ROBOTS THAT CHANGES THE WORLD

How the Future of Surgery is Changing:

Robotics, telesurgery, surgical simulators and other advanced technologies

Introduction

Although many factors, such as economics, managed care and regulation contribute to the changing landscape in surgery, nothing causes such dramatic change as the introduction of a revolutionary technology. The change is so rapid in fact, that a new term, disruptive technology, is applied to signify such abrupt and radical change in a short time. The American baseball coach, Yogi Berra very cleverly anticipated such change when he said “The Future is not what it used to be!”. The implication of course, is that we cannot judge future changes by using contemporary standards. Laparoscopic surgery was the first of such technologies, and many others are soon to follow. Robotics is just emerging onto the scene, along with virtual reality for surgical simulation – and many others are in the laboratory today. The following is a review of the current status of robotics, telesurgery and surgical simulators as well as an introduction to numerous other new technologies that may significantly impact upon the practice of surgery. The rapid rate of such technological change is creating such extraordinary social, behavioral and ethical upheaval that speculation upon their profound effects is warranted.

Surgery in the Information Age.

Before examining specific technologies, it is important to provide the framework for a change of such magnitude. This radical change is a reflection of Information Age technologies, such as computers, robots and virtual reality. However there are some underlying principles that must be understood, since it is this “Information Age perspective” which not only drives the change, but binds everything together.

The first principle is that we can represent real objects in the real world with a computer or information equivalent (this is Nicholas Negroponte’s ‘Bits instead of atoms’ analogy¹) – for example, your body in the real world and a CT scan of your body in the information world. To this total body image is added all the vital signs, mechanics, physiology, etc of an individual person. The result is an information representation, a holographic medical electronic representation (or holomer) ², of a person which is a surrogate for the patient or person in information ‘space’ (computer), and which permits simulation upon the image before actually providing medical diagnosis or treatment on the person (see below).

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Future surgical ‘instruments’

Most surgical instruments are mechanical, although a few such as electrocoagulation, radiofrequency ablation, cryotherapy are available. This shows one new trend, which is to replace mechanical instruments with energy systems. An innovative new example is using ultrasound. Currently available is the Sonosite 180 diagnostic ultrasound system . Research is being conducted to add to the system, high intensity focused ultrasound (HIFU). When two identical ultrasound beams are focused to intersect each other, harmonic inter action occurs, which generates heat. Depending upon the frequency, power, etc, the heat can be generated to either coagulate tissue and blood or even to vaporize tissue. Clinical trials are being conducted in areas such as uterine fibroids, prostate cancer, benign breast lesions, and liver lesions. By combining HIFU on the diagnostic ultrasound, it will be possible to use the Doppler to diagnose a source of bleeding, focus the HIFU, and stop the bleeding – all from outside the body. Animal research has been successful in non-operatively stopping hemorrhage from femoral arteries and solid organs like liver, kidney and spleen⁹. It is likely that more and more energy directed instruments, such as lasers, radio frequency, cryotherapy, ultrasound, etc will be emerging which have both diagnostic and therapeutic possibilities, permitting the surgeon to make a diagnosis and then perform treatment simultaneously. Another instrument, called the “smart scalpel”, is in research with the military to create a laser scalpel which scans the tissues in front of the cutting laser to diagnose if there are blood vessels in the area, and if so, the laser is automatically shut off so as to not cut through major blood vessels. Next generation will attempt to distinguish normal from cancerous tissue to aid in oncologic surgery.

Education and training

Training in surgical skills and surgical certification has not changed much in centuries.However recently, aviation simulation technologies, which first begun in the 1950s and have become ultra-realistic), have transitioned to surgery made their first appearances in the 1990s in surgical simulation ¹¹. Using virtual reality  and the exponential growth of computers, progress has been rapid to a point where surgical simulation is becoming very realistic and even portable . Modern adult learning principles combined with new methodologies of objective assessment have brought simulation for surgical skills into the 21^st century. Sophisticated systems, such as the red dragon and blue dragon  provide accurate measurements of hand motions, forces, direction etc. This results in a quantifiable ‘signature’ of skills assessment which accurately distinguishes the performance of a novice from an expert and which provides quantifiable information on how to improve performance . With the ability to so accurately quantify performance, the next generation simulators will be incorporating the performance of experts as the benchmark criteria which students much achieve. Training programs are now changing from chronology (time) based training to proficiency-(criterion-)based training; the student no longer trains for a given time and then begins operating, instead the student continues training on the simulator until they achieve the benchmark ‘criteria’ of an expert before they operate upon their first patient. This dramatically decreases the amount of time a student will ‘practice’ on a patient. The Yale University study demonstrated that criterion-based training on a simulator can decrease operating time by 30% and decrease errors by 85%¹³.

Cellular Surgery (Biosurgery)

Beyond mesoscopic scale is the microscopic scale and individual cells. A new technology, called femto-second lasers (or ultra-short pulsed lasers) emit pulsed laser light at 1x10^-15. When directed at a cell membrane, it is possible to create a hole (incision) into the membrane without damage, providing access into the cell²⁵. Various researchers are beginning to manipulate the individual structures within the cell; and a group in Dundee, Scotland is actually entering the nucleus and manipulating chromosomes. One might speculate that in the future, surgeons will be using such systems to actually manipulate individual genetic material or perhaps directly operate upon genes. If this were to occur, the results of surgery would be to change the very biology of the cell (biosurgery), rather than trying to remove organs or restructure tissues²⁶. Such research is now conducted by controlling the position of the laser from a workstation. Interestingly, this is very similar to what surgeons are doing today with robotic surgery, the main difference is that the scale in cellular surgery is thousands of times smaller. In addition, the researchers are using other tools, such as atomic force microscope (AFM), to manipulate and visualize cells. These video monitors for the AFM show not only the outlines of the cells, but the actual forces between cells, giving researchers a whole new way of ‘seeing’ the function of a cell.

Actually, the Artic ground squirrels put themselves into a hypometabolic state, with vital signs that are radically reduced to a few percentage of normal. This occurs because some molecule in the hypothalamus is secreted – if the area in the hypothalamus is ablated and the ground squirrel is placed in the cold, it will freeze instead of hibernate. Also, if you put a normal ground squirrel in the desert surrounded with food, it still will hibernate. The signaling molecule from the hypothalamus is unknown, but it has been discovered that on the mitochondria where energy (ATP) is produced, a molecule blocks this site and oxygen cannot transfer its electrons to ATP to create energy. Mark Roth of Fred Hutchinson Cancer Center in Seattle has experimented in mice and been able to create such a block in mice such that they are put into a state of suspended animation for about 6 hours³⁵ – no respiration, heart rate, blood pressure, EKG, EEG, body temperature assumes ambient temperature and even no activitiy on functional

technology and then apply them with empathy and compassion for each patient. The following are some examples of technologies that pose extraordinary ethical challenges.

Human Cloning

In April 2002, an announcement was made public that the first human was impregnated with a clone , and 9 moths later the first human clone was born. The immediate response from all governments was to ban human cloning, however today there are at least 3 countries that support human cloning. The debate continues over human cloning (and stem cells) while science is left with conflicting messages, reduced funding and the threat of suffocating oversight. Who should decide what (or who) should be cloned? Should brainless clones be developed as spare parts? Do we really need to clone more people, what will happen to the human population? We have difficulty in feeding many people today.

Robots that change their world:

Inferring Goals from Semantic Knowledge

A growing body of literature shows that endowing

a mobile robot with semantic knowledge, and with the ability to

reason from this knowledge, can greatly increase its capabilities.

In this paper, we explore a novel use of semantic knowledge:

we encode information about how things should be, or norms,

to allow the robot to infer deviations from these norms and to

generate goals to correct these deviations. For instance, if a robot

has semantic knowledge that perishable items must be kept in a

refrigerator, and it observes a bottle of milk on a table, this robot

will generate the goal to bring that bottle into a refrigerator. Our

approach provides a mobile robot with a limited form of goal

autonomy: the ability to derive its own goals to pursue generic

aims. We illustrate our approach in a full mobile robot system

that integrates a semantic map, a knowledge representation and

reasoning system, a task planner, as well as standard perception

and navigation routines.

Index Terms—Semantic Maps, Mobile Robotics, Goal Generation,

Goal Autonomy, Knowledge Representation, Proactivity.

I. INTRODUCTION

Mobile robots intended for service and personal use are

being increasingly endowed with the ability to represent and

use semantic knowledge about the environment where they

operate [13]. This knowledge encodes general information

about the entities in the world and their relations, for instance:

that a kitchen is a type of room which typically contains a

refrigerator, a stove and a sink; that milk is a type of perishable

food; and that perishable food is stored in a refrigerator. Once

this knowledge is available to a robot, there are many ways in

which it can be exploited to better understand the environment

or plan actions [10], [18], [19], [21], [23], [25], assuming of

course that this knowledge is a faithful representation of the

properties of the environment. There is, however, an interesting

issue which has received less attention so far: what happens

if this knowledge turns out to be in conflict with the robot’s

observations?

Suppose for concreteness that the robot observes a milk

bottle laying on a table. This observation conflicts with the

semantic knowledge that milk is stored in a refrigerator. The

robot has three options to resolve this contradiction: (a) to

update its semantic knowledge base, e.g., by creating a new

subsclass of milk that is not perishable; (b) to question the

*Corresponding author. System Engineering and Automation Dpt. University

of M´alaga, Campus de Teatinos. E-29071 M´alaga, Spain. Email:

cipriano@ctima.uma.es.

This work has been partially supported by the Spanish Government under

contract DPI2008-03527.

validity of its perceptions, e.g., by looking for clues that

may indicate that the observed object is not a milk bottle;

or (c) to modify the environment, e.g., by bringing the milk

into a refrigerator. While some work have addressed the first

two options [6], [11], the last one has not received much

attention so far. Interestingly, the last option leverages an

unique capability of robots: the ability to modify the physical

environment. The goal of this paper is to investigate this

option.

We propose a framework in which a mobile robot can exploit

semantic knowledge to identify inconsistencies between

the observed state of the environment and a set of general,

declarative descriptions, or norms, and to generate goals to

modify the state of the environment in such a way that these

inconsistencies would disappear. When given to a planner,

these goals lead to action plans that can be executed by the

robot. This framework can be seen as a way to enable a

robot to proactively generate new goals, based on the overall

principle of maintaining the world consistent with the given

declarative knowledge. In this light, our framework contributes

to the robot’s goal autonomy. Although behavioral autonomy

has been widely addressed in the robotic arena by developing

deliberative architectures and robust algorithms for planning

and executing tasks under uncertainty, goal autonomy has

received less attention, being explored in the last years in

the theoretical field of multi-agents [4], [8] and implemented

through motivational architectures [1], [7].

Our framework relies on a hybrid semantic map, which

combines semantic knowledge based on description logics [2]

with traditional robot maps [11], [18], [21]. Semantic maps

have been already shown to increase the robot’s behavioral

autonomy, by improving their basic skills (planning, navigation,

localization, etc.) with deduction abilities. For instance,

if a robot is commanded to “fetch a milk bottle” but it ignores

the target location, it can deduce that milk is supposed to be  in fridges which, in turn, are located at kitchens. We now

extend our previous works on these issues [10], [11] to also

include partial goal autonomy through the proactive generation

of goals based on the robot’s internal semantic model.

More specifically, we consider a robot with the innate

objective of keeping its environment in good order with respect

to a given set of norms, encoded in a declarative way in

its internal semantic representation. Incoherences between the

sensed reality and the model, i.e., the observation of facts

that violate a particular norm, will lead to the generation

of the corresponding goal that, when planned and executed,

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will re-align the reality to the model, as in the milk bottle

example discussed above. It should be emphasized that in this

work we only focus on the goal inference mechanism: the

development of the required sensorial system, and the possible

use of semantic knowledge in that context, are beyond the

scope of this paper.