AI-Empowered Biophotonics

AI-Empowered biophotonics is a burgeoning field that bridges artificial intelligence (AI) with cutting-edge optical imaging techniques to boost device design and diagnostic accuracy. Despite notable progress, contemporary biophotonic methods like photoacoustic imaging (PA) encounter issues such as spectral coloring effects and challenges in chromophore quantification and real-time data interpretation. Machine learning (ML) advancements are setting new benchmarks by enabling more efficient real-time analysis, superior image reconstruction, and more accurate quantitative imaging. This research topic aims to delve into how AI can redefine biomedical imaging technologies and improve both tissue characterization and clinical applications.

This Research Topic aims to investigate how AI-Empowered biophotonics can revolutionize medical imaging through enhanced accuracy, efficiency, and real-time data interpretation. We look to address several key goals: developing AI-driven approaches to aid biomedical device innovation, enhancing image reconstruction with validated AI models, and improving the quantitative capability of PA and other imaging techniques for better clinical decisions. We will also explore AI-simulated training and real-world data calibration for robust model creation and investigate AI's potential in automating imaging processes to boost diagnostic precision and reduce manual efforts.

To gather further insights in AI integration within biophotonic technologies, we welcome articles addressing, but not limited to, the following themes:

o AI-enhanced photoacoustic imaging and its applications.
o AI innovations in myocardial imaging, including automated segmentation and adaptive systems.
o Real-time AI applications in biophotonics for personalized medicine and diagnostics.
o Innovative methods combining AI with optical imaging for improved non-invasive diagnostics. We invite submissions of original research and review articles that explore theoretical advances, experimental validations, and translational applications of AI in biophotonics. Contributions will significantly influence the ongoing development and clinical integration of AI-driven imaging solutions.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: Artificial intelligence, Biophotonics, Machine learning in medical and biological imaging, Quantitative tissue characterization, Personalized medicine, Image Processing

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.