to have the best accuracy when compared with the state-ofthe-art algorithm. Also, it proves to be more accurate against some recent research ideas. It got 95% in all cases, considering this work focused more on increasing the number of instances in comparison.

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A novel conversion prediction method of MCI to AD based on longitudinal dynamic morphological features using ADNI structural MRIs

Cited by 16 publications

References 79 publications

Early MCI‐to‐AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

Early MCI‐to‐AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data

Predicting Alzheimer's Disease Progression by Combining Multiple Measures

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