Classification of Alzheimer’s Disease from 18F-FDG and 11C-PiB PET Imaging Biomarkers Using Support Vector Machine

Yang, Bohan; Chen, Jyh Cheng; Chou, Wen Hsiang; Huang, Wen Sheng; Fuh, Jong Ling; Liu, Ru‐Shi; Wu, Chongsheng

doi:10.1007/s40846-020-00548-1

Cited by 9 publications

(8 citation statements)

References 48 publications

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“…[107][108][109][110][111][112][113][114][115][116][117][118] The same approach applied to amyloid PET also demonstrated accuracies of >85% for predicting MCI conversion and diagnosing AD. 115,117,[119][120][121] Non-SVM approaches, such as convolutional neural networks and deep learning, on FDG PET and amyloid PET showed variable performance in predicting a final diagnosis of AD, cognitive decline, or MCI conversion, 46,[122][123][124][125][126][127][128][129][130][131][132] with accuracy between 75% and 100%. Model accuracy in multicenter studies (>70% accuracy) was lower than that of those relying on local datasets (>78% accuracy).…”

Section: Pet/spect Imagingmentioning

confidence: 83%

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

Borchert

Azevedo

Badhwar

et al. 2023

Alzheimer's & Dementia

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IntroductionArtificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.MethodsWe systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.ResultsA total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.DiscussionThe literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.Highlights There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

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Section: Pet/spect Imagingmentioning

confidence: 83%

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

Borchert

Azevedo

Badhwar

et al. 2023

Alzheimer's & Dementia

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“…Also, a developed algorithm called "Support Vector Machine Leave-One-Out Recursive Feature Elimination and Cross-Validation" (SVM-RFE-LOO) for early detection of AD was proposed 15 . Moreover, several researchers also used SVM concerning the detection of AD [16][17][18][19][20][21][22][23][24] . For instance 25 , used Artificial Neural Network (ANN) with MRI images to perform prediction for the transition from mild cognitive impairment (MCI) to AD with an accuracy of 89.5%.…”

Section: Very Severe Cogniɵve Decline (Very Severe Demenɵa)mentioning

confidence: 99%

A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm

Elgammal

Zahran

Abdelsalam

2022

Sci Rep

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Alzheimer's Disease (AD) is considered one of the most diseases that much prevalent among elderly people all over the world. AD is an incurable neurodegenerative disease affecting cognitive functions and were characterized by progressive and collective functions deteriorating. Remarkably, early detection of AD is essential for the development of new and invented treatment strategies. As Dementia causes irreversible damage to the brain neurons and leads to changes in its structure that can be described adequately within the framework of multifractals. Hence, the present work focus on developing a promising and efficient computing technique to pre-process and classify the AD disease especially in the early stages using multifractal geometry to extract the most changeable features due to AD. Then, A machine learning classification algorithm (K-Nearest Neighbor) has been implemented in order to classify and detect the main four early stages of AD. Two datasets have been used to ensure the validation of the proposed methodology. The proposed technique has achieved 99.4% accuracy and 100% sensitivity. The comparative results show that the proposed classification technique outperforms is recent techniques in terms of performance measures.

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“…FDG PET, amyloid PET and/or MRI) were more accurate in terms of diagnosis of both MCI and AD (Shao et al 2020;Zu et al 2016;Zhan et al 2015;Ortiz et al 2015;L. Liu et al 2015;Yang et al 2020;Xu et al 2015;Ben Bouallegue et al 2018). An additional approach used PET and structural MRI data in combination with other markers (i.e.…”

Section: Pet/spect Imagingmentioning

confidence: 99%

“…Liu et al 2014). The same approach applied to amyloid PET also demonstrated accuracies of >85% for predicting MCI conversion and diagnosing AD(El-Gamal et al 2018;Yang et al 2020;Zhan et al 2015;Xu et al 2015;Nozadi and Kadoury 2018). Non-SVM approaches, such as convolutional neural networks and deep learning, on FDG PET and amyloid PET showed variable performance in predicting a final diagnosis of AD, cognitive decline or MCI conversion(Choi and Jin 2018;Ding et al 2019;Huang et al 2019;Son et al…”

mentioning

confidence: 90%

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Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

Borchert

Azevedo

Badhwar

et al. 2021

Preprint

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Introduction Recent developments in artificial intelligence (AI) and neuroimaging offer new opportunities for improving diagnosis and prognosis of dementia. To synthesise the available literature, we performed a systematic review. Methods We systematically reviewed primary research publications up to January 2021, using AI for neuroimaging to predict diagnosis and/or prognosis in cognitive neurodegenerative diseases. After initial screening, data from each study was extracted, including: demographic information, AI methods, neuroimaging features, and results. Results We found 2709 reports, with 252 eligible papers remaining following screening. Most studies relied on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (n=178) with no other individual dataset used more than 5 times. Algorithmic classifiers, such as support vector machine (SVM), were the most commonly used AI method (47%) followed by discriminative (32%) and generative (11%) classifiers. Structural MRI was used in 71% of studies with a wide range of accuracies for the diagnosis of neurodegenerative diseases and predicting prognosis. Lower accuracy was found in studies using a multi-class classifier or an external cohort as the validation group. There was improvement in accuracy when neuroimaging modalities were combined, e.g. PET and structural MRI. Only 17 papers studied non-Alzheimers disease dementias. Conclusion The use of AI with neuroimaging for diagnosis and prognosis in dementia is a rapidly emerging field. We make a number of recommendations addressing the definition of key clinical questions, heterogeneity of AI methods, and the availability of appropriate and representative data. We anticipate that addressing these issues will enable the field to move towards meaningful clinical translation.

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Classification of Alzheimer’s Disease from 18F-FDG and 11C-PiB PET Imaging Biomarkers Using Support Vector Machine

Cited by 9 publications

References 48 publications

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm

Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

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