ArticleBioengineering (Basel, Switzerland)2023
An Approach to Binary Classification of Alzheimer's Disease Using LSTM.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Computer-Aided Decision Support Systems of Alzheimer's Disease Diagnosis - A Systematic Review.Current medical imaging · 2025Pooled it
- Development of a machine learning-based prediction model for hypothyroidism-associated delirium in elderly hypothyroid patients in the intensive care unit.BMC geriatrics · 2026Article
- GPND-AI NULISA: A 15-Protein AI classifier for diagnosis and co-pathology profiling across neurodegenerative diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Predicting Alzheimer's Disease Progression from Sparse Multimodal Data by NeuralODE Models.bioRxiv : the preprint server for biology · 2025Article
- Protein-based Diagnosis and Analysis of Co-pathologies Across Neurodegenerative Diseases: Large-Scale AI-Boosted CSF and Plasma Classification.Research square · 2025Article
- Protein-based Diagnosis and Analysis of Co-pathologies Across Neurodegenerative Diseases: Large-Scale AI-Boosted CSF and Plasma Classification.medRxiv : the preprint server for health sciences · 2025Article
- Early detection of Alzheimer's disease using deep learning methods.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Data Processing and Machine Learning for Assistive and Rehabilitation Technologies.Bioengineering (Basel, Switzerland) · 2025Article
- Brain functional connectivity analysis of fMRI-based Alzheimer's disease data.Frontiers in medicine · 2025Article
- GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.