# Roadmap to Autonomous Surgery - A Framework to Surgical Autonomy

Amritpal Singh  College of Computing
Georgia Institute of Technology, USA  Corresponding author<EMAIL_ADDRESS>College of Computing
Georgia Institute of Technology, USA

###### Abstract

Robotic surgery has increased the domain of surgeries possible. Several
examples of partial surgical automation have been seen in the past decade. We
break down the path of automation tasks into features required and provide a
checklist that can help reach higher levels of surgical automation. Finally,
we discuss the current challenges and advances required to make this happen.

###### Index Terms:

Reinforcement learning, Surgery, Automation, Artificial intelligence,
Healthcare

##  I Introduction and Motivation

The term ”Autonomous surgery” has been used repeatedly in the literature [1]
[2] [3] [4] [5] [6] [7] with different interpretations in the author’s mind.
It has been used for pure robotic surgery without autonomous control, training
resident doctors, suturing automation, and path planning.

Robotic surgery systems like Da Vinci surgery robots provide a mechanical
system to develop and test different automation efforts. In general, Robotic
surgeries offer several advantages over minimal-invasive surgeries -
perception augmentation( medically imaging - allowing better surgical plan
execution, avoiding accidental trauma to vital organs, ability to not miss
behind any tumor region. and manipulation augmentation(overcoming technical
difficulties of using a manual laparoscopic tool - allowing increased distal
dexterity, manipulation of multiple instruments, improved precision, and
steadiness, allowing multiple surgeons to manipulate multiple instruments and
collaborate on surgeries). This will also allow surgeons to have a lower
cognitive and physiological burden associated with minimal instrumental
laparoscopy.

While previous works [1] [2] [8] [9] have laid the foundations on different
levels, the description of tasks required to achieve them is not discussed
clearly. Since several technological breakthroughs will be required in
robotics, artificial intelligence, and other industries, rather than talking
about these relevant technologies, we will talk about checkpoints required to
be achieved. In this article, we talk about sub-components of these systems
that will achieve a higher level of automation in different surgical
components. We will talk about why thinking in this framework is essential and
its potential benefits. This work aims to lay a roadmap for robotics and AI
engineers to get to higher levels of surgical automation.

Figure 1: Different points of interactions

##  II Breakdown of a typical surgery

A typical surgery involves many dynamics and is a team effort. The team
generally consists of at least a surgeon, nurse, and anesthetist. There is an
entire sequence of tasks done in any regular surgery. While the details might
vary based on site/type of surgery, the main flow stays somewhat similar.
Table. I represents different phases of surgery. Of these, automated systems
can be relatively good at postoperative tasks like documentation. At the same
time, anesthesia inducing and waning off is a difficult task in itself. For
full phase 5 in scenarios like military, battle surgery, or deep space
exploration, this becomes more critical, as there might not be someone to
assist with these tasks.

Phase | Surgeon | Nurse | Anaesthetist
---|---|---|---
Pre-operative planning | Imaging/Tests required, Equipment planning | Sterilise instruments | Induce anaesthesia
Intra-operative prep | Locate entry point, surgical scrub over patient | Assist | Monitoring vitals
Puncture | Skin incision, Port creation | Assist | Monitoring vitals
Operative phase | Perform surgery | Assist | Monitoring vitals
Concluding steps | Skin suturing | Assist | reduce anaesthesia
Post-operative | Documentation | Documentation | Patient hand over
Table I: Dynamics of typical surgery

###  II-A Phases of surgery

Any surgery can be divided mainly into preoperative, operative, and
postoperative phases. The perioperative period starts when the patient is
informed of the need for surgery until the patient resumes their normal
activities after the surgical procedure. The preoperative phase starts after
the patient is informed of the need for surgery and is prepared physically and
psychologically for the surgery. Intra-operative phase mainly spans the
patient’s stay inside the operative room. Postoperative phases begin from the
patient’s transfer from the operative room until the resolution of surgery
sequelae.

In 2017, authors in [2] introduced the six levels of surgical automation,
starting from no automation at level 0 to full automation at level 5. [1], [2]
elaborated on this definition with examples of some commercially available
versions of them. Currently, most approved systems are level 1, with few
examples of level 2 and 3 systems. Rising higher levels will help augment
surgeons, allowing higher precision surgery and improvements in time
efficiency. Levels 4 and 5 systems will be capable of automating some or most
parts of surgery. These systems are primarily fictional as of today and can
have a potential use case to treat soldiers in battle and deep space
explorations. Substantial efforts are being put into bringing this into
reality. These frameworks do not consider preoperative and postoperative care,
which play an essential role in patient prognosis and are critical to
developing level 5 systems.

In this work, we introduce different wings of surgical automation -
Preoperative, Operative, and Postoperative. Most work has happened in the pre
and postoperative phases. Over the last decade, we have started seeing more
and more work happening around the operative phase - suturing, path planning,
and a better field of view.

Figure 2: Different levels of Surgery automation

####  II-A1 Preoperative surgery

A lot of work has happened around the preoperative phase. This ranges from
patient selection for surgery, prognosis prediction, patient prep for surgery,
instrument planning, and scrubbing patients. The last 2 need more attention.

####  II-A2 Operative surgery and Levels of Automation

Other than performing physical surgical procedures, any autonomy will require
patient monitoring along with other well-established requirements like
navigating to the area of interest, tissue manipulation, and
Dissect/Implant/Incision. Following is a summary of levels of automation as
discussed in several works [3] [4] [5] [6] [7].

  * •

Level 0 - No automation - Manually controlled. Humans monitor patient vitals.
Open surgeries and Mechanically lap surgeons are examples of this. The human
surgeon performs all the tasks

  * •

Level 1 - Surgical assistance - Humans are assisted but still physically
perform the surgery. Intraoperative image guidance.

  * •

Level 2 - Partial automation - Robotic hip arthroplasty robot. - reduced level
of human input required. Predict emergencies, Patient prognosis, Efficiency -
Helps in - Navigation, difficult vision, saves time in long procedures Predict
emergencies, Patient prognosis

  * •

Level 3 - Conditional Automation - Automated some components. Cyberknife,
Automated preoperative planning. Surgeons provide instruction to execute a
specific task.

  * •

Level 4 - High Automation Robots are capable of performing most, if not all,
parts of surgery. While the system could do many of the sub-tasks like -
Initial puncture, Instrument navigation, Exploration of field, manipulation of
field, dissection/resect/implant the region, These actions are performed only
under human supervision, with the ability to override the actions. Since these
systems will require monitoring patients - the ability to monitor and respond
to patient vitals will be required. Will need to require detecting/predicting
emergencies, with follow-up response to restrict damage or request override.

  * •

Level 5 - Full Automation - Do not require human assistance/ monitoring -
Dynamic surgical task. Fully autonomous, versatile. It can be used in space
exploration. These systems could potentially innovate new, more efficient ways
of surgery. Human override components can be removed here. These systems can
do all the sub-tasks as well - Initial puncture, Instrument navigation,
Exploration of field, manipulation of field, dissection/resect/implant of the
region, as well as plan for these actions.

Level 5 systems will be a great step of artificial narrow intelligence, with
the ability to do specific complex tasks, with complex planning in closed
environments. Achieving this will take us one step closer to our understanding
to build AGI.

####  II-A3 Postoperative surgery

Attempts have been made to predict postoperative complications [37] and
adverse events [38] using NLP methods. Postoperative phase patients need to be
dosed off from the anesthesia and require active monitoring. This will be
crucial to no table-side surgeon/physician.

Levels |  Task |  Human interaction/ monitoring |  Features
---|---|---|---
Level 0 |  No Autonomy |  exclusively surgeon |  None
Level 1 |  Robot Assistance |  exclusively surgeon + supportive robot |  Tool tracking, Eye tracking, Stabilisation, Haptic feedback
Level 2 |  Task Autonomy |  perform task, but only after all parameter of task are defined |  Suturing, Tissue retraction, Ablation
Level 3 |  Conditional Autonomy |  devices strategy, to be approved by humans |  Tissue modelling, Navigation
Level 4 |  High Autonomy |  make a decision and execute it autonomously, under supervision of surgeon |  Tissue segmentation, resection, debridement
Level 5 |  Full Autonomy |  Perform all procedure on its own |  -
Table II: Different levels of Automation for Operative phases

##  III Current level of automation

With the development of robotic surgery, even without any automation (level
0), we can perform tele-operative surgeries. This became more important during
COVID times when elective surgeries could be performed without risk of
contamination. Adding a layer of augmentation or automation to these robotic
systems will help increase surgical efficiency. To name a few - Increased
efficiency and precision, hence reducing the duration of surgeries and making
longer-duration surgeries feasible. Augmenting human surgeons will allow for
Intelligent maneuvers, tissue damage avoidance, suturing in hard-to-reach
areas, camera control [10], Disturbance rejection to minimize tissue damage
[11], and path planning in soft tissue surgeries [12]. Authors in [13]
proposed another system for Smart Tissue Autonomous Robot (STAR) to enable
semi-autonomous robotic anastomosis on deformable tissue.

With the expansion of universal healthcare access to surgery, there is going
to be a deficiency of surgeons to tackle these tasks. High-Income countries
have 36% surgeons while their population is 18% of the world. The low-income
countries that account for 12% world population get only 2% of
surgeons[35][36]. The estimated increase in surgeons will not catch up with
the required demand to meet unmet surgical needs. Increasing the augmentation
of surgeons can help tackle this to some extent.

###  III-A Assistive tasks

Autonomous robotic systems for the removal of blood during surgical procedures
have been developed. In one such example, a 6 DOF robot with a dual camera,
aspirator, and a path planning algorithm to reach the aspiration site. [14].

In other cases, an algorithm for autonomous grasping of grasp soft tissues in
different modes is damage, slip, or safe grasp. On testing, no signs of tissue
tear or slippage were found[15]. The HEARO robotic system is another surgical
robot to assist the surgeon with orientation, reference information to
anatomical structures, and drill trajectory during otological and
neurosurgical procedures. [16]

###  III-B Partial Automation

Collaborative systems can help increase the efficiency of several surgical
processes. One such system was developed using a Smart tissue Autonomous
robot(STAR) for confidence-based supervised autonomous suturing to perform
robotic suturing tasks along with a surgeon collaboratively. Suture placement
accuracy was 94.74% on pure STAR automation and 98.1% accuracy with a 25%
human intervention[17].

Autonomous suturing has also been tried for in-vivo open soft tissue surgery,
using plenoptic three-dimensional and near-infrared fluorescent (NIRF) imaging
system. Consistency of suturing was tracked based on their average suture
spacing, the number of mistakes that required removing the needle from the
tissue, completion time, pressure at which the anastomosis leaked, and lumen
reduction in intestinal anastomosis. In comparison to manual laparoscopic
surgery and clinically (using RAS approaches), the outcome of autonomous
procedures was found superior to surgery performed by expert surgeons. The
system received this despite dynamic scene changes and tissue movement during
surgery[18].

present the Dexterous Surgical Skill (DESK) database for Knowledge transfer
between robots has also been tried. Two such approaches - No transfer and
domain transfer have been compared. In the no-transfer scenario, the training
and testing data were obtained from the same domain, compared to a domain-
transfer scenario where training data is a blend of simulated and real robot
data but tested on real robot data only. Authors in [19] tested this on
several types of robots and found that the transfer model showed an accuracy
of 81%, 97.5%, and 93% for the YuMi, Taurus II, and the da Vinci robot,
respectively. While, in the YuMi case, the ratio of real-to-simulated data was
22% to 78%, later were trained only with simulation data. This shows great
promise for the augmentation and automation of sub-tasks.

Figure 3: Trauma pod - diagram replicated from [20]

###  III-C Limitations

The limitation of such systems will be the ability to help in robotic
surgeries only. Open surgeries cannot be augmented. On the other hand, there
are surgeries that are not performed by robotic surgery systems like Da Vinci.
One such standard procedure is a cesarean section. At times, some laparoscopic
surgeries are also converted into open surgeries. Doing so with a fully
autonomous system could be challenging.

##  IV Components of Surgical Automation

  * •

Instruction engine - responsible for gathering instructions about the current
procedure to perform/ assist. The interface that can support this can be the
screen, gesture/button, and voice-based. Devices already exist and are used
today that are voice-activated (ViKY, Endocontrol, Grenoble, France), and it
is conceivable that NLP could eventually evolve to benefit the action of
voice-controlled devices during a procedure [21], [22]

  * •

Execution engine - Executing actions with precision after being told. Variety
of tasks to solve for - Suturing, Exploration, Manipulation, Incision,
Drainage, Cauterise, Pick and Place, Implant. This area has received recently
improved due to deep learning-based methods.

  * •

Planning and coordination engine - Create a standard plan to control a local
procedure to some or full extent, given ideal situations. Decides what to do,
when to do, and when to transition between sub-tasks. Works on a relatively
long time horizon.

  * •

Navigation engine - Extension to exploration tasks. Involves local navigation
to reach a site of interest. (e.g. mechanical thrombectomy. Adaptive human
anatomy understanding

  * •

Emergency engine - This allows the system to predict, detect, react and
control any emergency conditions. Doing so requires differentiating between
planned vs unplanned events, tolerable, unplanned, and unwanted events.
Overall, this requires looking at a longer horizon to predict such events but
also being able to act on them in a smaller time horizon.

  * •

Self-doubt engine- This acts as an internal safety system to raise flags about
what it can’t handle (“I don’t know this” system) and raises appropriate
warning/human override requests.

  * •

Observer engine - This will allow observer ability like humans, hence imitate
behaviors given adequate information and patient parameters. Allows
instruction fewer actions.

  * •

Innovator engine - This allows the system to innovate more efficient
approaches to perform previous tasks efficiently or level up to perform tasks
previously not feasible. This requires the system to have the ability to
measure what to improve and perform controlled testing/simulation.

It is important to note that while Fig. 4 separate components as separate
compartments, it is purely for representational ease. As we have seen in
previous technology cycles(audio detection systems), models get more
straightforward with minimal sub-components as our understanding of a task
improves. Our hope is to see more such examples in surgery automation.

Figure 4: Different components of automated surgical systems

##  V Challenges and area of improvements

Robotic surgical systems can help to increase OR autonomy through camera
control, application of intelligent instruments, and even accomplishment of
automated surgical procedures. Currently, the autonomy level of robots depends
on three factors: the complexity of the task, the environment in which the
robot operates, and the required level of human-robot interaction[23].

Automation of robotic surgery will inherently carry all the challenges posed
to robotic surgery - Namely - Higher cost, disposable instruments, and annual
service charges. Others include lack of haptics or tactile feedback, fixed
positioning of the operating table, and longer operative time compared to open
surgery. Transmission delay over long distances can be a significant challenge
for remote control.

Another important aspect is the adaption of the general technical OR
environment. This includes adaptive OR setting and context-adaptive
interfaces, automated tool arrangement, and optimal visualization.[5]
Integration of peri- and intraoperative data consisting of the electronic
patient record, OR documentation and logistics, medical imaging, and patient
surveillance data could increase autonomy.

Authors in [24] discuss the human-machine interaction perspective about
generic corporative robotic device profiles, features, and use cases. New
communication channels between surgeons and robotics have been tried, for
example, eye tracking and voice inputs. There are known frameworks for
evaluating training AI[25]. Since testing robotic surgery systems in the real
world is not feasible, testing frameworks are required to evaluate the
surgical techniques for robotic surgeons. Surgical simulations - allows for
faster experiments and testing ideas. Reinforcement learning has been used to
simulate and train agents to learn the task of surgical suturing [26] [27]
[28]. Authors in [29] used a percutaneous injection of a tumour mimic mixture
for experiments on renal tumour targets. More such innovations are required.

More detailed checkpoints are required to measure the accuracy metrics for
different surgery tasks[30]. What cannot be measured cannot be improved on. A
great deal of work in NLP has been possible because of a clear definition of
tests/benchmarks/datasets to measure the progress. The AI community needs to
agree on specific standards to evaluate such surgical systems that will allow
regulatory approval.

Finally, most work done is around Da Vinci type of robots, which have their
fair share of limitations. Recently there has been a broader adaptation for
Continuum robotics. Continuum robotics allow a higher degree of freedom [31]
[32] [33]. Reinforcement learning has shown great promise with self-
exploration, as it eliminates the data collection process. [26] [27] [28]
Additional work on explainability will act as a catalyst for the initial
adoption of such systems.

Few arguments have also been raised about whether automating the surgery
should be considered a goal and propose the term ”surgeon-in-the-loop”.[7]
Full surgical automation remains far in the future. As we pave our path
towards such systems, parallel work is required to look at ethical loopholes
and patient aspects. That being said, the enhancement of surgeons using
automation is essential.

Authors in [34] showed that performing robotic major liver resection without
the presence of a table-side surgeon is safe and feasible. This no-table-side
surgeon will be critical for full telesurgery and any possible automation.
This will also include switching instruments and patient monitoring. Trauma
Pods [20] is a deployable system for operative surgeries on battle/ military
wounded soldiers. This can allow acute critical stabilization and/or surgical
procedures, autonomously or in a tele-operative mode, on wounded soldiers on
the battlefield who might otherwise die before treatment in a combat hospital
could be provided. The authors also talk about the different mechanical
components required to make this happen. Lessons learned here can pave the
path for deeper hardware understanding for building deployable robotic surgeon
pods.

##  VI Conclusion

Surgical augmentation is happening. Several systems with level 2 and 3 level
automation have been developed. Although there is still a long journey ahead,
in this work, we propose a framework of surgical system components required to
rise higher in the level of automation. The importance of pre-operative and
post-operative efforts can’t be ignored, especially once we get closer to
level 4 or 5.

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