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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63752

AI TOOL FOR EARLY STAGE DEMENTIA DETECTION

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63752

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PKN

POOJA K N

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING TECHNOLOGY

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NKMK

NAVEEN KUMAR M K

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING TECHNOLOGY

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Abstract

-Dementia is a long-term neurological condition that gradually affects memory, thinking ability, and the performance of everyday activities. Disorders such as Alzheimer’s disease fall under this category and typically worsen over time. Identifying dementia at an early stage is extremely important, as medical and during the mild cognitive impairment (MCI) phase, before irreversible damage occurs in the brain. The project titled “AI Tool for Early-Stage Dementia Detection” focuses on using AI and ML methods to recognize early indicators of cognitive decline. The system examines multiple forms of data, including speech characteristics, facial behavior, handwriting patterns, behavioral responses, and available clinical or biological data. By analyzing information obtained from routine medical records and digital interactions, the NLP techniques to identify variations in speech clarity, vocabulary usage, and fluency. In parallel, study subtitle facial movements and handwriting behavior, while predictive models estimate the likelihood of progression from MCI to dementia. The proposed solution is designed to be non- invasive, affordable, and scalable, making it suitable for use in primary healthcare centers and community-based environments rather than being limited to specialized memory clinics. Previous studies on similar AI-driven systems have reported accuracy levels close to 90% in identifying early cognitive decline.

KEYWORDS

live detecting Artificial intelligence AI Driven system have reported accura Machine learning Neural Network detective levels close to in idebtifing early cognitive Analytics services

Cite This Article

POOJA K N, NAVEEN KUMAR M K, "AI TOOL FOR EARLY STAGE DEMENTIA DETECTION," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026.

Full Article/Conclusion

In conclusion, the development of an AI-based for early-stage dementia detection represents a significant advancement in modern healthcare. Dementia, especially Alzheimer's disease, progresses gradually and is often diagnosed only after noticeable cognitive decline. Early improve the well-being of individuals affected by dementia. AI-based tools are highly effective in the early detection of dementia and Alzheimer’s disease by analyzing data such as speech, medical records, and brain images. These technologies improve diagnostic accuracy and help identify symptoms at an early stage. Early detection allows timely medical intervention and better patient care. AI methods are non-invasive, fast, and support healthcare professionals in decision-making. Overall, AI plays a crucial role in improving outcomes and quality of life for patients. This project is 30% Accurate

References

[1]              X. Zhang, Y. Li, and H. Wang, “AI- based early detection of dementia using multimodal data,” Journal of Medical Systems, 2021. [2]              J. Chen, Y. Liu, and P. Zhou, “Machine learning approaches for mild cognitive impairment and early dementia detection,” IEEE Access, vol. 8, pp. 135724–13573X, 2020. (Note: Please verify the last page number — it appears truncated in your original entry.) [3]              J. C. de la Torre, “Alzheimer’s disease: Early detection and preventive strategies,” Frontiers in Aging Neuroscience, pp. 201– 215, 2018. [4]              F. Rudzicz et al., “Speech-based detection of cognitive impairment,” ACM Transactions on Accessible Computing, 2014. (Volume, issue, and page numbers are missing — add them if available.) [5]              OpenCV, “Open-Source Computer Vision Library,” 2025. [Online]. Available: https://opencv.org/ [6]              TensorFlow, “An end-to-end open- source machine learning platform,” 2025.

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Publication Date

5/26/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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