Applications of AI in Biomedical Engineering for Healthy Ageing

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1184

Special Issue Editor


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Guest Editor
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: Artificial intelligence; cancer; deep learning; image segmentation and classification; machine learning; medical image analysis; personalized medicine; prognostication; radiomics; risk stratification
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Special Issue Information

Dear Colleagues,

The elevating accessibility of biomedical data from biobanks, medical imaging, electronic health records, and wearable biosensors have prepared the ground for multi-modal big data analyses and the development of sophisticated AI solutions that can capture the complexity of human diseases and hence facilitate the clinical decision-making processes toward personalized medicine. Aging is a universal, gradual, and irreversible process that every human encounters; examples of aging-associated diseases cover, but are not limited to, neurodegenerative diseases, cardiovascular diseases, metabolic diseases, musculoskeletal diseases, sarcopenia, cancers, and immune system diseases. With the rapidly growing aging population worldwide, promoting healthy aging is in pressing demand. To this end, there is a huge potential for us to leverage global solidarity to develop advanced AI solutions for healthy aging. This Special Issue on “Applications of AI in Biomedical Engineering for Healthy Ageing” aims to collect reviews, basic research, and clinical/technical studies that employ AI in biomedical engineering to resolve existing challenges towards healthy aging.

Dr. Sai Kit Lam
Guest Editor

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Keywords

  • artificial intelligence in healthy aging
  • aging-associated diseases
  • cancer
  • deep learning
  • image segmentation and classification
  • machine learning
  • medical image analysis
  • personalized medicine
  • prognostication
  • risk stratification

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Published Papers (1 paper)

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Research

21 pages, 3106 KiB  
Article
LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment
by Firas Al-Hindawi, Peter Serhan, Yonas E. Geda, Francis Tsow, Teresa Wu and Erica Forzani
Bioengineering 2025, 12(1), 86; https://doi.org/10.3390/bioengineering12010086 - 17 Jan 2025
Viewed by 772
Abstract
Alzheimer’s disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities. Early detection, particularly of mild cognitive impairment (MCI) as an indicator of potential AD onset, is crucial yet challenging, [...] Read more.
Alzheimer’s disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities. Early detection, particularly of mild cognitive impairment (MCI) as an indicator of potential AD onset, is crucial yet challenging, given the limitations of current diagnostic biomarkers and the need for non-invasive, accessible tools. This study aims to address these gaps by exploring driving performance as a novel, non-invasive biomarker for MCI detection. Using the LiveDrive AI system, equipped with multimodal sensing (MMS) technology and a driving performance assessment strategy, the proposed work analyzes the predictive capacity of driving patterns in indicating cognitive decline. Machine learning models, trained on an expert-annotated in-house dataset, were employed to detect MCI status from driving performance. Key findings demonstrate the feasibility of using nuanced driving features, such as velocity and acceleration during turning, as indicators of cognitive decline. This approach holds promise for integration into smartphone or car applications, enabling real-time, continuous cognitive health monitoring. The implications of this work suggest a transformative step towards scalable, real-world solutions for early AD diagnosis, with the potential to improve patient outcomes and disease management. Full article
(This article belongs to the Special Issue Applications of AI in Biomedical Engineering for Healthy Ageing)
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