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Peer Review of “Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development”

Peer Review of “Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development”

Limited evaluation: The evaluation is limited to the Open Access Series of Imaging Studies (OASIS) dataset, which may not be representative of the diverse AD population. The authors should evaluate their system on larger and more diverse datasets, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, to demonstrate its generalizability. Insufficient implementation details: The implementation details of the SVMs and ANNs are insufficient.

Anonymous, Anonymous

JMIRx Med 2025;6:e73768

Authors’ Response to Peer Reviews of “Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development”

Authors’ Response to Peer Reviews of “Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development”

Limited evaluation: The evaluation is limited to the Open Access Series of Imaging Studies (OASIS) dataset, which may not be representative of the diverse AD population. The authors should evaluate their system on larger and more diverse datasets, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, to demonstrate its generalizability. Response: Done, experiments were achieved by applying the ADNI database. Please see the ADNI Data Set subsection (page 3) for more details on this basis.

Lilia Lazli

JMIRx Med 2025;6:e72821

Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development

Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development

In this context, we have experimented the performance of the proposed CAD system on the OASIS [26] and ADNI [27] datasets. The OASIS dataset [26] was prepared by Dr Randy Buckner from the Howard Hughes Medical Institute at Harvard University, the Neuroinformatics Research Group at Washington University School of Medicine, and the Biomedical Informatics Research Network. OASIS is a longitudinal multimodal neuroimaging, clinical, cognitive, and biomarker dataset for normal aging and AD.

Lilia Lazli

JMIRx Med 2025;6:e60866