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Diagnostics
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Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition
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Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 553

Special Issue Editor

Prof. Dr. Mohammad Shahadat Hossain

E-Mail Website
Guest Editor
1. Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh
2. Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Lulea, Sweden
Interests: artificial intelligence; expert systems; health informatics; barin informatics; Alzheimer’s disease; machine learning; explaianble AI; soft computing; pervasive computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Alzheimer’s disease is a neurodegenerative disorder that affects memory and other cognitive functions. It is also the fifth-leading cause of death in adults aged 65 and above. Therefore, the early detection and diagnosis of Alzheimer's disease are crucial in developing effective treatments and improving quality of life for patients. The scope of artificial intelligence (AI) in diagnosing Alzheimer's disease is vast and impressive, thanks to advancements in a range of areas, including learning, reasoning, and explainability. AI has demonstrated the ability to predict the likelihood of developing Alzheimer's disease. AI systems show promise in detecting the early signs of this disease by analyzing patterns and anomalies in large data sets. Furthermore, AI can be used to track the progression of the disease using the patient's cognitive function over time. Our aim for this Special Issue is to share novel research on AI systems that have been developed, implemented, and evaluated to support the prediction, early detection, and progression of Alzheimer’s disease over time, in accordance with the policy of the journal Diagnostics.

Prof. Dr. Mohammad Shahadat Hossain
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Alzheimer’s disease
  • artificial intelligence
  • machine learning
  • explainable AI
  • expert systems
  • computer vision
  • deep learning
  • diagnosis
  • cognition
  • brain informatics
  • neurodegenerative disorder

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Published Papers (1 paper)

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Research

17 pages, 1666 KB  
Article
The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer’s Disease Diagnosis
by Ernesto Barceló, Duban Romero, Ricardo Allegri, Eliana Meza, María I. Mosquera-Heredia, Oscar M. Vidal, Carlos Silvera-Redondo, Mauricio Arcos-Burgos, Pilar Garavito-Galofre and Jorge I. Vélez
Diagnostics 2025, 15(23), 3045; https://doi.org/10.3390/diagnostics15233045 (registering DOI) - 28 Nov 2025
Abstract
Background/Objectives: Alzheimer’s disease (AD) accounts for ~70% of global dementia cases, with projections estimating 139 million affected individuals by 2050. This increasing burden highlights the urgent need for accessible, cost-effective diagnostic tools, particularly in low- and middle-income countries (LMICs). Traditional neuropsychological assessments, [...] Read more.
Background/Objectives: Alzheimer’s disease (AD) accounts for ~70% of global dementia cases, with projections estimating 139 million affected individuals by 2050. This increasing burden highlights the urgent need for accessible, cost-effective diagnostic tools, particularly in low- and middle-income countries (LMICs). Traditional neuropsychological assessments, while effective, are resource-intensive and time-consuming. Methods: A total of 760 older adults (394 [51.8%] with AD) were recruited and neuropsychologically evaluated at the Instituto Colombiano de Neuropedagogía (ICN) in collaboration with Universidad del Norte (UN), Barranquilla. Machine learning (ML) algorithms were trained on a screening protocol incorporating demographic data and neuropsychological measures assessing memory, language, executive function, and praxis. Model performance was determined using 10-fold cross-validation. Variable importance analyses identified key predictors to develop optimized, abbreviated ML-based protocols. Metrics of compactness, cohesion, and separation further quantified diagnostic differentiation performance. Results: The eXtreme Gradient Boosting (xgbTree) algorithm achieved the highest diagnostic accuracy (91%) with the full protocol. Five ML-optimized screening protocols were also developed. The most efficient, the ICN-UN battery (including MMSE, Rey–Osterrieth Complex Figure recall, Rey Auditory Verbal Learning, Lawton & Brody Scale, and FAST), maintained strong diagnostic performance while reducing screening time from over four hours to under 25 min. Conclusions: The ML-optimized ICN-UN protocol offers a rapid, accurate, and scalable AD screening solution for LMICs. While promising for clinical adoption and earlier detection, further validation in diverse populations is recommended. Full article
(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition)
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