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Neuro-fuzzy Systems for Patient Treatments

Authors:
  • Visoka tehnička škola strukovnih studija, Serbia, Subotica
  • Subotica Tech - College of Applied Sciences Subotica

Abstract and Figures

Neuro-fuzzy systems are widely used in both applied and experimental medicine and are one of the most modern subjects of today's Medical Informatics. Despite the initial refuse of their use due to informatical, economical, educational and other reasons, these systems are widely accepted in medical institutions operating in all levels of healthcare. The accuracy of some up-to-date neuro-fuzzy systems today matches or even surpasses the diagnostic abilities of physicians, thus holding an important role in risk-assessment and diagnostics in medicine. This work deals with newer applications of these systems in medicine along with the possibility of their implementation in the region of Central Europe.
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1st Regional Conference - Mechatronics in Practice and Education MECH - CONF 2011
Neuro-fuzzy Systems for Patient Treatments
Andor Sagi*, Anita Sabo**, Bojan Kuljić***, Tibor Szakáll****
Subotica Tech, Subotica, Serbia
peva@vts.su.ac.rs, saboanita@vts.su.ac.rs,
bojan.kuljic@gmail.com , szakall.tibor@gmail.com
Abstract— Neuro-fuzzy systems are widely used in both applied and experimental medicine and are one of
the most modern subjects of today’s Medical Informatics. Despite the initial refuse of their use due to
informatical, economical, educational and other reasons, these systems are widely accepted in medical
institutions operating in all levels of healthcare. The accuracy of some up-to-date neuro-fuzzy systems
today matches or even surpasses the diagnostic abilities of physicians, thus holding an important role in
risk-assessment and diagnostics in medicine. This work deals with newer applications of these systems in
medicine along with the possibility of their implementation in the region of Central Europe.
Keywords: neuro-fuzzy systems, medicine, patient treatment
I. NEURO-FUZZY SYSTEMS IN MEDICAL SCIENCES
Medical diagnostics, despite the wide use of international Latin nomenclature and disease
classification systems, is a field of evaluation of vague, human-like categories. Risk
assessment requires high degree modeling, but on the same time must provide the necessary
environment of interaction with the human factor.
To establish objective quality control the process must be monitored by a multi-level
feedback system operated by a team of experts preferably of medical informatics, social
medicine, epidemiology and by a committee for verifying the accuracy of the established
diagnoses. These factors are important not solely for the purpose of satisfying the principles of
Good Clinical Practice (GCP), but also the terms defined by social medicine and the country`s
main development program.
These systems will need to be changeable as clinical considerations may alter the existing
categories. The frequent lowering of tolerance levels for LDL cholesterol provides us with
excellent example. These changes are often unforeseen in medical sciences so the speed of
changing these systems will come to foreground. In the previous example the interaction of
business interest of antihyperlipemic drug manufacturers and changing medical aims – among
others – resulted in frequent changes of these values.
Figure 1. Neuro-fuzzy information feedback system
II. THE ROLE OF NEURO-FUZZY SYSTEMS IN MEDICAL DIAGNOSTICS
Neuro-fuzzy systems, among other requirements, have to meet not only the standards and
expectations of physicians, but their patients as well. An ample example for this is the use of
neuro-fuzzy systems by a group of prominent scientists of the Annamalai Univerity. Their
work, published in 2005 is titled “An investigation of neuro-fuzzy systems in psychosomatic
disorders” in which they state the possibility of interpreting the vague, human-like symptoms
of the hospitalized as well as appropriately diagnose their psychic condition. I might recollect
that this is one of the most ambitious examples of the efficacy of neuro-fuzzy systems, since it
was able to validly simulate the understanding of complex pathophysiological laws to the
extent of valid diagnoses with its effectiveness approved physicians. It is for the first time that
a system that is based on artificial neural networks actually used directly for the evaluation of
function of a real neural network.
1st Regional Conference - Mechatronics in Practice and Education MECH - CONF 2011
Mental diseases and psychosomatic disorders are closely determined with the structural
architecture and the conductive abilities of this biological network. In the age of examining the
human brain the simulation of such a system becomes imperative as medical ethics greatly
hinders the possibilities of legal experiments on humans. The electric charge in the neurons is
generated by a sodium influx and potassium efflux which depolarizes the cell. Complex,
additional biochemical reactions make possible the flow of this current. The speed of current
propagation as well as its strength and other properties are now clearly defined, opening the
path for making appropriate models. Current conduction by chemical synapses and the
properties of the certain neurotransmitters is also a subject of knowledge. The number of
possible connections if billion times billion, but the histological and neurological properties of
the human brain still provide ample ground for modeling purposes. Many mental disorders are
mostly due to abnormal production or lack of certain neurotransmitters or their quantitive
proportions. In these examples no higher structural analysis is required. On the other hand in
epilepsy the abnormal excitation of pathologic neuron groups provide the clinical picture.
Since these phenomena can be measured by EEG the neuro-fuzzy system can be filled up with
data intervals necessary to classify vague signals. Epilepsy can be triggered with many external
influences which patients can describe only subjectively and therefore not precisely. These
effects could be intense or rapidly changing visual or auditive impulses.
The role of simulation becomes evident in the so-called surgical simulators too. Surgeons
use these simulators to practice new surgical methods before actually applying them on
patients. Apart from realistic 3d modeling they also need to recreate the feel and consequences
of manipulating with elastic tissue. The tissue`s level of elasticity was often left without
simulation creating an unreal practicing environment. In the modern surgical simulators the
modeling and realization is achieved using neuro-fuzzy systems. The Institute for Medical
Informatics, Technical University of Braunschweig, and Institute for Knowledge and
Language Processing, University of Magdeburg together developed the plan for such a sytem
[2,1998].
Figure 2. Surgical simulator: virtual laparoscopy
It is also desirable that the modeling respects at least the main histological layers of these
organs, required for example at microsurgical or some gynecologic or otorhinolaryngological
operations. During these procedures the physician is using a sort of microscope and usually
interferes with the integrity of very thin histological layers, leaving more healthy tissue intact.
The evolution of these multi-layer systems is at the stage of planning, but with the speed of
evolution of this branch of medical informatics it is considered soon to be realized. There is on
the other hand some concern that these simulations require more expensive computers. The
most important and the most common aspects of possible application of neuro-fuzzy systems
are in primary health care.
Besides the anamnesis and physical examination, laboratory diagnostics is usually among
the most often diagnostic method in the inventory of a general practitioner, but is essential part
of any preventive branch of medicine. The anamnesis and physical examination is written in
the patient’s records and their interpretation and control is relatively objective. The laboratory
1st Regional Conference - Mechatronics in Practice and Education MECH - CONF 2011
results on the contrary only contain the measured value and the intervals in which these values
are to be regarded as physiological. The patient is deprived from any insight of his condition,
while doctors can also interpret them sometimes too deliberately (since often there are many
proposed interpretations in medical literature). Nevertheless this system cannot be also
effectively integrated in a bigger, modern neuro-fuzzy based one for diagnostics or risk
assessment.
A possible solution was provided for numerous values by the J.W. Goethe University of
Frankfurt [5,2000]. Among others fuzzy categories where defined for the following: mean
corpuscular erythrocyte volume, alkalic phosphatase, glutamat-pyruvat-transaminase in serum,
glutamat-oxalacetat-transaminase in serum gammaglutamyltranspeptidase, and for the number
of daily consumed alcohol portions. The significance of this research is pointed out in the
Proceedings of European Symposium on Artificial Neural Networks, ESANN 2000, Elsevier
Publ., 2000, pp. 201-206 with these words: “The decision and diagnostic power of neural
networks are needed in many tasks of everyday life. Successful example applications show that
especially in medicine human diagnosis abilities are significantly worse than those of neural
diagnosis systems.“
The electrocardiogram is also a common device in diagnostics and cardiovascular risk
evaluation in all levels of healthcare. The proper interpretation of the electrocardiogram is time
consuming for every physician since there are 12 leads to consider, not to mention their
comparison, becoming impractical for many branches of healthcare like intensive care units,
coronary care units, ambulance or any physician providing life support. Many successful
attempts have been made for that matter to construct an ECG device which also establishes a
diagnose. These ECG devices are using fuzzy logic and are relatively common in Serbia. The
disadvantage of these systems is their reliability which varies from manufacturer to
manufacturer, and therefore cannot be trusted by physicians. The great responsibility of
administrating the proper drug in certain acute situations and the general flaws of these devices
prompt doctors toward manual diagnosis making regardless of the situation. This is one of the
compelling reasons why diagnosing ECG devices are not significantly preferred to the ones
who do not use fuzzy logic.
Many groups of authors recently published works independently on the possibilities of
applying neuro-fuzzy systems to enhance the capabilities of artificial ECG interpretation. It is
encouraging that these studies are conducted even in this region like in the Dept. of Applied
Electronics and Information Engineering, Polytechnic University, Bucharest, Romania
[6,2003]; providing possibilities for Serbian scientists possibilities of collaboration in this field.
Romania published its results under the title: “A Neuro-Fuzzy Approach to Classification of
ECG Signals for Ischemic Heart Disease Diagnosis”. They state that the rate of correctness for
ECG interpretation for Ischemic Heart Diseases is already 100% in the examined number of
patients.
In 2007 a group of scientists from various Chinese universities discussed the possibility of
describing complex hormonal regulation mechanisms using the neuro-fuzzy technology.
Endocrinology is a particularly complex branch of medicine, since the adjustment of the
desired hormonal status is the function of many organ systems, the brain, the numerous
endocrine glands, blood hormone levels, the concentration of sodium in the blood or
osteopoesis are only a few from the various interacting elements.
A typical neuroendocrine mechanism can be described with the following generalized
algorithm, the knowledge of which is the basis of making efficient neuro-fuzzy systems in
endocrinology. The hypothalamus produces releasing hormones which are transported to the
glandula pinealis. The glandula pinealis releases a so called endocrine gland stimulating
hormone. This stimulating hormone influences gland cell behavior and biochemical processes,
allowing the gland to produce the desired product. The so produced hormones are directly
injected in the blood flow and travel to the target cells.
In certain diseases the hormone could get blocked while residing in the blood flow. The
presence of certain receptors on the target cells are usually microscopically connected with
certain proteins and/or induce biochemical chain reactions altering the host’s metabolism. The
presence, absence or quality of these receptors is the main factor in many widely common
diseases such as Diabetes type II, from which approximately 8% of the population suffers.
1st Regional Conference - Mechatronics in Practice and Education MECH - CONF 2011
There are many feedback mechanisms that inform the various members of hormonal
homeostasis of the hormonal status, lowering or otherwise adjusting their function. All of this
is underlined with a firm histological basis, but the scope of this discussion doesn`t permit
further discussion on it, nevertheless the presented system is also sufficiently applicable for
modeling. Frequently tumors of the neuroendocrine organs alter this fine-tuned regulation
profoundly, which – due to the high prevalence of these neoplasms - is also should be part of a
more sensitive neuro-fuzzy based simulation.
Neuro-fuzzy systems are more and more accepted in interpreting the results of
ophthalmological examination. Since ophthalmology a largely visual branch of medical
science, various interpretations and judgments are possible for certain conditions. The
universities of Madrid and Tenerife have worked together to make a neuro-fuzzy system for
automatic interpretation of the visual field [3, 2001]. The scientists stated that it achieved high
accuracy and was generally able to substitute the physician in the examined cases. Similarly to
these examples, almost every medical field in principle that requires any sort of interpretations
of vague, human-like categories is open for introducing neuro-fuzzy interpretators.
There is a vast field of possibilities in introducing these systems in the process of physical
therapy and rehabilitation of patients. As an example of such a use of neuro-fuzzy system a
particular experiment will be demonstrated. Scientists from the University of Malaya tried to
objectively express the strength of human grip as a means of a more precise evaluation of the
progress of rehabilitation as well as neural damage [1, 2006]. Test results clearly showed that
an Adaptive Network-based Fuzzy Interference System is “a robust and adaptive tool to
recognize human hand grip strength patterns. “. This system has also an immensely accurate
tool for diagnostic purposes, mainly diseases of the musculoskeletal system, for example
almost all the forms of myopathias. The professors are constantly improving the recognition
accuracy; it is currently about 85%. This solution is also a valid tool for post-operative
monitoring and healthcare. It is also important that the signals are “calibrated, filtered and
stored in a database for retrieval and querying purposes.”, and that these data are presented in
an acceptable way to the physician.
The strength of the hand grip is detected using strain gauges, and then an electrical is
produced, registered by the Labview software. Then it is transferred to the data classification
unit. Personal computers enable practical storage of the data, thus creating a comprehensive
patient record and enables comparison – so monitoring the effectiveness of treatment in the
function of time. A brief schematic block diagram of this computational hand grip assessment
tool is given below. I leave out the particular technical details, because there are other
prominent projects for the evaluation of human hand grip strength, which achieve similar
results by using different specifications.
Figure 3. Schematic block diagram of the computational hand grip assessment tool
III. CHALLENGES AND FRONTIERS OF NEURO-FUZZY SYSTEMS
The hand grip assessment tool`s significance is clear from the fact that many authors on the
application of neuro-fuzzy systems agree that this point of communication is one of the most
problematic and controversial in these systems. This problem lies mostly in the complexness of
medical, international Latin terminology and language which can be hardly generated by
artificial intelligence. The debates were mostly centered about what the nature and detail of the
presentation of the collected data should be. Artificial intelligence often presented these data
by a not precise computer generated language or in a way that was understandable only for the
1st Regional Conference - Mechatronics in Practice and Education MECH - CONF 2011
technicians, therefore not practical. This was one of the main reasons of refusal by the
professional healthcare staff.
One of the most comprehensive medical textbooks on the subject of neuro-fuzzy systems
nowadays is “Fuzzy and Neuro Fuzzy Systems in Medicine” edited by Horia-Nicolai
Teodorescu et al. In this textbook, apart of defining the current challenges and status of this
technology, the author also presents us case-studies based on medical experiments [4,1991]. In
the following lines I mention some of the biggest and most ambitious projects besides the ones
I already elaborated in this work. Though their mention is more due to a simple list, it still
validly represents in which branches of medical science was the adaptation of neuro-fuzzy
systems was most wide.
There was, as I mentioned before, many attempts to understand the brain as a fuzzy system
and modeling its functions; identifying its state or forecasting acute pathology. Brain surgery
was performed and segmentation of neural tumors this way. Many experiments were
conducted on diagnosis and treatment for diseases of the myocardium (mostly for ischemic
heart disease) or blood pool. The establishment of an expert system in intensive care
diagnostics (for addressing conditions such as heart failure or shock) has reported to be
effective in lowering patient mortality. Many practical methods are proposed for this purpose,
but according to Teodorescu they are of ”comparable complexity and have similar
performances”.
Analyzing EEG signals in neurology is now possible using various technical solutions: time
frequency analysis, multiscale decomposition by the fast wavelet transform, multichannel
model based and decomposition by matching pursuit. Their classification is usually conducted
by one of the following methods: by an unsupervised optimal fuzzy clustering algorithm or the
weighted fuzzy k-means algorithm.
Combination of neuro-fuzzy systems with wavelet analysis brought many possibilities to
exist among various branches of medical imaging technology. SPECT of the ventricular
myocardium is one of the most stunning examples of successful practical implementation of
this combination. MR image analysis is also a prominent example for the use of neuro-fuzzy
systems in medical imaging, tested for unsupervised brain tumor segmentation and labeling.
The edge of the tumor mass could be determined more precisely then with other pre-existent
method, allowing more precise data for surgical purposes. This system is tuned by the most
possible accuracy (since it is for surgical purposes) on two levels, similar to other neuro-fuzzy
systems: on the level of physicians and on the fuzzy categories which is also called “fine
tuning”.
So far we have discussed strictly the medical applications of neuro-fuzzy systems, although
it is interesting that stomatology also began to benefit from them. Stomatology is closely
interwoven with a branch of medical science, maxillofacial surgery and therefore its worth to
mention that occlusion analysis and analysis of masticatory function is already realized using
this technology. Besides maxillofacial surgery, a pediatric advantage is defined, namely the
reducing of the necessary amount of X-ray examination of children, that way lowering the
accumulation of radiation.
Hemodynamic control during anesthesia by a neuro-fuzzy system has proved to be a
problem which solution is among the future challenges of medical informatics. High level of
uncertainty, the complexity of control and constantly changing physiological parameters did
not allow successful results.
While many attempts have been made to design a system that could work without medical
supervision, these systems prove themselves not stable and still have to be controlled by
physicians.
CONCLUSION
Despite promising results, the use of neuro-fuzzy systems in medicine still faces certain
general problems regardless of the field of medical science. It is already mentioned that many
of these systems (surgical simulators for example) require more sophisticated and therefore
more expensive hardware, therefore must be subject to evaluation by social medicine. The use
of these systems provide many, but different advantages on all three levels of healthcare, as
1st Regional Conference - Mechatronics in Practice and Education MECH - CONF 2011
well as for those institutions operating on multiple levels, therefore it must be controlled by the
countries healthcare development program mainly from economical perspective.
Hardware minimization or optimization between architecture and design of these devices
are also listed among the most discussed problems. Most authors agree that the main
constrains of investigation of the effectiveness of a neuro-fuzzy system is evaluation of the
systems autonomy, reliability and the precision of computation.
REFERENCES
[1] A. Hafiz, S. Anandan, T. Kamarul “Recognition of Human Grip Strength Patterns Using Neuro Fuzzy
Technique “ Proceedings of the Third International Conference on Artificial Intelligence in Engineering &
Technology November 22-24, 2006, Kota Kinabalu, Sabah, Malaysia.
[2] A. Radetzkya, A. Nürnbergerb, D. P. Pretschnera “The simulation of elastic tissues in virtual medicine using
neuro-fuzzy systems”, SPIE, Bellingham WA, ETATS-UNIS, 1998.
[3] E. Carmona, J. Mira, J. G. Feijoo, M. G. de la Rosa “Neuro-Fuzzy Nets in Medical Diagnosis: The
DIAGEN Case Study of Glaucoma” Proceedings of the 6th International Work-Conference on Artificial and
Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II ,pp. 401 – 409, 2001.
[4] H N Teodorescu, Horia Nicolai L Teodorescu, Abraham Kandel “Fuzzy and Neuro-Fuzzy Systems in
Medicine”, CRC Press, 1991.
[5] R. Brause, F. Friedrich “A Neuro-Fuzzy Approach as Medical Diagnostic Interface” Proc. European
Symposium on Artificial Neural Networks ESANN 2000, Elsevier Publ., 2000, pp. 201-206.
[6] VE Neagoe, IF Iatan, S. Grunwald “A neuro-fuzzy approach to classification of ECG signals for ischemic
heart disease diagnosis.” AMIA Annu Symp Proc. 2003:494-8.
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Recognition of Human Grip Strength Patterns Using Neuro Fuzzy Technique
  • A Hafiz
  • S Anandan
  • T Kamarul
A. Hafiz, S. Anandan, T. Kamarul "Recognition of Human Grip Strength Patterns Using Neuro Fuzzy Technique " Proceedings of the Third International Conference on Artificial Intelligence in Engineering & Technology November 22-24, 2006, Kota Kinabalu, Sabah, Malaysia.
The simulation of elastic tissues in virtual medicine using neuro-fuzzy systems
  • A Radetzkya
  • A Nürnbergerb
  • D P Pretschnera
A. Radetzkya, A. Nürnbergerb, D. P. Pretschnera " The simulation of elastic tissues in virtual medicine using neuro-fuzzy systems ", SPIE, Bellingham WA, ETATS-UNIS, 1998.
Fuzzy and Neuro-Fuzzy Systems in Medicine
  • Horia H N Teodorescu
  • L Nicolai
  • Abraham Teodorescu
  • Kandel
H N Teodorescu, Horia Nicolai L Teodorescu, Abraham Kandel "Fuzzy and Neuro-Fuzzy Systems in Medicine", CRC Press, 1991.