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Electronics
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New Insights of Machine Learning, Artificial Intelligence and Digital Health...
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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 1358

Special Issue Editors

Dr. Bernard Fong

E-Mail Website
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
Prof. Dr. Loi Lei Lai

E-Mail Website
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

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Electronics 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 2400 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

  • 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 973
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
(This article belongs to the Special Issue New Insights of Machine Learning, Artificial Intelligence and Digital Health in Smart Cities)
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