Revolutionizing Biomedical Imaging with AI and Open-Source Tools
Innovative open-source tools powered by AI are transforming biomedical imaging by making fluorescence lifetime imaging microscopy faster, more accessible, and more precise for live biological research.
Recent advancements in biomedical imaging have been significantly propelled by the integration of artificial intelligence (AI) and open-source software. A breakthrough development by Ph.D. student Sofia Kapsiani from the Molecular Neuroscience Group at the University of Cambridge has introduced innovative tools that make fluorescence lifetime imaging microscopy (FLIM) faster, more accessible, and cost-effective. These tools address longstanding challenges related to imaging speed, data analysis complexity, and high costs associated with traditional closed-source software.
Kapsiani has developed two complementary open-source solutions: FLIMPA, a versatile software for phasor analysis, and FLIMngo, a deep learning model designed to drastically reduce data acquisition time. Together, they enable researchers to perform high-throughput and real-time FLIM, which is crucial for live imaging in biological and medical research. The deployment of FLIMngo, trained on minimal photon data, allows for rapid in vivo image analysis in just seconds without compromising accuracy. This significantly minimizes light exposure and phototoxicity, essential factors when working with live specimens.
The impact of these tools extends beyond speed. FLIMngo opens new avenues for live diagnostics and dynamic studies, such as tracking disease-related proteins in living organisms like C. elegans. Its open-source nature ensures wide accessibility and adaptability across various imaging systems.
In addition, the earlier software, FLIMPA, provides a user-friendly platform for phasor analysis, supporting the interpretation of complex FLIM data. Its compatibility with multiple file formats and advanced visualization features have made it a valuable resource for both research and clinical applications, including the development of assays for cancer drug research.
These advancements mark a transformative step in biomedical imaging, empowering researchers and healthcare professionals to perform more efficient, detailed, and non-invasive studies. By removing barriers related to cost and technical complexity, AI and open-source solutions are set to accelerate discovery and improve patient care.
Source: https://medicalxpress.com/news/2025-07-ai-source-software-faster-easier.html
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