New Insights of Machine Learning, Artificial Intelligence and Digital Health in Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 1328

Special Issue Editors


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Guest Editor
College of Computing and Informatics, Providence University, Taichung City 43301, Taiwan
Interests: IoT; data analytics; telemedicine
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Interests: smart grid, smart health, smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities are rapidly evolving into systems that support the integration of various applications and services. Advances in electronics technology and data-driven solutions will enhance the quality of life for residents and visitors in smart cities.  One of the key pillars of smart cities’ development is the design and implementation of Machine Learning (ML) and Artificial Intelligence (AI) in various sectors to optimize and streamline processes.  In these applications, biomedical electronics plays a crucial role in ensuring the health and well-being of urban populations. This Special Issue aims to explore the intersection of ML, AI, and biomedical applications within the context of smart cities’ development.

Dr. Bernard Fong
Prof. Dr. Loi Lei Lai
Guest Editors

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Keywords

  • assistive care
  • embedded electronics
  • health monitors
  • telemedicine
  • wireless sensing

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Published Papers (1 paper)

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Research

17 pages, 56471 KiB  
Article
Attention-Enhanced Guided Multimodal and Semi-Supervised Networks for Visual Acuity (VA) Prediction after Anti-VEGF Therapy
by Yizhen Wang , Yaqi Wang, Xianwen Liu, Weiwei Cui, Peng Jin, Yuxia Cheng and Gangyong Jia
Electronics 2024, 13(18), 3701; https://doi.org/10.3390/electronics13183701 - 18 Sep 2024
Viewed by 950
Abstract
The development of telemedicine technology has provided new avenues for the diagnosis and treatment of patients with DME, especially after anti-vascular endothelial growth factor (VEGF) therapy, and accurate prediction of patients’ visual acuity (VA) is important for optimizing follow-up treatment plans. However, current [...] Read more.
The development of telemedicine technology has provided new avenues for the diagnosis and treatment of patients with DME, especially after anti-vascular endothelial growth factor (VEGF) therapy, and accurate prediction of patients’ visual acuity (VA) is important for optimizing follow-up treatment plans. However, current automated prediction methods often require human intervention and have poor interpretability, making it difficult to be widely applied in telemedicine scenarios. Therefore, an efficient, automated prediction model with good interpretability is urgently needed to improve the treatment outcomes of DME patients in telemedicine settings. In this study, we propose a multimodal algorithm based on a semi-supervised learning framework, which aims to combine optical coherence tomography (OCT) images and clinical data to automatically predict the VA values of patients after anti-VEGF treatment. Our approach first performs retinal segmentation of OCT images via a semi-supervised learning framework, which in turn extracts key biomarkers such as central retinal thickness (CST). Subsequently, these features are combined with the patient’s clinical data and fed into a multimodal learning algorithm for VA prediction. Our model performed well in the Asia Pacific Tele-Ophthalmology Society (APTOS) Big Data Competition, earning fifth place in the overall score and third place in VA prediction accuracy. Retinal segmentation achieved an accuracy of 99.03 ± 0.19% on the HZO dataset. This multimodal algorithmic framework is important in the context of telemedicine, especially for the treatment of DME patients. Full article
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