ArticleAging medicine (Milton (N.S.W))2022
Development of an artificial intelligence-based diagnostic model for Alzheimer's disease.
Article in Aging medicine (Milton (N.S.W)), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A machine learning AI model for predicting condylar erosion in patients with temporomandibular joint disorders and analyzing factors contributing to prediction.Oral radiology · 2026Article
- A Hybrid Explainable AI Framework (HXAI) for Accurate and Interpretable Diagnosis of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Article
- Statistical Parametric Mapping and Voxel-Based Specific Regional Analysis System for Alzheimer's Disease (VSRAD): Principles and Clinical Applications.Brain sciences · 2025Review
- A Feature-Augmented Explainable Artificial Intelligence Model for Diagnosing Alzheimer's Disease from Multimodal Clinical and Neuroimaging Data.Diagnostics (Basel, Switzerland) · 2025Article
- A Meta-Learning-Based Ensemble Model for Explainable Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands.Imaging science in dentistry · 2025Article
- Daily activities and suspected dementia among community-dwelling older adults: a cross-sectional study.BMC geriatrics · 2024Article
- Investigating the Potential Impact of Air Pollution on Alzheimer's Disease and the Utility of Multidimensional Imaging for Early Detection.ACS omega · 2024Review
- Development of an artificial intelligence-based diagnostic model for Alzheimer's disease.Aging medicine (Milton (N.S.W)) · 2022Article
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6 authors.
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Abstract
Introduction: The diagnosis of Alzheimer's disease (AD) is sometimes difficult for nonspecialists, resulting in misdiagnosis. A missed diagnosis can lead to improper management and poor outcomes. Moreover, nonspecialists lack a simple diagnostic model with high accuracy for AD diagnosis. Methods: Randomly assigned data, including training data, of 6000 patients and test data of 1932 from 7932 patients who visited our memory clinic between 2009 and 2021 were introduced into the artificial intelligence (AI)-based AD diagnostic model, which we had developed. Results: The AI-based AD diagnostic model used age, sex, Hasegawa's Dementia Scale-Revised, the Mini-Mental State Examination, the educational level, and the voxel-based specific regional analysis system for Alzheimer's disease (VSRAD) score. It had a sensitivity, specificity, and c-static value of 0.954, 0.453, and 0.819, respectively. The other AI-based model that did not use the VSRAD had a sensitivity, specificity, and c-static value of 0.940, 0.504, and 0.817, respectively. Discussion: We created an AD diagnostic model with high sensitivity for AD diagnosis using only data acquired in daily clinical practice. By using these AI-based models, nonspecialists could reduce missed diagnoses and contribute to the appropriate use of medical resources.
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