Application of bioinspired methods and means in medicine

Imprint

Alexander V. Yarovoy, Murat P. Deberdeev, Artem O. Chepi-ga, Ilya M. Borodyansky. Application of bioinspired methods and means in medicine. Cardiometry; Issue No. 29; November 2023; p. 62-66; DOI: 10.18137/cardiometry.2023.29.62-66; Available from:

Bioinspiration is a methodological approach that uses natural processes and phenomena with the aim of applying them in various fields, including medicine. The main idea of this approach is that living nature has already developed effective and optimal solutions for various problems, and science, in particular medicine, can use these solutions in its treatment methods. Bioinspired methods in medicine, based on evolutionary methods, as well as techniques using recommendation systems, represent an innovative approach to solving complex problems in diagnosis, rehabilitation and treatment. They allow us to use the principles and mechanisms developed by nature to create more effective and personalized methods in medical practice.[1]

A genetic algorithm is a bioinspired optimization method that uses genetic principles to find optimal solutions to various problems. Bioinspired techniques were developed in the 1960s and have since become widely used in various fields, including medicine. Recommender systems are algorithms that select relevant actions aimed at an object based on previously obtained data about such an object. This technology is considered as one of the sections of machine learning for medical equipment. In addition, recommendation systems in medicine are complexes of services and programs that compare patient tests and diagnoses made by doctors, and then, analyzing the tests of subsequent patients, try to predict their diagnosis and prescribe treatment for them.[2]

Bioinspired methods include various algorithms that are created by evolutionary biological processes and are viable. One of the most effective and promising bioinspired methods is the genetic algorithm. A genetic algorithm is a heuristic search algorithm, usually used to solve optimization and modeling problems by randomly selecting, combining and varying the required parameters, based on the principles of natural selection and genetics. It uses ideas drawn from the evolution of living organisms to solve complex optimization and search problems. In medicine, a genetic algorithm is applicable for diagnosis, treatment and rehabilitation.[3]

A genetic algorithm, together with recommendation systems, can be used to analyze medical data and identify hidden patterns that may lead to the occurrence of a certain disease. It allows for more accurate and consistent examination, which ensures ear- ly detection and more effective treatment of various pathologies. The method can be modified by the patient’s condition over time. It can adapt to new data and adjust recommendations according to changes in the patient’s condition. This is especially important in cases where the disease progresses or new symptoms appear.

The bioinspired method, together with recommendation systems, can also be used to select the optimal treatment for each patient. It allows you to analyze the patient’s genetic information and predict his body’s reactions to various medications. The dosage of medications is also optimized. This allows us to develop an individual approach to each patient, taking into account the characteristics of each patient’s body. Each body is unique, and what may be beneficial for one patient may be ineffective or even harmful for another. The bioinspired approach increases the effectiveness of treatment and reduces the risk of unwanted effects. By analyzing the genetic data of patients, a genetic algorithm allows us to identify the presence of genetic mutations associated with various diseases. It is especially useful in diagnosing diseases caused by genetic disease, such as hereditary cancers, heart disease, neu-rodegenerative diseases and others. The method can assess the development of disease severity and take appropriate measures and preventive measures. This allows for earlier examination and prevention of the development of diseases in the initial stages.[4-6]

Physical rehabilitation is an important element of the recovery process after injury or illness. It is aimed at restoring body functions, improving mobility and reducing pain. In recent years, bioinspired methods, including genetic algorithms, have become widely used in physical treatment, which has led to the emergence of new opportunities and prospects for effective recovery of patients.

One of the main bioinspired methods of physical rehabilitation used is the optimization of exercise and training programs. A genetic algorithm allows you to analyze a patient’s genetic material and determine its characteristics, such as a tendency to muscle activity or recovery rate. Based on this data, a genetic algorithm can create an optimal physical recovery program based on the patient’s capabilities. This allows for more effective results and shorter recovery times.[7]

Another method of applying bioinspired methods in physical rehabilitation is the use of recommendation systems to optimize physical activity parameters.

The patient’s condition, physical structure and exercise status are analyzed to determine optimal training parameters such as condition, duration and frequency. This allows you to increase the effectiveness of your training and minimize the risk of re-injury or over-work.[8]

In addition, bioinspired methods, including genetic algorithms, can be used to customize medical devices and prosthetics. A genetic algorithm can analyze data about the failure and function of a patient’s organs or limbs, as well as their genetic information, to determine the optimal parameters for devices and prostheses.[9]

One of the important areas of possible application of a genetic algorithm in medicine is the processing of medical data. Medical data mining is the process of analyzing and calculating large amounts of information obtained from various sources, such as patient medical records, laboratory results, images, genetic data, etc. The purpose of processing medical data is to identify patterns, trends and relationships that can help in diagnosing, predicting pathologies and eliminating their consequences.[10]

Genetic algorithm can be used to process medical data to optimize various tasks such as classification, clustering, prediction and prudent treatment selection. One example of the application of the genetic method in the processing of medical data is the problem of classifying disorders. GA can be used to determine optimal sets of features that may be most informative for distinguishing between different classes of diseases. This allows the creation of a classification model that can automatically determine the type of disease based on input data.[11]

The bioinspired approach can be used to develop new drugs. It allows you to virtually screen molecules and change their structure and properties using genetic transformations. This allows you to speed up the process of developing new drugs and reduce the costs of their creation.[12]

Another application of the bioinspired approach to medicine is its use in patients at the rehabilitation stage. Genetic algorithms and recommendation systems can help improve physical and psychological rehabilitation after injury or surgery. They allow us to provide an adaptive program of assistance to the patient, taking into account his genetic characteristics and parameters. This ensures faster and more efficient recovery for the patient.[13]

The molecular biological diagnostic method allows you to obtain information about the presence of a disease before symptoms appear. It can be used to determine asthma, lung and prostate cancer, and osteoporosis. Diagnostics shows high efficiency in the field of oncology. Biomolecular diagnostics is one of the fields of medicine that allows us to determine the presence or absence of certain pathological changes or mutations in the future. A genetic algorithm, in turn, is a computational method that can be used to optimize the molecular diagnostic process. [14,15]

One of the main advantages of a genetic algorithm in biomolecular diagnostics is its ability to process large volumes of genetic information and find optimal solutions to complex problems. Unlike the “human” method, the genetic method can analyze huge amounts of genetic data and identify hidden patterns and relationships between genes and diseases. This improves the accuracy and efficiency of the biomolec-ular detector, which is of great importance for early research and treatment of various diseases.[16,17]

A genetic algorithm can be used in biomolecular diagnostics for a variety of tasks, such as finding optimal sets of genetic markers, classifying patients based on genetic data, predicting disease risk, and identifying the most effective treatments. To do this, a genetic algorithm uses the principles of natural selection, mutation and crossbreeding to create new genetic traits and evaluate their effectiveness in periodic biomolec-ular diagnostics. [18-20]

One example of the use of a genetic algorithm in molecular diagnostics is the search for optimal sets of genetic markers for diagnosing a disease. The genetic method can analyze large amounts of genetic data and identify the most informative and discriminatory genetic markers that can be used to accurately diagnose the disease. This allows you to reduce the time and costs of biomolecular diagnostics and increase its accuracy. [21-23]

The implementation of a genetic algorithm in medicine is a complex and multifaceted process that faces a number of challenges and factors. Despite the potential benefits and prospects, there are challenges and limitations to its implementation.

Ethical and legal issues . One of the main issues concerning the development of genetic methods in medicine are ethical and legal issues. One of the main legal aspects is the issue of access to patient medical data. How to ensure that genetic information is stored, 64 | Cardiometry | Issue 29. November 2023

used safely and securely? How can we ensure patient confidentiality and prevent possible misuse of this information? These issues require serious discussion and the development of appropriate legal and ethical standards.[24,25]

Technical limitations . The introduction of genetic methods in medicine also faces technical limitations. Effective operation of a genetic algorithm requires large amounts of computing resources and high-speed systems, and the results obtained using bioinspired algorithms can be complex to analyze and require additional analysis by specialists. In addition, not all medical institutions and laboratories provide services for the implementation of such systems. [26]

Lack of qualified experts . Another challenge associated with the introduction of bioinspired methods in medicine is the lack of qualified experts. Designing and implementing a genetic algorithm requires expertise in genetics, bioinformatics, and programming. However, there is currently a shortage of professionals with the skills and customization skills. This may slow down the process of development of the genetic method in medicine and requires additional advances in the education and training of experts.

Complexity and unpredictability of biological systems. Biological systems are complex and unpredictable. The introduction of bioinspired methods into medicine requires understanding and studying these systems. However, due to the complexity and unpredictability of biological processes, developing effective genetic methods may be more challenging. More research and experimentation is needed to determine the optimal parameters and strategies for genetic algorithm operation in specific medical problems.[27]

Resistance and rejection from the medical community. The introduction of new technologies and methods in medicine always causes resistance and rejection from some men in the medical community. Doctors and specialists may be skeptical about some genetic methods and question their effectiveness and safety. This may hamper the process of development of the genetic method in medicine and requires development and information trends to overcome this obstacle.

Financial restrictions. The introduction of genetic methods in medicine also faces limitations. The development and implementation of a genetic algorithm requires significant financial investments. Not all medical institutions and organizations can allocate sufficient funds to ensure health and support the ge- netic method. This may pose a problem for its widespread use in medical practice.

The need for research and medical testing. To achieve the results of bioinspired methods in medicine, more research and medical trials are needed. This requires time, resources and cooperation between various underlying institutions and organizations. The effectiveness and safety of bioinspired techniques in a variety of medical process settings needs to be verified before they are widely used.[28]

In conclusion, it is worth noting that despite the limitations, the use of bioinspired methods and means in medicine has a number of advantages over conservative methods. This approach makes it possible to analyze large volumes of patient data and identify complex relationships that may be invisible to the doctor. Recommender systems are trained to work in conditions of uncertainty and fuzziness, which makes them more flexible and adaptive. A genetic algorithm can be automated and executed on a computer without direct human intervention. This allows you to reduce the time spent on data analysis and decision making. For a person who may be limited by their physical and cognitive abilities, the genetic method can work much faster and more efficiently. Bioinspired methods can take into account a disproportionately larger number of medical factors, which allows for more accurate and reliable results.