Henriques

# Harnessing Artificial Intelligence
To Reduce Phototoxicity in Live Imaging

Estibaliz Gómez-de-Mariscal  Optical cell biology group, Instituto Gulbenkian
de Ciência, Oerias, Portugal  Equally contributed authors  Mario Del Rosario
Optical cell biology group, Instituto Gulbenkian de Ciência, Oerias, Portugal
Equally contributed authors  Joanna W Pylvänäinen  Faculty of Science and
Engineering, Cell Biology, Åbo Akademi University, Turku, Finland  Guillaume
Jacquemet  Faculty of Science and Engineering, Cell Biology, Åbo Akademi
University, Turku, Finland  Turku Bioimaging, University of Turku and Åbo
Akademi University, Turku, Finland  InFLAMES Research Flagship Center, Åbo
Akademi University  Ricardo Henriques  Optical cell biology group, Instituto
Gulbenkian de Ciência, Oerias, Portugal

###### 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.

###### keywords:

photodamage | phototoxicity | live-microscopy | artificial intelligence | deep learning | data driven microscopy | fluorescence microscopy | live-cell super-resolution microscopy

rjhenriques and igc.gulbenkian.pt

## Introduction

The ability to comprehend biological dynamics is inherently linked to the
capacity for non-invasive observation. Fluorescence microscopy has been
instrumental in facilitating these analyses across a range of scales, with
molecular specificity (1, 2, 3). Over the past two decades, technological
advancements such as light sheet microscopy (4, 5, 6, 7), structured
illumination microscopy (SIM) (8, 9), and single molecule localisation
microscopy (SMLM) (10, 11, 12), have revolutionised fluorescence light
microscopy, enabling us to characterise biological events from molecular
interactions up to larger living organisms.

Figure 1: Graphical abstract. The delicate balance between sample health and
the information obtained by imaging requires a compromise between both
elements. Deep learning augmented microscopy aims to reduce this compromise,
striving to obtain equal information from our sample with less impact on its
health. SNR: Signal-to-noise ratio

A key challenge associated with advanced microscopy analysis is its likely
need to increase fluorescence excitation light levels, which results in
phototoxicity or photodamage. These terms refer to the detrimental impacts of
light, especially when employing photosensitising agents or high-intensity
illumination (13, 14, 15). Despite certain distinctions like photodamage
occurring also in non-living materials, both terms are here used
interchangeably, for simplicity. Sample illumination may also result in
photobleaching, a process characterised by an irreversible loss of a
fluorescent signal attributed to the destruction of the fluorophore. This is
one manifestation of light damage, among other possible effects. Phototoxicity
severely influences the experimental outcomes by altering biological processes
under observation, skewing findings and impeding consistency. Therefore, it’s
crucial during live-cell microscopy to carefully consider these factors to
prolong imaging durations and achieve dependable research outcomes.

The biological validity of live-cell imaging experiments requires a precise
balance between the data quality and the specimen’s health, as depicted in
Fig. $1$. Major advancements have been made in both hardware and software
technologies aiming to reduce sample light damage. Hardware innovations such
as Lattice Light Sheet Microscopy (LLSM) (16) and Airyscan Microscopy (17) are
notable examples. Additionally, computational advancements like Fluctuation-
based Super Resolution Microscopy offer promising solutions to these issues
(18, 19). A recent study has shown that a two-colour illumination scheme
combining near-infrared illumination with fluorescence excitation presents an
interesting capacity to limit the phototoxicity caused by light-induced
interactions with fluorescent proteins (20). These technological breakthroughs
have the potential to optimise observation accuracy while mitigating
photodamage.

In parallel, artificial intelligence (AI), specifically deep learning, can
significantly improve imaging information in low illumination scenarios by
considerably enhancing image quality and quantification (21, 22, 23). This has
inspired the search for integrated solutions by the microscopy community (24,
25, 26). The fusion of advanced optical hardware with computational models and
intelligent analytics heralds new breakthroughs in overcoming sample damage
induced by traditional live fluorescence microscopy methodologies, marking the
advent of AI-enhanced intelligent microscopy.

Figure 2: Phototoxicity and sample health during live-cell acquisition. a)
Examples of photodamage across widefield and confocal microscopy. Cellular
rounding up and membrane blebbing are observed in widefield; actin
depolymerisation and cellular collapse are seen in confocal. yellow: actin;
orange: tubulin; cyan: nuclei. b) The effect of different light microscopy
modalities according to the type of illumination on the imaged sample. Some
modalities, such as Lightsheet microscopy, decrease the amount of light on the
sample to increase cell survival. Other modalities sacrifice sample health to
gain spatial resolution, such as STED c) AI-enabled low phototoxicity live-
cell microscopy by reducing the amount of light needed on the sample to obtain
the same information. A few examples are shown, such as image restoration and
contrast enhancement applicable to different microscopy modalities,
acquisition sampling reduction by tuning time acquisitions, and fluorescent
channel tuning. The scale bar represents $25\mu m$.

## Understanding phototoxicity

Cellular environments undergo continuous adaptation to maintain homeostasis,
with oxygen playing a key role in multiple chemical processes (27). Reactive
Oxygen Species (ROS), resulting from oxidative processes involving oxygen
radicals (Table 1), are integral to cell growth, differentiation, and other
functions. However, uncontrolled ROS processes lead to oxidative stress and
cellular damage, resulting in autophagy, inflammation, and potentially cell
death (27). For these reasons, a balance between these states is critical for
cellular physiology.

Table 1: Oxygen radicals and examples of biomolecules affected by oxidative processes. The most common oxygen radicals present during ROS processes (27) and biomolecules affected across different levels, interfering with molecular pathways and cellular signalling (28, 29, 30, 31, 32).  Oxygen Radicals | Biomolecules Affected
---|---
Hydrogen Peroxide | Flavins
Singlet molecular oxygen | Porphyrins
Nitric Oxide | NAD(P)H
Superoxide anion radicals | Tyrosine
Hydroxyl radical | Catecholamines
Hydroxide ion | Cysteinyl thiols

Light irradiation to excite fluorescence, in addition to inducing oxidative
stress by producing high amounts of ROS, triggers multiscale alterations
across biological samples that alter the homeostasis of oxidative processes.
Moreover, higher doses of light irradiation increase this oxidative stress,
further aggravating cellular homeostasis. Photochemical processes at the
molecular level lead to intracellular component damage or toxic compound
formation (33, 34). The literature extensively documents the impacts of
fluorescence excitation light, especially UV light on DNA, such as DNA double-
stranded breaks (35), thymidine dimerisations (35), UV-induced apoptosis (36),
and tumour factor activation (36). These effects are even routinely employed
in research settings under controlled conditions (37, 38, 39, 40, 41).
Moreover, molecules naturally present in cells (Table $1$) undergo degradation
via exposure to light-induced oxidative stress, canonically produced during
the fluorescence excitation process. This degradation culminates in the
generation of reactive oxygen compounds that directly impair cell health by
oxidising DNA, proteins, unsaturated fatty acids, and enzyme cofactors (15,
42). Excessive ROS shifts the homeostasis of the cell cytoplasmic environment’
leading to organelle damage like mitochondrial fragmentation due to
compromised membrane potential and integrity, among others (43, 35, 44, 45,
13).

While helpful and versatile, fluorescence microscopy illumination entails an
additional source of ROS formation (46). When excited, fluorophores undergo
autocatalysis through dioxygen, releasing hydrogen peroxide and consequently
degrading - a process commonly known as photobleaching (47). Photobleaching
produces ROS similar to other biomolecules, intensifying phototoxic effects
(46). However, despite the interrelation between photobleaching and
phototoxicity (20), these phenomena exhibit distinct features and can occur
independently. Namely, identical oxygen radical compounds originate from
photobleaching and direct fluorescence excitation light interactions with
other cellular components (46, 47). Suggesting that a reduction of
photobleaching does not necessarily imply a decrease in phototoxicity, and the
other way around. The intricate mechanisms underlying these phenomena have
been thoroughly researched, especially concerning various fluorophores’
photosensitising effects in live microscopy. Given the common necessity for
oxygen in cell cultures, ROS formation during live-cell imaging is an
unavoidable aspect of the imaging process.

At lower thresholds, the impact of light exposure may be minimal and
reversible, allowing the cell to return to physiological conditions. This cell
recovery will depend on several factors, namely how resilient the sample is
and the degree of phototoxicity incurred by the experiment (13). In severe
cases, the behaviour of cells will be permanently altered (15). Moreover,
further alterations normally accumulate within a cell’s homeostatic
environment until reaching an irreversible point causing pronounced impairment
of cell health that leads to unwanted cell behaviour (cellular collapse) or
death (apoptosis) (Figure $2$a) (42, 15, 34, 35).

Although scientists have identified several indicators of photodamage, the
correlation between fluorescent excitation light dose and light damage is not
yet fully understood due to variability in experimental conditions. These
variations include culture conditions, illumination modes (Figure $2$b) and
biological sample variability, hindering the understanding between fluorescent
excitation light dose and light damage (15, 14). Despite challenges associated
with varying specimen resistances to light damage, establishing universal
quantitative benchmarks across diverse samples could harmonise these effects
and enhance reproducibility in the imaging context.

## Phototoxicity quantification

The literature describes several phototoxicity hallmarks with numerous known
markers available to identify and characterise sample damage (33). However,
using these markers presents additional challenges in live-cell experiments.
Firstly, incorporating phototoxicity markers necessitates extra planning and
may require allocating fluorescent channels usually reserved for observing
conditions of interest (_e.g._ , markers for DNA oxidative damage). Not only
does this limit the number of fluorescent channels available for the
experiment, but when paired with live cell experiments could enhance ROS
formation, thereby escalating the phototoxicity risk on the specimen.
Secondly, despite well-documented phototoxicity markers, quantification-based
screenings are less commonly employed (34). The absence of live-cell imaging
universal metrics relatable to light exposure and consequent damage across
various biological systems hampers experimental reproducibility, undermines
results’ robustness, and limits the obtainable biological readouts.
Furthermore, without these universal metrics, it’s challenging to fully
leverage the capacity to image biological systems. This results in the
incorrect assessment of experimental conditions to achieve maximum spatial and
temporal resolution while preserving cell viability.

Researchers primarily rely on observation and experience to assess cell health
and viability during photodamage evaluation (Figure $2$a) (13, 34, 15). While
there are some attempts to provide quantifiable metrics for improving sample
viability (14, 42, 13), they often simplify the impact of fluorescence
excitation light to a binary classification of viable/healthy or non-
viable/dead. These approaches may overlook subtle effects that could disturb a
specimen’s normal physiology. A gradient model that considers the accumulation
of such discrete minor effects would more accurately depict the spectrum of
effects documented in the existing literature. Namely, these approaches could
be even more flexible by considering both cell health decline and recovery.

Yet an important aspect to consider is the collective physiological and
behavioural outcome, rather than individual effects. In controlled
experimental conditions, specimens typically exhibit consistent behaviours
such as cell division, motility, and membrane dynamics. By modelling and
quantifying these behaviours, deviations can be correlated to the negative
effects of fluorescent light exposure on the sample.

Phototoxicity is a known issue in live-cell imaging, and a plethora of
strategies exist to mitigate its effects, ranging from reducing light
irradiation by either reducing the acquisition points or the light dose (7) to
using more sensitive light detectors (17). Other strategies focus on
controlling oxidative stress effects in biological samples by reducing its
effects by supplementing antioxidants (48, 49), or increasing the resistance
of the sample itself (50). Although some strategies are simpler to integrate
than others, they require using specific and costly equipment or altering the
conditions of the specimen.

As advanced image analysis tools become increasingly available, a future
strategy for fully reproducible live-cell imaging procedures could incorporate
specific phototoxicity reporters within automated image acquisition and
analysis workflows. This approach, paired with standardised experimental
guidelines for identifying and quantifying phototoxic events, represents a
promising solution. As imaging technologies evolve, adopting such methods
should be prioritised by scientists aiming to create robust imaging strategies
for visualising biological phenomena.

## The relationship between fluorescence excitation light, image information
and phototoxicity

Fluorescence live-cell imaging involves the excitation of fluorophores using
light, resulting in photon emission. These photons can be captured by a camera
or sensor, facilitating the visualisation of cellular targets. Various
microscopy methods are available for sample illumination as depicted in Figure
$2$b. Some strategies, such as widefield, confocal microscopy, and Stimulated
Emission Depletion (STED), illuminate the sample across the optical axis. In
contrast, two-photon excitation primarily excites sample regions near the
imaging focal plane while also subjecting the sample to less harmful infrared
light across the optical axis. Total Internal Reflection Fluorescence (TIRF)
imaging optically limits illumination to near the coverslip surface,
substantially reducing in-depth illumination. However, it generally only
illuminates the interface between the sample and the coverslip. Light-sheet
microscopy, a more live-cell-friendly approach, illuminates only around the
focal plane of the sample but often poses challenges in achieving high
resolution compared to alternative methods. Hence, the choice of microscopy
depends on sample characteristics; more sensitive specimens like embryos
benefit from gentler modalities such as light-sheet microscopy. Inversely,
more resilient samples can withstand intense irradiation experienced during
Single-Molecule Localization Microscopy (SMLM) or STED microscopy (Figure
$2$b).

As introduced earlier, modern microscopy technologies seek to minimise the
required illumination by becoming more specific towards the type of
information that needs to be visualised. However, the acquired information’s
quality may depend on factors such as the signal-to-noise ratio (SNR), image
contrast, and temporal and spatial resolution of the image data (Figure $2$c).
Each microscopy equipment has inherent limitations that affect these
properties’ optimisation, necessitating a trade-off, which is often expressed
by the microscope ’pyramid of frustration’ (51), or as characterised
explicitly for super-resolution microscopy by Jaquemet et al. (52). This
trade-off can be optimised based on experimental needs by adjusting light
exposure intensity and acquisition speed - common parameters used to achieve
less harmful imaging configurations. As such, methods that enhance image
quality from sample-friendly setups, like deep learning methods, are
particularly valuable when pushing against intertwined image information and
phototoxicity limitations (Figures $1$ and $2$c). Specifically, deep
learning’s growing ability to augment and refine image-based information is
attracting considerable interest in the microscopy community. It is becoming
one of the most popular strategies to enable imaging setups with reduced
phototoxicity (53, 25). In succeeding sections, we explore different
strategies conceived to enhance microscopy image data’s information contrast
and quality, which could facilitate varied live-cell imaging setups with
decreased fluorescent light illumination.

## Deep learning for microscopy to the rescue

Figure 3: The deep learning landscape for a gentler live-cell microscopy
imaging. a) Types of deep learning methods according to the training strategy.
b) High illumination intensities are used to obtain images with high SNR at
the expense of causing photobleaching, among others. Reducing the fluorescence
illumination intensity prevents photobleaching at the expense of obtaining
images with low SNR. These common limitations in traditional live-cell imaging
can be overcome by using deep learning to enhance the image contrast and
extend the acquisition in a gentler manner. Additionally, one could image
fixed samples to create the training data. c-f) Deep learning models can be
used to run microscopy acquisitions that use lower fluorescence light
intensities or illuminate the sample less often. For this, after the imaging
experiment, one could c) restore and denoise images; d) improve the temporal
resolution by inferring intermediate time points; or e) virtually super-
resolve images with supervised super-resolution or with generative approaches
for cross-modality style transfer. f) One could avoid using fluorescence
illumination with virtual staining or partially, by using unmixing approaches
that can decouple different structures from autofluorescence or crosstalk
between channels. a), c), e) were extracted and modified from (54); b) and d)
from (55); and f) from (54) for the virtual labelling and (56) for the
unmixing. SNR: signal-to-noise ratio.

The exceptional ability of deep learning to automatically discern, uncover,
and summarise complex patterns within images is undeniably one of its most
transformative contributions to bioimaging. It has significantly influenced
microscopy image analysis by ushering in a shift from traditional mathematical
feature modelling to data-driven methods (57, 21, 58, 22, 23). We have
observed the potential of deep learning to achieve remarkable accuracy and, in
some cases, human-level performance in diverse computer vision tasks such as
segmentation, denoising, detection, reconstruction, unmixing, and response
prediction. Additionally, emerging imaging processing tasks like cross-modal
style transfer (59, 60) and virtual labelling (61, 62, 63, 64) hold promising
prospects due to the flexibility they introduce when designing imaging
experiments (_e.g.,_ spectral unmixing or rapid low-resolution acquisitions
that can be virtually super-resolved for subsequent quantification) (21).
Indeed, microscopy imaging naturally allows for creating paired image datasets
by alternating acquisition setups and combining different modalities (65, 54),
simulating data (66, 67, 68, 69, 70), or, recently, developing correlative
approaches such as CLEM (71). Cumulatively, all these advancements have laid a
solid foundation for the growing field of deep learning-augmented microscopy
(Figure $3$).

Regardless of the strategy, the key idea behind deep learning is to define
models that learn to identify or enhance specific features directly from the
data. Traditionally, the learning process, or training, has been classified as
supervised, where the model is trained with paired input-output image
datasets, or unsupervised, where the model is only exposed to input images
during training (Figure $3$a). Typically, supervised approaches have
demonstrated superior accuracy and specificity to the task and data
distribution, but their versatility relates to the availability of paired
images, which could be a limitation in live microscopy. Some alternatives (51,
55, 72) propose training models using paired images of _ex vivo_
samples—providing perfectly aligned images for training and assessment—to
subsequently perform inference with _in vivo_ images (Figure $3$b).
Importantly, collecting images from fixed images supports faster creation of
more extensive and diverse datasets than live imaging. Depending upon the
image features, simulated data may also be viable for training such models
(67, 69, 66, 68). However, there remain scenarios where obtaining such paired
datasets does not encapsulate the complexity of live experiments, is not
experimentally feasible or cross-modality acquisition devices are
inaccessible. Given this, alongside time-consuming data annotation processes,
has propelled exploration into alternative approaches such as semi- or weakly-
supervised (73), self-supervised (74, 75), or generative techniques (59, 63,
76) (Figure $3$a).

Many possibilities exist to exploit deep learning-augmented microscopy and
reduce phototoxicity. Drawing inspiration from the delineation provided in
(22), we distinguish strategies that aim either to surmount the physical
limitations intrinsic to live fluorescence microscopy imaging (_i.e._ ,
acquisition speed or illumination) or to enhance the content in less
qualitatively superior but more sample-friendly image data. The former
includes techniques such as denoising, restoration, or temporal interpolation.
The latter, referred to by the original authors as "augmentation of microscopy
data contrast", includes techniques such as virtual super-resolution (59, 77,
78, 65, 79, 80).

Recent denoising (81, 74, 75) and restoration approaches (51, 82, 83, 84, 85,
86) have successfully virtualised noise removal, enhanced the SNR, improved
fluorescence channel contrast with unparalleled accuracy, or provided
isotropic reconstruction of volumetric information of images with low SNR
(Figure $3$c). That is, they support imaging setups with reduced fluorescence
illumination that results in lower SNR or reduced optical $3$D sectioning,
which in turn, are gentler for the sample. Similarly, acquisition can be
slowed down and use intelligent interpolators like CAFI (87) or DBlink (69) to
recover temporal information (Figure $3$d). As we discussed earlier, reducing
the number of illumination time points can significantly decrease the
cumulative phototoxicity while potentially enabling some degree of photodamage
recovery.

Another innovative approach to enable reduced illumination imaging setups is
exploiting cross-modal style transfer methodologies. In brief, these methods
involve training a model to translate images between varied microscopy
modalities (Figure $3$e). For example, it was shown that SIM images could be
inferred from input images acquired with wide-field illumination (65),
reducing the photon dose by a factor of $9$/$15$ in $2$D/$3$D. This capability
extends to numerous types of fluorescence microscopy modalities such as
confocal to STED (59, 26), SIM and SRRF (54), or wide-field to SMLM (88, 67,
89). The enhancement in spatial resolution via learning fine details is
similar in objective to traditional deconvolution. It offers comparable
benefits for mitigating phototoxicity, such as using less aggressive imaging
modalities (widefield and/or confocal against SIM, STED, and SMLM). While the
extent these methodologies can contribute towards scientific discovery is up
for debate (21, 90), they undoubtedly elevate image data quality which
directly impacts subsequent tasks like accurate tracking or segmentation (51,
87). As suggested in (51), less aggressive live imaging approaches may yield
visually less appealing but easier-to-analyse data due to the reduction in
artefacts induced by photodamage (_e.g._ , apoptosis, stressed cells or
specimen shrinkage during illumination) with the benefit of preserving close-
to physiological conditions.

Exploiting the prowess of data-driven methods, artificial labelling approaches
have emerged (Figure $3$f). They span from inferring cell nuclei (_e.g._ ,
Hoechst staining) from actin (e.g., Lifeact staining) (54), to estimating
specific fluorescence information (_e.g._ , nucleoli, cell membrane, nuclear
envelope, mitochondria or neuron-specific tubulin) from brightfield input
images (91, 61). It’s worth noting that the latter technique is also
categorized as a cross-modality style transfer approach. Moreover, artificial
labelling can also be employed for channel unmixing (92, 93, 94), which offers
several key advantages, including illumination channel reduction, acceleration
of image acquisition, or support for more straightforward or cost-effective
imaging experiments (Figure $3$f). Among these benefits, the former is pivotal
in enabling more sample-friendly setups. Indeed, artificial labelling often
works as an intermediary step for further quantification such as segmentation
or tracking (60, 54).

Beyond the questions present also in the natural domain, novel inpainting and
modality transfer techniques pose an additional challenge in bioimaging: they
must accurately infer reliable and quantifiable physiological information.
Thus, further validation, more assessment methodologies, and standard
quantitative strategies ought to ascertain both the biological reliability of
restored images and the integrity of recovered signal intensity (_i.e.,_
virtual images) are needed (21, 95, 96).

Biomedical data typically exhibit high variability due to factors such as the
biological sample’s physiology, experimental protocol, imaging setup, and even
the individual researcher conducting the experiments. Defining and
establishing an accurate ground truth becomes crucial (95, 97), as deep
learning model training highly depends on the data quality. Quality in terms
of image processing ranges from the number of images available to train to how
suitable the features of these images are for the task to train the model on.
For instance, it remains to be seen how to strike an optimal balance between
data acquisition and model accuracy (_i.e._ , defining an ideal sampling
frequency for precise cell tracking). A prevalent notion suggests that larger
training datasets enhance model performance. Simultaneously, many publicly
available annotated or paired databases are growing (98, 99, 97). However,
strategies to combine these datasets effectively while preserving each
experimental setup’s specificity still need to be clarified. Furthermore, pre-
trained models that facilitate fine-tuning and transfer learning are readily
accessible thanks to trained deep learning model repositories such as the
Bioimage Model Zoo (100) and MONAI (101). Nonetheless, further enquiry is
necessary to ascertain the best practices for assembling adequate training
data and executing effective transfer learning while considering data quality,
image features, and task-specific analytical requirements. Because live-cell
image data is usually highly redundant, such optimisation could maximise
information extraction from images while minimising photodamage during the
acquisition.

Life scientists hold the top authority in deciding parameters such as sampling
frequencies, resolution, or fields of view. While these decisions may
sometimes be sub-optimal for subsequent quantification, they are our best
reference for accurate performance. Incorporating users in the loop or their
expertise as priors to guide model performance towards more specific and
biologically relevant results is one of the pro missing directions to take
into AI-enhanced microscopy. Recent technological advances such as the
analytical representation of sparse and raw information to create priors - an
approach already proposed for the segmentation of natural pictures in works
such as the Segment Anything Model (SAM) (102) \- is an important step towards
it.

## Future Outlook

Figure 4:  AI-enhanced photodamage-aware microscopy. In practice, a more
gentle imaging setup would balance the health state of the imaged sample and
the quality of the information obtained with deep learning augmented
microscopy. Several aspects, such as the levels of phototoxicity, the
properties of the extracted image information, and the different tasks that
one could perform with deep learning, their expected accuracy and their
likelihood to fit well in the experiment must be considered when deciding an
optimal imaging configuration. In this way, combining advanced image
processing in microscopy acquisition pipelines would support reproducible and
optimised conditions for live-cell imaging. The source images for this figure
are provided by (54, 55)

With the advance of fluorescence microscopy technology, the field is
developing intelligent imaging techniques to minimise photodamage and enable
accurate observations of biological dynamics. Similar to automatic cars or
smart robots in industrial environments, microscopes can be empowered with AI
components that enable real-time decisions by integrating the information
extracted from the observed data into an intelligent feedback loop that
balances the health of the sample and the image data quality (Figures $2$c and
$4$) (53). Note that we refer to image quality as the combination of features
(_e.g.,_ resolution in time and space, SNR, the size of the field of view, or
the number of fluorescent channels) that allow getting the most information
relevant for the understanding of the observed process. In line with these,
some have already conceptualised and proved such systems. In the first place,
we find event-driven approaches, which automatically identify specific objects
or incidents in the image that trigger the acquisition in real-time (103, 104,
105, 106, 107). While these adaptive approaches reduce the induced
phototoxicity by only illuminating the sample when needed, in most cases, they
are equipped with deep learning models trained to recognise predefined objects
or elements in the images, which is not always the case in biology and may
limit, even bias, the observation of novel physiological processes.
Alternative approaches propose the integration of image resolution enhancement
in the loop to get faster and gentler setups. In (24), a deep learning model
is trained and validated in the acquisition loop to enhance the volumetric
reconstruction and provide an adaptive light field microscopy (LFM) setup. In
the context of super-resolution imaging, Bouchard _et al._ (26) propose
evaluating the quality of virtually inferred STED images from confocal
microscopy images to determine the uncertainty in the observed sample and
determine whether a new STED image should be acquired or not. All these works
pose new paradigms in the realm of smart microscopy.

Deep learning approaches, particularly the unsupervised ones, learn and match
data distributions even in highly heterogeneous or complex scenarios without
the need for human descriptions or annotations. As mentioned in (108), such
methods could be exploited to identify the events that deviate from the
general distribution, _i.e._ , to discover new biological patterns. Thus,
advancing generative models and unsupervised/self-supervised approaches that
can effectively learn from unpaired data alone can provide flexibility when
paired datasets are difficult to obtain experimentally or when investigating
new dynamics, and contribute to unbiased observations.

Straightforwardly, one would realise that despite sample health preservation
being a strong motivation and a major limitation in live-cell imaging, none of
the existing solutions analyses or estimates it directly. Robust photodamage
reporters that provide quantitative assessments of sample health without
requiring additional fluorescence channels can directly contribute to more
reproducible biological readouts. This could involve exploring modalities like
transmitted light microscopy or label-free techniques. Namely, quantitative
reporters would support the design of automated workflows that analyse sample
health in real-time during image acquisition rather than only evaluating the
image quality (Figure $4$). This would allow the detection of early signs of
photodamage and the adaptive determination of optimal imaging conditions. In
other words, it will open the door for data-driven sample-oriented live
microscopy.

Pursuing such technical innovations while deepening our understanding of
photodamage mechanisms will enable microscopists to unlock the full potential
of intelligent imaging. With photodamage-aware AI and automated tools, the
goal of observing undisturbed physiological processes can be realised. This
will profoundly enhance fluorescence microscopy’s capacity to uncover ground
truths in biology.

## Discussion

Fluorescence microscopy has become an indispensable tool in cell biology,
providing unparalleled insights into biomolecular dynamics. However,
phototoxicity remains a major impediment, necessitating a deeper mechanistic
understanding alongside the development of imaging techniques that mitigate
this limitation. While the intercourse between microscopy hardware innovation
and computational imaging has yielded promising solutions, standardised
methodologies to comprehensively assess photodamage are lacking. Recent
strides in deep learning provide optimism by enhancing information extraction
from low-light or accelerated acquisitions. Nonetheless, robust validation
strategies are still required to ensure biological fidelity.

A key opportunity lies in constructing universal photodamage metrics that
account for subtle, cumulative deviations in sample physiology. Integrating
such quantifications in intelligent automated analysis enables microscopes to
optimise imaging conditions dynamically. This calls for a convergence of
synergistic advancements spanning photodamage biology, microscopy hardware,
computational imaging and model interpretation. An outstanding challenge is
model training, which requires extensive paired datasets that sufficiently
encapsulate biological variability. Alternatives like unsupervised learning
provide flexibility but may compromise accuracy. Incorporating biological
expertise through techniques like priors and prompts appears promising to
guide models. Additionally, optimised strategies for effective model training
and validation must be developed through empirical examination.

While deep learning has elevated imaging capabilities, over-reliance on its
reconstruction prowess could promote complacency. Achieving gentler
acquisition first requires re-evaluating illumination intensities and sampling
frequencies. We claim that AI should extract the maximum information from the
least invasive data rather than recover information already compromised by
excessive phototoxicity. Keeping biological relevance at the crux while
exploiting technology will lead to microscopes that truly observe life
undisturbed.

E.G.M., M.DR., and R.H. conceptualised the majority of the manuscript. E.G.M.
and M.DR. wrote the manuscript with input from J.W.P., G.J., and R.H. J.W.P.
and G.J. contributed text, figure design, critical comments, and conceptual
suggestions to improve the manuscript. J.W.P., G.J., and R.H. reviewed and
edited the manuscript.

###### Acknowledgements.

M.DR., E.G.M. and R.H. acknowledge the support of the Gulbenkian Foundation
(Fundação Calouste Gulbenkian), the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation programme (grant
agreement No. 101001332), the European Commission through the Horizon Europe
program (AI4LIFE project with grant agreement 101057970-AI4LIFE, and RT-
SuperES project with grant agreement 101099654-RT-SuperES), the European
Molecular Biology Organization (EMBO) Installation Grant (EMBO-2020-IG-4734)
and the Chan Zuckerberg Initiative Visual Proteomics Grant (vpi-0000000044
with DOI:10.37921/743590vtudfp). E.G.M. was also supported by EMBO
Postdoctoral Fellowship (EMBO ALTF 174-2022). This study was supported by the
Academy of Finland (338537 to G.J.), the Sigrid Juselius Foundation (to G.J.),
the Cancer Society of Finland (Syöpäjärjestöt; to G.J.), and the Solutions for
Health strategic funding to Åbo Akademi University (to G.J.). This research
was supported by InFLAMES Flagship Programme of the Academy of Finland
(decision number: 337531).

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