New Sight of Deep Learning in Bioengineering: Updates and Future Directions (2nd Edition)

Dear Colleagues,

This Special Issue is the second edition of the previous release, ‘New Sight of Deep Learning in Bioengineering: Updates and Future Directions’ ( https://www.mdpi.com/journal/bioengineering/special_issues/B84S454C55).

It aims to explore the transformative impact of deep learning techniques within the field of bioengineering. It serves as a comprehensive collection of recent advancements, innovative applications, and future prospects for deep learning methodologies in various bioengineering domains.

It includes, but is not limited to, the following fields:

• Deep learning algorithms: This Special Issue highlights cutting-edge algorithms that have been developed or adapted for bioengineering applications, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
• Applications in biomedical imaging: A significant focus is on how deep learning is revolutionizing biomedical imaging techniques, enhancing image analysis, and improving diagnostic accuracy in areas such as radiology, pathology, and microscopy.
• Genomics and personalized medicine: Contributions will discuss the role of deep learning in genomics, including gene expression analysis, variant calling, and the development of personalized treatment plans through predictive modeling.
• Bioinformatics and data analysis: This Special Issue covers advancements in bioinformatics, emphasizing how deep learning approaches can process and analyze large biological datasets, leading to new insights and discoveries.
• Robotics and biomechanics: The integration of deep learning in robotics and biomechanics is examined, particularly how these technologies can improve prosthetics, rehabilitation devices, and human–robot interactions.

This Special Issue seeks to foster dialog among researchers, practitioners, and policymakers to advance the integration of deep learning in bioengineering, ultimately aiming to enhance patient care and biomedical research outcomes.

Dr. Tao Zhang
Guest Editor

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