AI-Driven Imaging and Analysis for Biomedical Applications

Dear Colleagues,

Recent advances in AI-driven medical imaging and analysis are transforming computational bioengineering research and clinical practice, enabling quantitative insights into healthy and diseased tissues. From static imaging- to dynamic video-based analyses, these techniques provide tools for understanding complex physiological processes such as vascular pathologies, tumor hypoxia, neurodegeneration, and fetal development. Imagine modalities, such as ultrasound, MRI, CT, and PET, are at the forefront of this transformation, supported by cutting-edge AI-driven computational methods.

Recent breakthroughs in AI, particularly machine learning, are driving innovations in medical image and video analysis. These include addressing challenges such as motion artifacts, operator dependency, and multimodal integration. We welcome contributions on cutting-edge AI-driven methods and novel hardware/software integrations that enhance medical imaging for diagnosis, monitoring, and therapy, including the following:

• AI-driven methods for real-time interpretation of medical images and videos, including deep learning models for rapid disease detection and automated clinical decision support;
• Advances in multimodal imaging integration, such as AI-enhanced fusion of ultrasound, MRI, Doppler, and NIRS, for improved diagnostic accuracy;
• Bioengineered solutions for next-generation patient-centric imaging systems, including low-cost portable devices, automated scanning protocols, and ergonomic human–machine interfaces designed for accessibility and ease of use;
• Computational methods for dynamic medical video analysis, incorporating real-time biomechanical tracking, spatiotemporal feature extraction, and AI-enhanced motion artifact correction for improved diagnostic precision;
• Techniques addressing the challenges of video-based medical analysis, including noise reduction, contrast enhancement, and real-time segmentation;
• Explainable AI approaches for medical imaging, ensuring transparency, interpretability, and trust in AI-driven diagnostic systems;
• Generative AI for medical imaging and analysis, leveraging synthetic data generation, AI-driven image enhancement, and virtual contrast agents to improve image quality and model robustness.

This Special Issue highlights interdisciplinary research at the intersection of technological innovation, clinical application, and computational analysis, demonstrating how AI-driven imaging advances biomedical diagnosis, monitoring, and treatment.

Dr. Ulas Bagci
Dr. Netzahualcoyotl Hernandez-Cruz
Dr. Zeyu Fu
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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.

• biomedical image and video analysis
• ultrasound-based image and video analysis
• machine learning
• multimodal imaging
• AI-assisted diagnostics
• real-time analysis
• generative AI
• explainable AI