Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

Ahmed, Samsuddin; Kim, Byeong C.; Lee, Kun Ho; Jung, Ho Yub

doi:10.1371/journal.pone.0242712

Cited by 33 publications

(22 citation statements)

References 57 publications

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“…Data were obtained from the Gwangju Alzheimer’s Disease and Related Dementias Cohort database in Gwangju, South Korea [ 21 , 22 , 23 ]. All research data and samples were collected between August 2015 and October 2017 in Gwangju and Jeollanam-do in Korea.…”

Section: Methodsmentioning

confidence: 99%

Diagnostic Blood Biomarkers in Alzheimer’s Disease

et al. 2022

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Potential biomarkers for Alzheimer’s disease (AD) include amyloid β1–42 (Aβ1–42), t-Tau, p-Tau181, neurofilament light chain (NFL), and neuroimaging biomarkers. Their combined use is useful for diagnosing and monitoring the progress of AD. Therefore, further development of a combination of these biomarkers is essential. We investigated whether plasma NFL/Aβ1–42 can serve as a plasma-based primary screening biomarker reflecting brain neurodegeneration and amyloid pathology in AD for monitoring disease progression and early diagnosis. We measured the NFL and Aβ1–42 concentrations in the CSF and plasma samples and performed correlation analysis to evaluate the utility of these biomarkers in the early diagnosis and monitoring of AD spectrum disease progression. Pearson’s correlation analysis was used to analyse the associations between the fluid biomarkers and neuroimaging data. The study included 136 participants, classified into five groups: 28 cognitively normal individuals, 23 patients with preclinical AD, 22 amyloid-negative patients with amnestic mild cognitive impairment, 32 patients with prodromal AD, and 31 patients with AD dementia. With disease progression, the NFL concentrations increased and Aβ1–42 concentrations decreased. The plasma and CSF NFL/Aβ1–42 were strongly correlated (r = 0.558). Plasma NFL/Aβ1–42 was strongly correlated with hippocampal volume/intracranial volume (r = 0.409). In early AD, plasma NFL/Aβ1–42 was associated with higher diagnostic accuracy than the individual biomarkers. Moreover, in preclinical AD, plasma NFL/Aβ1–42 changed more rapidly than the CSF t-Tau or p-Tau181 concentrations. Our findings highlight the utility of plasma NFL/Aβ1–42 as a non-invasive plasma-based biomarker for early diagnosis and monitoring of AD spectrum disease progression.

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Section: Methodsmentioning

confidence: 99%

Diagnostic Blood Biomarkers in Alzheimer’s Disease

et al. 2022

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“…As a consequence, Ahmed et al (2020) proposed a CNN model for earlier diagnosis and classification of Alzheimer disease. Within the dataset consists of 6628 MRI images, the proposed model achieved 99% accuracy [ 107 ]. Nawaz et al (2020) proposed deep feature-based models and achieved an accuracy of 99.12% [ 108 ].…”

Section: Machine Learning Techniques For Different Disease Diagnosismentioning

confidence: 99%

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

2022

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Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.

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“…Data were obtained from the database of Gwangju Alzheimer's Disease and Related Dementias Cohort database in Gwangju, South Korea [21][22][23]. All research data and samples were collected between August 2015 and October 2017 in Gwangju and Jeollanam-do of Korea.…”

Section: Study Participantsmentioning

confidence: 99%

Diagnostic Fluid Biomarkers in Alzheimer’s Disease: Blood and Cerebrospinal Fluid

Park

Gunasekaran

Cho

et al. 2021

Preprint

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Background: Potential biomarkers for Alzheimer’s disease (AD) include amyloid β 1-42 (Aβ 1-42 ), t-Tau, p-Tau 181 , neurofilament light chain (NFL), and neuroimaging, but the feasibility of using these for the diagnosis and monitoring of AD has not been reported. Therefore, further development of these biomarkers is essential. Methods: We measured NFL and Aβ 1-42 concentrations in CSF and plasma samples from 136 participants and performed correlation analysis to evaluate the utility of these biomarkers for early diagnosis and monitoring of disease progression in AD spectrum. Results: With disease progression, concentrations of NFL increased, and those of Aβ 1-42 were decreases. The plasma and CSF values of NFL/Aβ 1-42 were strongly correlated ( r = 0.558). In addition, the plasma value of NFL/Aβ 1-42 was strong correlated with hippocampal volume/ICV ( r = 0.409). In the early stage of AD, the plasma_NFL/Aβ 1-42 was associated with higher diagnostic accuracy than were the individual biomarkers. Moreover, in preclinical AD, plasma_NFL/Aβ 1-42 changed more rapidly than did either the t-Tau or the p-Tau 181 values measured in the CSF. Conclusions: Taken together, our findings highlight the utility of plasma_NFL/Aβ 1-42 as a biomarker for early diagnosis and monitoring of disease progression in AD spectrum.

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Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

Cited by 33 publications

References 57 publications

Diagnostic Blood Biomarkers in Alzheimer’s Disease

Diagnostic Blood Biomarkers in Alzheimer’s Disease

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Diagnostic Fluid Biomarkers in Alzheimer’s Disease: Blood and Cerebrospinal Fluid

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