Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging

TL;DR

This paper discusses how artificial intelligence can be integrated into live-cell fluorescence microscopy to reduce phototoxicity, enabling more accurate and reliable biological observations with less damage to living samples.

Contribution

It highlights the importance of establishing constraints for AI-driven imaging to minimize photodamage and promotes data-driven approaches for safer, high-quality live-cell imaging.

Findings

AI can help maintain photodamage at safe levels during imaging

Data-driven methods improve live-cell imaging accuracy

Potential for AI to enable less invasive biological studies

Abstract

Fluorescence microscopy, widely used in the study of living cells, tissues, and organisms, often faces the challenge of photodamage. This is primarily caused by the interaction between light and biochemical components during the imaging process, leading to compromised accuracy and reliability of biological results. Methods necessitating extended high-intensity illumination, such as super-resolution microscopy or thick sample imaging, are particularly susceptible to this issue. As part of the solution to these problems, advanced imaging approaches involving artificial intelligence (AI) have been developed. Here we underscore the necessity of establishing constraints to maintain light-induced damage at levels that permit cells to sustain their live behaviour. From this perspective, data-driven live-cell imaging bears significant potential in aiding the development of AI-enhanced photodamage-aware microscopy. These technologies could streamline precise observations of natural biological dynamics while minimising phototoxicity risks.