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Editorial
Artificial intelligence for image interpretation in
ultrasound-guided regional anaesthesia
J. Bowness,
1,2
K. El-Boghdadly
3,4
and D. Burckett-St Laurent
5
1 Clinical Lecturer, Institute of Academic Anaesthesia, University of Dundee, Dundee, UK
2 Honorary Specialty Registrar, Department of Anaesthesia, Ninewells Hospital, Dundee, UK
3 Consultant, Department of Anaesthesia and Peri-operative Medicine, Guy's and St Thomas's NHS Foundation Trust,
London, UK
4 Honorary Senior Lecturer, King's College London, London, UK
5 Consultant, Department of Anaesthesia, Royal Gwent Hospital, Newport, UK
.................................................................................................................................................................
Correspondence to: J. Bowness
Email: james.bowness@nhs.net
Accepted: 1 July 2020
Keywords: anatomy; artificial intelligence; blocks; machine learning; regional anaesthesia; ultrasound
Twitter: @bowness_james, @elboghdadly
Here is my prophecy: In its final development, the
telephone will be carried about by the individual,
perhaps as we carry a watch today. It probably will
require no dial or equivalent, and I think the users will
be able to see each other, if they want, as they talk.
——Mark R Sullivan (Pacific Telephone and Telegraph
Co., 1953)
The initial challenge presented to a practitioner during
ultrasound-guided regional anaesthesia is the
interpretation of sono-anatomy upon placing a probe on
the patient. To date, technological advancements have
focused on methods to enhance needle viewing [1]. Sono-
anatomical interpretation remains an under-explored
avenue of research to improve the availability, efficacy and
safety of regional anaesthetic techniques. We present the
case for the use of artificial intelligence (AI) in identifying key
anatomical features to facilitate ultrasound-guided regional
anaesthesia.
Ultrasound image analysis in
ultrasound-guided regional
anaesthesia
Ultrasound guidance has been a major advancement in
regional anaesthesia since the turn of the century. It is often
accepted that ultrasound has led to improved outcomes
following regional anaesthesia, although it is not clear that is
has reduced the incidence of nerve trauma [2].
The American Society of Regional Anesthesia and Pain
and the European Society of Regional Anaesthesia and Pain
Therapy joint committee recommendations for education
and training in ultrasound-guided regional anaesthesia
categorise four activities [3]:
1Understanding device operations
2Image optimisation
3Image interpretation (locating and interpreting anatomy
under ultrasound)
4Visualisation of needle insertion and injection (needle-
probe orientation; the maintenance of needle
visualisation; and optimal anatomical view whilst
moving the needle towards the target object)
Much effort has been directed towards needle
guidance systems and echogenic needles to improve
needle visibility [1]. However, augmenting image
interpretation has received less attention –despite a sound
understanding and interpretation of sono-anatomy being
required for the practice of ultrasound-guided regional
anaesthesia [3, 4]. This is particularly pertinent as anatomical
knowledge among anaesthetists is known to be imperfect
©2020 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists 1
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Anaesthesia 2020 doi:10.1111/anae.15212
[5]. Human image analysis is similarly fallible [6] and human
performance is subject to fatiguability [7].
The many difficulties in acquiring and maintaining the
skill sets involved in anatomical recognition and needle
guidance also restrict the number of clinicians confident
and able to perform ultrasound-guided regional
anaesthesia. Currently, the majority of peripheral nerve
blocks are performed by a restricted number of experts [4].
Breaking down these barriers may particularly enhance
uptake by non-expert regional anaesthetists. Ultrasound-
guided regional anaesthesia also has the potential to be
employed more widely, for example, by nurse anaesthetists,
emergency medicine physicians, armed forces/battlefield
medical practitioners and those treating pain in the chronic
pain clinic or palliative care. Widening patient access to
these techniques has potential to directly address several of
the anaesthesia and peri-operative care priorities of the
James Lind Alliance [8].
Artificial intelligence, machine learning
and deep learning in anaesthesia
Artificial intelligence is a general term which includes
machine learning and deep learning (Fig. 1). There has
been a recent proliferation of publications relating to the
utility of AI, in particular machine learning, in the peri-
operative setting [7, 9]. Most focus on systems to
assimilate and analyse data input from multiple sources,
to assist in pre-operative assessment and risk stratification,
monitor depth of anaesthesia/sedation, enhance early
detection of unwell patients, or predict intra-operative
adverse events (e.g. hypotension) and postoperative
outcomes (e.g. pain and mortality) (Table 1). However,
implementation of these technologies in clinical practice
is not yet commonplace [9].
Machine learning in ultrasound-guided
regional anaesthesia
Published work includes the study of automated nerve and
blood vessel identification for ultrasound-guided regional
anaesthesia [17]. Indeed, medical image interpretation is a
particularly popular focus of research in healthcare AI [18].
One such example is the collaboration between researchers
and clinicians at DeepMind (Alphabet Inc, Palo Alto, CA,
USA), Moorfields Eye Hospital and University College
London, who have developed a system which reaches or
exceeds expert performance in analysis of optical
coherence tomography [19]. A similar collaboration has
demonstrated equally successful results in the field breast
cancer screening mammography; with an AI system that is
capable of surpassing human experts in breast cancer
prediction [20]. It thus follows that image analysis in
ultrasound-guided regional anaesthesia could similarly be
an area in which assistive machine learning technology may
provide patient benefit.
Figure 1 A summary of artificial intelligence, machine learning and deep learning.
2©2020 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists
Anaesthesia 2020 Editorial
Given the complexity, diversity and operator
dependence (leading to inter- and intra-individual variation)
in ultrasound appearance of anatomical structures on
ultrasound, it is difficult to develop nascent AI algorithms to
recognise all salient features de novo [18]. Therefore,
automated medical image analysis can be trained to
recognise this wide variety of appearances by ‘learning from
examples’, which is the premise of machine learning [18].
Such assistive technology could be used to enhance
interpretation of sono-anatomy by facilitating target
identification (e.g. peripheral nerves and fascial planes), and
the selection of optimal block site through demonstrating
relevant landmarks and guidance structures (e.g. bone and
muscle). The safety profile may be enhanced by
highlighting safety structures (e.g. blood vessels) to
minimise unwanted trauma.
We postulate that providing a ‘head-up display’
(display within the user’s existing field of vision) of anatomy
in real time, as an adjunct to the conventional narrative and
instructions from an expert, may reduce the cognitive load
for less experienced operators. It may also reduce time
required for image acquisition and analysis and increase
operator confidence. This in turn may improve performance
in needle/probe manipulation by increasing spare cognitive
capacity for these activities. Head-up and instrument-
mounted displays have been proven to be of use in military
aviation and the automotive industry [21]. Furthermore,
computerised systems are not subject to fatigue and can
reproducibly perform the desired activity with complete
fidelity [7].
AnatomyGuide
TM
(Intelligent Ultrasound Limited,
Cardiff, UK) is a system based on AI technologies. It has
been developed with the use of B-mode ultrasound video
for specific peripheral nerve block regions. Each video is
broken into multiple frames, with each frame receiving a
coloured overlay of specific structures identified as either
landmarks, safety structures or targets. These labelled
frames are then used to train the machine learning
algorithm, which uses deep learning to develop
associations between the labels and underlying structures.
Table 1 Potential artificial intelligence applications to anaesthetic practice based on examples of current evidence.
Area of practice Application
Pre-operative Risk stratification during pre-operative assessment (to influence anaesthetic technique and for outcome prediction)
-Karpagavalli et al. [10] trained three supervised machine learning systems on pre-operative data
(37 features) from 362 patients
-These systems were able to accurately categorise patients into low, medium and high-risk groups
(broadly correlating with ASA grade)
Intra-operative Automated ultrasound spinal landmark identification in neuraxial blockade
-Oh et al. [11] have demonstrated improved spinal ultrasound interpretation and first pass spinal success
using an intelligent image processing system to identify spinal landmarks
Prediction of post-induction/intra-operative hypotension
-Wijnberge et al. [12] demonstrated the ability to reduce the duration and depth of intra-operative
hypotension through the use of a machine learning-derived early warning system
Prediction of post-intubation hypoxia
-Sippl et al. [13] retrospectively analysed data from 620 cases to develop a machine learning system
capable of predicting post-intubation hypoxia to the same level as that observed by medical experts
Monitoring/control of level of sedation/hypnosis
-Lee et al [14]. present a deep learning model, training on data sets from 131 patients, to predict bispectral
index response during target-controlled infusion of propofol and remifentanil
Postoperative Prediction of postoperative in-hospital mortality
-Fritz et al. [15] present a deep-learning model based on patient characteristics and peri-operative data
to predict 30 day mortality
Prediction of analgesic response
-Misra et al. [16] use machine learning for the automated classification of pain state (high and low)
based on EEG data
EEG, electroencephalogram.
©2020 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists 3
Editorial Anaesthesia 2020
In time, the algorithm is able to label raw B-mode ultrasound
data in real-time on new ultrasound scans of similar regions.
System performance is a function of the quantity and quality
of labelled data presented during training: the training set
used for each block included over 120,000 images to
achieve the current level of performance.
One example of a peripheral nerve block for which a
model has been well developed for AnatomyGuide is the
adductor canal block. Information used to train the
algorithm is similar to that used for an inexperienced
operator in clinical practice by identifying the relevant
anatomy. In this model, the sartorius and adductor longus
muscles, as well as the femur, were first identified as
landmarks. The optimal block site is chosen as the region
where the medial borders of these two muscles align. The
femoral artery is labelled as both a landmark and safety
structure. The saphenous nerve is labelled as a target. The
intent is to assist the operator in identifying the nerve and
correct site to target for the block (Fig. 2 and Supporting
Information, Video S1).
Extended uses of machine learning
systems in ultrasound-guided regional
anaesthesia
Gaining early competencies in ultrasound-guided regional
anaesthesia is particularly challenging. It is difficult to
develop and use high-fidelity simulation, and training in the
clinical setting can be inconsistent. Experience is often
gained on an ad hoc basis, with long time intervals between
episodes, and different trainers may have differing
approaches. Assistive machine learning systems may
provide supplementary information to facilitate ultrasound-
guided regional anaesthesia training for inexperienced
operators. Simply highlighting the relevant structures will
aid understanding of their likely position and appearance in
future ultrasound analysis. This may aid in the initial skill
acquisition, and shorten the period required for direct
supervision, supporting the transition to indirectly
supervised/solo practice.
In the era of competency-based training, quantitative
assessment and evaluation of operator expertise is
important but difficult. It is often not practical in the clinical
environment and innovation is required. Methods to aid
assessment include an approach based on proficiency-
based progression [22]. By using descriptions of
ultrasound-guided regional anaesthesia performance,
broken down to specific actions, machine learning analysis
of data (e.g. video recording of operator, analysis of
sonographic video or needle tracking technology) can
provide an evaluation of the quality of operator
performance. Assuming a robust and successful evaluation
of such systems, this method may facilitate standardised
Figure 2 Sono-anatomy of the adductor canal block. (a) Illustration showing a cross-section of the mid-thigh. (b) Enlarged
illustration of the structures seen on ultrasound during performance adductor canal block. (c) Ultrasound view during adductor
canal block. (d) Ultrasound view labelled by AnatomyGuide.
4©2020 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists
Anaesthesia 2020 Editorial
assessment of operator performance, and reduce
subjectivity in evaluation/assessment [23].
Furthermore, it has been suggested that a move
towards standardising the implementation of regional
anaesthesia may engage a greater body of anaesthetists in
its practice [4]. Computational systems, by their nature,
assess novel data in a consistent manner, thus their use
could act as a conduit to facilitating the recommendation to
standardise ultrasound-guided approaches to peripheral
nerve blocks [4].
Potential limitations of machine
learning systems in ultrasound-guided
regional anaesthesia
Technological advancement is not without potential pitfalls
and the regulatory landscape for AI applied to medical
imaging is still developing. Few products have obtained
regulatory approval to date, particularly those evaluating
images in real-time. A personal teaching approach should
remain central to training in ultrasound-guided regional
anaesthesia and should not be replaced by ‘technological
supervision’. Operators must still learn where to
commence ultrasound scanning, and must assimilate the
nuances of probe pressure, angulation, rotation and tilt to
optimise image acquisition. Integrating AI into image
analysis may allow an uneven progression of training
between sono-anatomical recognition and needle-probe
co-ordination.
In time, there will need to be evidence that such
systems improve operator performance and patient
outcomes to justify continued development and
implementation in clinical practice. There is potential for
inaccuracies in the labelling of anatomy in such a system;
strict validation and quality control will need to apply,
particularly in the context of atypical or complex clinical
presentation and anatomy. Such reservations are applicable
to all new AI technologies, and previous methodological
concerns exist including poor validation, over prediction
and lack of transparency [24].
Early models will inevitably be improved upon but even
the first systems employed in clinical practice must offer
superior ultrasound image analysis to the non-expert
practitioner. A subsequent, and more stringent, challenge
will be to ensure they augment operators with high-level
expertise, but machine learning systems are not guaranteed
to be superior to human performance [23] and systems
should not be relied upon to replace clinician knowledge.
Conversely, identifying features and associations that are
not regularly viewed by eye might not improve clinical
performance or outcomes.
Artificial intelligence systems for ultrasound may
require the acquisition of new ultrasound machines, or be
retro-fitted to current devices, both of which may
understandably delay uptake and incur cost. Finally,
unpredictable clinical implications will likely emerge; these
should be anticipated and addressed where possible.
Conclusion
Despite early promise, the potential for utilisation of AI in
medical image analysis is yet to be realised, and few
applications are currently employed in medical practice
[25]. In particular, machine learning for ultrasound-guided
regional anaesthesia appears to have received relatively
little attention. Anatomical knowledge and ultrasound
image interpretation are of paramount importance in
ultrasound-guided regional anaesthesia, but the human
performance and teaching of both are known to be fallible.
Robust and reliable AI technologies could support clinicians
to optimise performance, increase uptake and standardise
training in ultrasound-guided regional anaesthesia. Mark R
Sullivan realised the potential of the mobile telephone
decades before they impacted the public consciousness.
Our belief is that AI systems in healthcare will have a similar
impact, and include the field of ultrasound-guided regional
anaesthesia, offering innovative solutions to change service
provision and workforce education. Anaesthetists should
embrace this opportunity and engage in the development
of these technologies to ensure they are used to enhance
the specialty in a transformative manner.
Acknowledgements
The authors would like to acknowledge the contributions of
Dr F. Zmuda (Fig. 1) and Dr J. Mortimer (Fig. 2) for the
production of illustrations used in this article. JB is a Clinical
Advisor for and receives honoraria from Intelligent
Ultrasound Limited. KE has received research, honoraria and
educational funding from Fisher and Paykel Healthcare Ltd,
GE Healthcare, and Ambu, and is an Editor for Anaesthesia.
DL is a Clinical Advisor for and receives honoraria from
Intelligent Ultrasound Limited and is the Lead Clinician on
AnatomyGuide. No other competing interests declared.
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Supporting Information
Additional supporting information may be found online via
the journal website.
Video S1. Sub-sartorial distal femoral triangle (adductor
canal) block: real-time anatomy overlay by AnatomyGuide.
6©2020 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists
Anaesthesia 2020 Editorial
