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Advanced Image and Video Processing Technology for Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 477

Special Issue Editors


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Guest Editor
Department of Family, Community and Health System Science, College of Nursing, University of Florida, Gainesville, FL, USA
Interests: artificial intelligence (AI) and machine learning (ML); electronic health records (EHRs) and clinical decision support (CDS); health information technology and mHealth; usability; HIV/AIDS; symptom care; substance use
Special Issues, Collections and Topics in MDPI journals
Division of Biomedical Informatics, University of Florida, Gainesville, FL, USA
Interests: computational modeling; RNA viruses modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of technology has led to an exponential increase in the volume of medical image and video data being generated. These vast datasets hold immense potential to transform healthcare, enabling advancements in diagnostics, treatment planning, remote monitoring, and surgical guidance. Effectively analyzing and utilizing this information requires innovative image and video processing techniques.

This Special Issue aims to highlight the latest research, methodologies, and applications in image and video processing that enhance healthcare delivery, medical diagnostics, and patient care. It provides a platform for researchers, engineers, and healthcare professionals to share cutting-edge innovations that advance medical imaging and video analysis, ultimately improving patient outcomes and advancing healthcare efficiency.

We invite original research and review articles covering a wide range of topics, including, but not limited to, the following:

  • Medical Image Processing: AI-driven analysis of MRI, CT, ultrasound, X-ray, and histopathological images.
  • Video Processing in Healthcare: Real-time surgical video analysis, endoscopic video enhancement, and remote patient monitoring.
  • Machine Learning and Deep Learning in Medical Imaging: AI-based techniques for image segmentation, classification, and anomaly detection.
  • Augmented Reality (AR) and Virtual Reality (VR): Applications in medical training, surgery planning, and rehabilitation.
  • Computer-Aided Diagnosis (CAD): Automated systems for disease detection and prognosis.
  • Telehealth and Remote Sensing: Image and video processing for telehealth and wearable health monitoring devices.
  • Mobile Health (mHealth) Applications in Medical Imaging: AI-powered diagnostic tools, real-time imaging analysis, and mobile-based solutions.
  • Data Security and Privacy in Medical Imaging: Secure transmission, storage, and processing of medical images and videos.
  • Integration with Electronic Health Records (EHRs): Enhancing patient data management through advanced imaging techniques.
  • Multimodal Foundation Models for Image and Video Understanding: Improving the model’s capability in video understanding through multi-modalities and foundation models.

Dr. Hwayoung Cho
Dr. Rui Yin
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. Applied Sciences 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

  • medical image processing
  • video processing in healthcare
  • AI and machine learning in medical imaging
  • multimodal foundation model
  • augmented reality
  • virtual reality
  • computer-aided diagnosis
  • telehealth
  • mobile health applications

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Published Papers (1 paper)

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Research

15 pages, 1369 KB  
Article
MTLNFM: A Multi-Task Framework Using Neural Factorization Machines to Predict Patient Clinical Outcomes
by Rui Yin, Jiaxin Li, Qiang Yang, Xiangyu Chen, Xiang Zhang, Mingquan Lin, Jiang Bian and Ashwin Subramaniam
Appl. Sci. 2025, 15(15), 8733; https://doi.org/10.3390/app15158733 - 7 Aug 2025
Viewed by 250
Abstract
Accurately predicting patient clinical outcomes is a complex task that requires integrating diverse factors, including individual characteristics, treatment histories, and environmental influences. This challenge is further exacerbated by missing data and inconsistent data quality, which often hinder the effectiveness of traditional single-task learning [...] Read more.
Accurately predicting patient clinical outcomes is a complex task that requires integrating diverse factors, including individual characteristics, treatment histories, and environmental influences. This challenge is further exacerbated by missing data and inconsistent data quality, which often hinder the effectiveness of traditional single-task learning (STL) models. Multi-Task Learning (MTL) has emerged as a promising paradigm to address these limitations by jointly modeling related prediction tasks and leveraging shared information. In this study, we proposed MTLNFM, a multi-task learning framework built upon Neural Factorization Machines, to jointly predict patient clinical outcomes on a cohort of 2001 ICU patients. We designed a preprocessing strategy in the framework that transforms missing values into informative representations, mitigating the impact of sparsity and noise in clinical data. We leveraged the shared representation layers, composed of a factorization machine and dense neural layers that can capture high-order feature interactions and facilitate knowledge sharing across tasks for the prediction. We conducted extensive comparative experiments, demonstrating that MTLNFM outperforms STL baselines across all three tasks (i.e., frailty status, hospital length of stay and mortality prediction), achieving AUROC scores of 0.7514, 0.6722, and 0.7754, respectively. A detailed case analysis further revealed that MTLNFM effectively integrates both task-specific and shared representations, resulting in more robust and realistic predictions aligned with actual patient outcome distributions. Overall, our findings suggest that MTLNFM is a promising and practical solution for clinical outcome prediction, particularly in settings with limited or incomplete data, and can support more informed clinical decision-making and resource planning. Full article
(This article belongs to the Special Issue Advanced Image and Video Processing Technology for Healthcare)
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