Uporaba Kirlianovih naprav v medicinski diagnostiki in zdravljenju
The use of Kirlian devices for medical diagnosis and treatment
Igor Kononenko and Tatjana Zrimec
Strojno učenje je primerno za induciranje diagnostičnih in prognostičnih pravil in reševanje specializiranih diagnostičnih in prognostičnih problemov. Medicinsko diagnostično znanje lahko avtomatsko zgeneriramo iz zapisov primerov iz preteklosti. Kljub ogromnemu napredku biomedicinske tehnologije v preteklosti pa je diagnostična točnost v mnogih primerih zelo nizka. Razlog je v tem, da medicinski instrumenti ne nudijo dovolj relevantne diagnostične informacije. Pred kratkim razvita tehnologija za zapisovanje človeškega elektromagnetnega polja z metodo vizualizacije ionizacijskega efekta (GDV) omogoča popolnoma novo informacijo o biofizičnem in psihičnem stanju pacienta in bi lahko v določenih primerih drastično izboljšala diagnostični proces. Ostaja pa problem interpretacije slik. S strojnim učenjem bi lahko rešili ta problem tako, da bi avtomatsko zgenerirali diagnostična pravila iz GDV slik pacientov z znanimi diagnozami. Dobili smo rusko napravo Crown-TV, ki omogoča rutinsko delo, in nameravamo razviti ekspertni sistem za diagnostiko iz GDV slik. Smo odprti za sodelovanje z raziskovalci na različnih področjih na skupnih projektih za preučevanje tega zanimivega področja.
Machine learning technology is well suited for the induction of diagnostic and prognostic rules and solving of small and specialized diagnostic and prognostic problems. The medical diagnostic knowledge can be automatically derived from the description of cases solved in the past. However, in spite of huge development of the biomedical technology, the diagnostic accuracy is in many cases rather low. The reason is that medical instruments do not provide enough relevant information for reliable diagnosis. Recently developed technology for recording the human bioelectromagnetic field using the Gas Discharge Visualisation (GDV) technique provide a completely new information about the biophysical and psychical state of the patient that could in some cases drastically improve the diagnostic process. However, the problem is the interpretation of the GDV images. By using machine learning we could alleviate that problem by means of automatically generating diagnostic rules from GDV images from the records of patients with known diagnoses. We have got the Russian Crown-TV Kirlian camera, we plan to develop an expert system for diagnosis from GDV images, and we are open for collaboration and welcome the researchers from various fields for joint projects on studying those exciting phenomena.
Machine learning technology (1,2) is well suited for the induction of diagnostic and prognostic rules and solving of small and specialized diagnostic and prognostic problems. Data about correct diagnoses/prognoses is often made available from archives of specialized hospitals and clinics, where the number of stored cases grows daily. All that has to be done is to type the data, i.e. the records of the patients with known correct diagnosis, into the computer in the appropriate form and run the learning algorithm. This is of course an oversimplification, but in principle, the medical diagnostic knowledge can be automatically derived from the description of cases solved in the past. The derived classifier can then be used either to assist the physician when diagnosing new patients in order to improve the diagnostic speed, accuracy and/or reliability, or to train the students or physicians non-specialists to diagnose the patients in some special diagnostic problem.
In several medical domains we actually applied machine learning algorithms (3,4,5,6). When applying a machine learning system in medical diagnosis there are several specific requirements that the system must meet, such as reliability and transparency of decisions. Typically, the automatically generated diagnostic rules achieve the same or slightly better diagnostic accuracy than physicians specialists.
In spite of huge developement of the biomedical technology, the diagnostic accuracy is in many cases rather low. The reason is that medical instruments do not provide enough relevant information for reliable diagnosis. Recently developed technology for recording human's bioelectromagnetic field by using the Gas Discharge Visualisation (GDV) technique (7,8) provide a completely new information about the biophysical and psychical state of the patient that could in some cases drastically improve the diagnostic process. However, the problem is the interpretation of the GDV images. By using machine learning we could alleviate that problem by means of automatically generating diagnostic rules from GDV images from the records of patients with known diagnoses.
2 Kirlian effect: a scientific tool for studying the human bioelectromagnetic field
All over the world various researchers study unusual phenomena, most of them under the name of parapsychology, and probably the most experienced researchers are in Russia (9). Most widely are studied telephaty, telekinetics, and extrasensory perception. Quite some people are known that are able to see the emanation flourescence of the humans and other biological objects, also known as aura (10). With the development of technology it has become possible to scientifically study some aspects of the emanation flourescence phenomenon, i.e. the bioelectromagnetic field (8).
The history of the so called Kirlian effect also known as the Gas Discharge Visualization (GDV) technique (a wider term that includes also some other techniques is bioelectrography) goes back to 1777 when G.C. Lihtenberg in Germany recorded electrographs of sliding discharge in dust created by static electricity and electric sparks. Later various researches contributed to the development of the technique (8): Nikola Tesla in USA, J.J. Narkiewich-Jodko in Russia, Pratt and Schlemmer in Prague, until the Russian technician Seymon D. Kirlian together with his wife Valentina noticed that through the interaction of electric currents and photograph plates, imprints of living organisms developed on the film. In 1970 hundreds of enthusiasts started to reproduce Kirlian photos. The research was until 1995 limited in using the photo-paper technique. In 1995 a new approach, based on the CCD Video techniques, and computer processing of data was developed by Korotkov (7,8) and his team in St. Petersburg, Russia. Their instrument, Crown-TV, can be routinely used which opens practical possibilities to study the effects of GDV.
The basic idea of GDV is to create an electromagnetic field using the generator of high voltage at high frequency. After a threshold voltage is exceeded the ionization of gas around the studied object takes place and, as a side effect, the quanta of light -- photons are emitted. So the discharge can be fixed optically by a photo, photo sensor or TV-camera. Various parameters influence the ionization process (8): gas properties (gas type, pressure, gas mixture), voltage parameters (amplitude, frequency, pulse waveform), electrodes' parameters (configuration, distance, dust and moisture, macro and micro defects, electromagnetic field configuration) and the studied object parameters (common impedance, physical fields, skin galvanic response, etc.). So the Kirlian effect is the result of mechanical, chemical, and electromagnetic processes, and field interactions. Gas discharge acts as a means of enhancing and visualization of super-weak processes.
Due to large number of parameters that influence the Kirlian effect it is very difficult or impossible to control them all, so in the development of discharge there is always an element of vagueness or stochastic. This is the reason why the technique was not yet widely accepted in practice as results did not have high reproducibility. All explanations of the Kirlian effect apprehended the fluorescence as the emanation of biological object. Due to low reproducibility, in academic circles there was a widely spread opinion that all observed phenomena are nothing else but the fluctuation of the corona discharge without any connection to the studied object. With modern technology the reproducibility became sufficient to enable serious scientific studies.
Besides studying non-living objects, such as water, various liquids (11), and minerals, the most widely studied are living organisms: plants (leafs, seeds, etc. (12,13)), animals (14), and of course humans. For humans, most widely are recorded GDV images of fingers (15,8), and GDV records of blood excerpts (16). Principal among these are studies of the psycho-physiological state and energy of the human being, diagnosis (17), reactions to some medicines (18,19), reactions to various substances, food (15), dental treatment (20), alternative healing treatment, such as accupuncture, 'bioenergy', homeopaty, various relaxation and massage techniques (21), GEM therapy (22), applied kineziology and flower essence treatment (23), leech therapy (8), etc.), and even studying the GDV images after death (7).
There are many studies currently going on all over the world and there is no doubt that the human bioelectromagnetic field, as vizualized using the GDV technique, is highly correlated to the human's psycho-physiological state, and can be used for diagnostics, prognostics, theraphy selection, and controlling the effects of the therapy.
3 Machine learning in medical diagnosis
This section discusses several issues related to the use of machine learning in medical diagnostic and prognostic problems. When applying a machine learning system in medical diagnosis there are several specific requirements that the system must meet. We discuss advantages and disadvantages of several different machine learning algorithms when used in medical diagnosis.
3.1 Machine learning
In recent years many different machine learning algorithms were developed (2,1). They can be classified into three major groups (24): statistical or pattern recognition methods (such as the K-nearest neighbors, discriminant analysis, and Bayesian classifiers), inductive learning of symbolic rules (such as top-down induction of decision trees, decision rules and induction of logic programs), and artificial neural networks (such as the multilayered feedforward neural network with backpropagation learning, the Kohonen's self-organizing network and the Hopfield's associative memory). However, not all the systems are equally appropriate. When applying a machine learning system in medical diagnosis there are several specific requirements that the system must meet.
3.2 Medical diagnosis
Typical diagnostic process is the following. During the interview of the patient the anamnestic data is obtained and immediately afterwards during the preliminary examination of the patient the physician records the status data. Depending on the anamnestic and the status data, the patient takes additional laboratory examinations. The diagnosis is then determined by the physician who takes into account the whole available description of the patient's state of health. Depending on the diagnosis the treatment is prescribed and after the treatment the whole process may be repeated. In each iteration the diagnosis may be confirmed, refined or rejected. The definition of the final diagnosis depends on the medical problem. In some problems the first diagnosis is also the final, in some others the final diagnosis is determined after the results of the treatment are available and in some problems there is no way to obtain the 100% reliable final diagnosis.
Medical diagnosis is known to be subjective and depends not only on the available data but also on the experience of the physician, his/her intuition and biases, and even on the psycho-physiological condition of the physician. Machine learning can be used to automatically derive diagnostic rules from description of the patients treated in the past for which the final diagnoses were verified. Automatically derived diagnostic knowledge may assist physicians to make the diagnostic process more objective and more reliable.
3.3 The performance
Typically, automatically generated diagnostic rules slightly outperform the diagnostic accuracy of physicians specialists when physicians have available exactly the same information as the machine. However, during the examination of the patient the physician often observes the patients condition in terms of the intuitive impressions which cannot be formally described and therefore cannot be typed in the computer. The lack of such information may be in some cases of crucial importance for the (in)ability to obtain the reliable diagnosis. The accuracy of physicians should therefore be considered as an estimate of how well the algorithms perform and not how badly the physicians diagnose. Although machine learning may derive more reliable diagnostic algorithms from the limited description of the patient, such diagnostic tools definitely cannot and also do not intend to replace the physicians but should be rather considered as helpful tools that can improve the physicians' performance.
4 Medical image processing
This section discusses various approaches to medical image processing. Many researchers have claimed that automatic processing of medical images can be successful only if model-based strategies are used in much the same way as radiologists do. We discuss the problems of medical imaging and we clarify why learning from images is necessary.
4.1 Main problems with medical image processing
Modern medical imaging technology offers a broad range of new ways to examine a patient in order to diagnose and to prescribe a proper treatment. In clinical practice today, physicians have access to various imaging devices and to the large amount of digital data. The challenge of diagnosis and proper pre-therapeutic planning is to identify relevant information from the large collections of often disparate images.
Medical treatment changes continuously toward less invasive therapeutic procedures. The evaluation of pathology relies on the inspection of various medical image data which provide a kind of geometric mapping of the patients anatomy. Various imaging modalities for measuring morphological and functional anatomical structures are available, for example imaging modality based on X-ray technology that supply 2D projections of the 3D patient anatomy, scanning devices based on tomographic techniques (CT, PET) or MRI (Magnetic Resonance Imaging) that provide a stack of cross sectional of volume data.
A number of studies have combined image processing with knowledge-based approaches in order to achieve better results in interpreting medical images (e.g. 25,26). These resulted in various expert systems for image processing and image understanding. In most expert systems, the knowledge mainly concerns how to effectively use image processing operators for image analysis (27). However, systems for image understanding, also known as model-based image processing, use object models to generate predictions during image analysis. These systems interpret images by finding instances of modelled objects and by predicting the existence of objects that have not been found but are known, according to the model, to be necessary for that scene. We use the same approach for analysing medical images, which are well suited for this type of analysis (28).
4.2 Why learning
Usually it is much easier to ask the radiologist to demonstrate the problem in an image and what to expect than to try to formalise their descriptions. Further, using examples of cases, they communicate more knowledge than if they are asked to provide general information. It is only possible to acquire such domain specific knowledge by learning from images which have been processed by experts (29). GDV imaging technique is able to record the bioelectromagnetic field distribution of human, providing a very comprehensive image of the functioning of the entire mind-body system (8). We are preparing experiments for investigating the human GDV images based on the Crown-TV equipment. The Crown-TV system uses the GDV technique.
4.3 CCD device and GDV images
CCD (charge-coupled detector) type of imaging device is very sensitive to direct exposer of x-rays as well as light, therefore CCDs have found wide application in a dental radiography in a vidicon system and recently in a system for measuring human bioelectromagnetic field in a computerised Kirlian equipment. CCD images that are used in the GDV technique, which is know as Kirlian effect, have a few good properties. Single frame can be stored in the computer and viewed continuously or a sequence of images can be produced and stored for further examination.
In order to follow the blood vessels, X-ray devices use protocol of producing temporal images. To be able to produce a clear imaged showing only the blood vessels a technique called Digital Subtraction Angiography (DSA) is used. In this technique the first image called mask is stored before injecting a contrast medium in the blood vessel. The difference of the first and the subsequent images, taken after the injection, outlines the blood vessels. Similar technique can be used in processing the sequence of images capturing the GDV images that reflect the state of a person. Paired images can be used to show differences of bioelectric field which changes with some mental activities.
An important part of image analysis is to formulate and describe visual information, such as what type of image features we can extract from the images, what properties those features are expected to have, and how they are related to each other. However, a lot of knowledge is required to interpret the human GDV images (8).
The image analysis of those images requires preprocessing, segmentation into meaningful image structures and transformation the structures into a suitable representation. It was found that the ability to read the bioelectromagnetic field distribution around fingers provides extremely efficient way of diagnosing problems and malfunctions of various organs and systems of the organism, long before physical symptoms become evident (8). Through long practice a considerable amount of domain knowledge has been collected for interpreting the GDV images. We propose to apply similar approach to GDV image processing as in our medical image understanding system i.e. to use the domain (expert) knowledge to guide the image processing.
5 Machine learning of medical diagnostic rules from GDV images
The procedure for using GDV images consists of image preprocessing to prepare the images for segmentation and image analysis. The preprocessing includes applying of different algorithms to improve the image quality or to enhance the image. Applying various filters and other algorithms for grey scale images will enable to extract and display the information patterns. Feature extraction process results in acquiring higher level image information such as shape or colour (different intensity) information. Pattern classification process uses this higher level information and identifies objects or patterns within the image. Next, a correspondence between the patterns in the GDV images and particular disorders has to be established. Pattern classification can be done manually or automaticaly by using the domain knowledge.
We will perform experiments on data obtained and verified by classical medical verification as well as obtained and verified with the help of an extrasense therapist. In both cases an expert will classify the patterns by attaching symbolic labels to patterns. In some cases a set of successive patterns can indicate a disease or some characteristic organisation of patterns in the image can strongly relate to some diagnose.
In medical imaging community recently new tools providing interactive communication between complex image data and the observer have been developed to assist the clinician and other experts to better delineate the pathology and to more accurately distinguish between normal and abnormal patterns. A similar tool for interactive communication between complex multi-layers GDV images and the expert can be developed. Using such tool the expert can easily postprocess the images and attach labels to the recognised regions which correspond or indicate a particular disease or can correct the segmented areas.
In the beginning of the experiments the classification will be done off-line on the printed images. Each image will be described with a set of attributes and a class. The set of attributes will contain values of the characteristic features (patterns) extracted from the image and some other attributes relevant for the image and the patient. The class value will be the diagnosis given by the expert.
The patient's GDV image will be recorded on his/her first visit to the physician specialist. After the classical diagnostic process and treatment is finished the verified diagnosis will be stored. The set of data from different patients with obtained image features and the verified diagnoses will serve as an input to the machine learning algorithm. After learning the automatically derived diagnostic rules will be verified on separate 'test' cases.
We propose to apply the techniques, knowledge and experience gained from the work in the domain of medical imaging to another kind of medical images, that of human GDV images. We have available different tools for classical image processing and tools specialised for processing of medical images. We will investigate and compare the results of different approaches to GDV image processing. The aim is to produce good and meaningful image analysis and to reveal more information encoded in the human GDV images. We will try also various machine learning algorithms in order to produce interpretations useful for manual diagnosing by non-experts and adequate for developing an automatic diagnostic system.
We have got the Russian Crown-TV and we plan to develop an expert system for diagnosis from GDV images using the machine learning techniques, which proved to be successful in classical medicine as addition tool for supporting the medical decision making (4). We are open for collaboration and welcome the researchers from various fields, such as physics, biology, chemistry, psychology, pharmacology, medicine etc. for joint projects on studying those exciting phenomena.
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