# Intra-operative imaging solutions for surgeons and the difficult quest for gold-standards of surgical skill

Birgitta Dresp

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Birgitta Dresp. Intra-operative imaging solutions for surgeons and the difficult quest for gold-standards of surgical skill. 2024, 10.56367/OAG-044-11672. hal-04877927

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# Intra-operative imaging technology and the difficult quest for gold standards of surgical skill

Birgitta Dresp-Langley

UMR 7357

Centre National de la Recherche Scientifique (CNRS)

FRANCE

In minimally invasive interventional procedures such as laparoscopic surgery, surgeons do not have a direct view of the organ or tissue they are interacting with, but are constrained to navigate the surgical instruments on the basis of image views provided by dedicated camera systems. Image-guided interventions have been on the rise in recent years as they are less invasive than traditional methods, and believed to enable more accurate surgical gestures. Yet, the surgeon's brain needs to re-interpret the visual information available in the image given. This has to be achieved swiftly and reliably for controlling the instruments with the greatest ease and accuracy, yielding the most precise surgical gestures in the shortest time possible. How can we assess critical parameters of surgical skill, and its evolution with time and training, under different conditions imposed by different technological solutions?

**Keywords:** Image-guided surgery; image technology; performance; surgical skill optimization; benchmark criteria

The most adequate, comfortable, affordable, and sustainable interventional imaging technology should allow the largest possible number of domain surgeons to perform to the best of their skill, whether the given imaging system is assisted or not by a robot and regardless of the extent to which the system relies on assistance by Artificial Intelligence. We know from research in cognitive engineering that there is an exchange function between the accuracy, or the precision, and the timing of skilled performance. Relating these two in the context of a real surgical task is anything but simple for lack of measurement criteria for surgical precision. Currently, time to task completion prevails in assessing high level skill and its evolution with training. The field is struggling to find adequate methods and procedures for more comprehensive performance-based skill assessment, which has been the “holy grail” for over a decade now. As a consequence, there still are no gold standards ensuring that the best technology for surgeons and patients will be developed and placed on the market, no silver bullet for objectively assessing the adequacy and sustainability of different intra-operative imaging solutions for the surgeons. Research in cognitive motor planning and control has highlighted some of the drawbacks of virtual reality headset viewing, found to negatively affect timing and precision of gestures in image-guided simulator tasks.

In *virtual reality* viewing, image representations need to be processed and interpreted by the brain to ensure the correct positioning of hands and tools while surgeons are looking at a three-dimensional (3D) reconstruction of the camera image through a computer controlled virtual reality (VR) headset. A major problem with virtually viewed three-dimensionality is that depth information needs to be reconstructed by the brain, which has learnt to assess visual depth and relative distances between real-world objects on the basis of how far they are away
from the individual's own body. Reliable body-to-space representations for depth and relative
distance estimates cannot be generated with virtual 3D image representations. As a
consequence, time-to-contact anticipation, movement planning, and eye-hand coordination are
made more difficult for the surgeon. From the classic two-dimensional image views provided by
high resolution laparoscopic cameras to new types of processed, virtual or augmented reality-
enriched, nanotechnology enhanced imaging solutions or scanned images superimposed on
camera views, there now is a multitude of technological solutions that may be more or less
appropriate for a given type of intervention. Augmented reality (AR) technology in image-guided
surgery is developed on the premise that organs and tissues become more salient when
enhanced by AR, i.e. spatial relationships can be visualized better by the surgeon. It is deemed
that this advantage is bound to improve surgical accuracy, yet, there seem to be no objective
criteria for testing the validity of this assumption.

In computer-assisted orthopaedic surgery, for example, bone location and CT data may
be visualized in three-dimensional AR. To this effect, a target needs to be attached to the bone
for tracking in a system with a very narrow focus of vision rendering notoriously poor depth
perception. Irreversible electroporation of tumours in the prostate or the pancreas, which is a
needle-based intervention, may benefit from AR systems that provide visual guidance for
planning needle insertion trajectories. These systems, however, produce tracking lags because
they lack in precision to reliably access raw sensor data for tracking moving targets, and they also
only have a limited field of vision. In vascular surgery, testing live views of the patient captured
and merged with preoperative volume-rendered vessels, AR viewing was found to add little
value to complex arteriovenous anomalies in terms of localizing the large draining vein, and
sometimes arteries were mistaken for veins and vice versa. Proof-of-concept using a phantom
model to allow fusion of preoperative single-photon emission computed tomography (SPECT)
and computed tomography (CT), also known as intra-operative SPECT/CT, generates a new breed
of state-of-the-art augmented reality (AR) in a surgical guidance system for pelvic sentinel lymph
node (SLN) detection in endometrial cancer patients. This innovative and promising approach
opens novel perspectives.

Artificial intelligence (AI) has been announced as having great potential in a wide
variety of applications in interventional radiology, for the support of decision-making and
outcome prediction. New functions and improvements in fluoroscopy, ultrasound, computed
tomography, and magnetic resonance imaging have been developed for image guided
interventions. The significant boost for fusion imaging, simulation, and robotic interaction
promised by AI, however, is severely limited by the procedural nature, heterogeneity, and lack of
standardisation and regulatory certainty. Whether a technological solution suits and will be
pushed for further development and optimization is currently established by lead surgeons in
the given field. The technology the stars of the profession deem the most promising then
becomes the gold standard by default. While this seems to make sense to the extent that
respected domain authorities will then adopt the technology of their choice to develop and
refine it in the context of their institutions, it does not ensure that this will advance the most
sustainable, affordable, effective, and comfortable solutions for surgeons in general. The
mechanisms through which a surgeon's brain adapts to any of the new technologies are
currently not known, nor is it possible to tell which technology optimally empowers them to
develop the finest of surgical skills possible. Finely tailored task designs with to-the-single-pixel
measurement of hand-tool movement trajectory precision in task time captured at the
millisecond level are currently possible in simulator environments only. Simulator training does
not straightforwardly transfer to clinical scenarios. Multidisciplinary training and research
programmes, within and between teams and within and across countries, will be necessary to
help establish reliable protocols and training procedures. The multi-criteria assessment of
surgeons' skill progress during consecutive training days of a curriculum with exposure to
different image-guided surgical training scenarios on several robotic systems sets the stage for
international surgeon-led training programmes responding to the growing need to integrate
domain competency in robotic approaches to national training programmes.

Before being placed on the market, new technology should have been widely evaluated
on the basis of objective if not universal benchmark criteria as the gold standard for training the
largest possible number of dedicated surgeons towards professional excellence and success. This
will lead to selecting the technological solutions that are the most adapted to surgeons’ needs
for achieving reliable planning and execution of highly precise surgical gestures. Technology only
a chosen few will be able to adapt to will not prevail in a domain that is to serve patients and
healthcare systems worldwide. Both patients and caregivers need and deserve the best, safest,
most widely accessible, affordable, and sustainable technology in the operating rooms of the
future.

References

Dresp-Langley, B. Towards Expert-Based Speed-Precision Control in Early Simulator Training for Novice Surgeons. *Information* 2018; 9(12):316. https://doi.org/10.3390/info9120316

Yamaguchi M, Crump MJ, Logan GD. Speed-accuracy trade-off in skilled typewriting: decomposing the contributions of hierarchical control loops. J Exp Psychol Hum Percept Perform. 2013; 39(3):678-99. doi:10.1037/a0030512.

Batmaz AU, de Mathelin M, Dresp-Langley B. Getting nowhere fast: trade-off between speed and precision in training to execute image-guided hand-tool movements. BMC Psychol. 2016; 4:55. doi:10.1186/s40359-016-0161-0

Blavier, A., Nyssen, AS. The effect of 2D and 3D visual modes on surgical task performance: role of expertise and adaptation processes. *Cogn Tech Work* 2014; 16:509-518. doi:10.1007/s10111-014-0281-3

Batmaz AU, de Mathelin M, Dresp-Langley B. Seeing virtual while acting real: Visual display and strategy effects on the time and precision of eye-hand coordination. *PLoS One*. 2017; 12(8):e0183789.
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