Alzheimer’s from Biology to Tests and AI Support
Research Article
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Alzheimer’s from Biology to Tests and AI Support

Haoyu Chen 1*
1 Santa Monica College, Santa Monica, California, United States
*Corresponding author: A1670194659@outlook.com
Published on 26 November 2025
Volume Cover
ACE Vol.210
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-567-7
ISBN (Online): 978-1-80590-568-4
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Abstract

Alzheimer’s disease (AD) is a health problem that can cause memory loss, trouble with thinking, and a decline in daily life. This health problem will bring a lot of stress to both patients and families. For a long time, diagnosis relied mainly on symptoms, but these symptoms appear late and overlap with other health problems. In recent years, a “biology first” approach has been developed, using biomarkers such as amyloid, tau, and neurodegeneration to give clearer and earlier answers. At the same time, Artificial intelligence (AI) becomes more important, for example, AI can analyze scans, speech, and clinical data. But there are still some problems, for example it is hard to make sure that everyone uses it fairly. This review brings together the current view of AD, the role of clinical and biological checks, and the growing support of AI. Its main goal is to help readers understand how diagnosis is moving from late symptom-based methods to earlier and more reliable systems.

Keywords:

AI in AD care, Alzheimer’s disease (AD), MRI, PET, speech data

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Chen,H. (2025). Alzheimer’s from Biology to Tests and AI Support. Applied and Computational Engineering,210,36-40.

References

[1]. Jack, C. R., Jr., et al. (2018). NIA–AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia. Defined in vivo by biomarkers.

[2]. Jack, C. R., Jr., et al. (2024). Revised criteria for diagnosis and staging of Alzheimer’s disease. Alzheimer’s & Dementia. Present objective criteria for diagnosis and staging AD.

[3]. ADNI official site. (n.d.). A longitudinal, multi-center, observational study…validate biomarkers for Alzheimer’s disease clinical trials.

[4]. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research.

[5]. Nasreddine, Z. S., et al. (2005). The Montreal Cognitive Assessment (MoCA): A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society.

[6]. Morris, J. C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology.

[7]. Basaia, S., et al. (2019). Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clinical. A single MRI deep model for diagnosis and conversion prediction.

[8]. Luz, S., et al. (2020). Alzheimer’s Dementia Recognition through Spontaneous Speech (ADReSS Challenge). INTERSPEECH. A shared task based on spontaneous speech.

[9]. Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021). Multimodal deep learning models for early detection of Alzheimer’s disease stage. Scientific Reports. Integrating multi-modality data outperforms single modality models.

[10]. Qiu, S., et al. (2022). Multimodal deep learning for Alzheimer’s disease dementia assessment. Nature Communications. Multiple diagnostic steps in successive fashion.

[11]. Castellano, G., et al. (2024). Automated detection of Alzheimer’s disease: A multi-modal approach with 3D MRI and amyloid PET (OASIS-3). Scientific Reports. Volumetric representations and integrating enhances performance.

[12]. Battineni, G., Chintalapudi, N., & Amenta, F. (2024). Machine learning driven by MRI for the classification of Alzheimer disease progression: Systematic review and meta-analysis. JMIR Aging.

[13]. Vermeulen, R. J., et al. (2025). Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review. Alzheimer’s & Dementia. Limited generalizability and high risk of bias.

References

[1]. Jack, C. R., Jr., et al. (2018). NIA–AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia. Defined in vivo by biomarkers.

[2]. Jack, C. R., Jr., et al. (2024). Revised criteria for diagnosis and staging of Alzheimer’s disease. Alzheimer’s & Dementia. Present objective criteria for diagnosis and staging AD.

[3]. ADNI official site. (n.d.). A longitudinal, multi-center, observational study…validate biomarkers for Alzheimer’s disease clinical trials.

[4]. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research.

[5]. Nasreddine, Z. S., et al. (2005). The Montreal Cognitive Assessment (MoCA): A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society.

[6]. Morris, J. C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology.

[7]. Basaia, S., et al. (2019). Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clinical. A single MRI deep model for diagnosis and conversion prediction.

[8]. Luz, S., et al. (2020). Alzheimer’s Dementia Recognition through Spontaneous Speech (ADReSS Challenge). INTERSPEECH. A shared task based on spontaneous speech.

[9]. Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021). Multimodal deep learning models for early detection of Alzheimer’s disease stage. Scientific Reports. Integrating multi-modality data outperforms single modality models.

[10]. Qiu, S., et al. (2022). Multimodal deep learning for Alzheimer’s disease dementia assessment. Nature Communications. Multiple diagnostic steps in successive fashion.

[11]. Castellano, G., et al. (2024). Automated detection of Alzheimer’s disease: A multi-modal approach with 3D MRI and amyloid PET (OASIS-3). Scientific Reports. Volumetric representations and integrating enhances performance.

[12]. Battineni, G., Chintalapudi, N., & Amenta, F. (2024). Machine learning driven by MRI for the classification of Alzheimer disease progression: Systematic review and meta-analysis. JMIR Aging.

[13]. Vermeulen, R. J., et al. (2025). Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review. Alzheimer’s & Dementia. Limited generalizability and high risk of bias.

Cite this article

Chen,H. (2025). Alzheimer’s from Biology to Tests and AI Support. Applied and Computational Engineering,210,36-40.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-567-7(Print) / 978-1-80590-568-4(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.210
ISSN: 2755-2721(Print) / 2755-273X(Online)

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