Развитие технологий искусственного интеллекта в онкологии и лучевой диагностике

Introduction. The widespread adoption of Artificial Intelligence (AI) technologies forms the core of the so-called Industrial Revolution 4.0.The aim of this study is to examine qualitative changes occurring over the last two years in the development of AI through an examination of trends in PubMed publications.Materials. All abstracts with keyword “artificial intelligence” were downloaded from PubMed database https://www.ncbi.nlm.nih.gov/pubmed/ in the form of .txt files. In order to produce a generalisation of topics, we classified present applications of AI in medicine. To this end, 78,420 abstracts, 5558 reviews, 304 randomised controlled trials, 247 multicentre studies and 4137 other publication types were extracted. (Figure 1). Next, the typical applications were classified.Results. Interest in the topic of AI in publications indexed in the PubMed library is increasing according to general innovation development principles. Along with English publications, the number of non-English publications continued to increase until 2018, represented especially by Chinese, German and French languages. By 2018, the number of non-English publications had started to decrease in favour of English publications. Implementations of AI are already being adopted in contemporary practice. Thus, AI tools have moved out of the theoretical realm to find mainstream application.Conclusions. Tools for machine learning have become widely available to working scientists over the last two years. Since this includes FDA-approved tools for general clinical practice, the change not only affects to researchers but also clinical practitioners. Medical imaging and analysis applications already approved for the most part demonstrate comparable accuracy with the human specialist. A classification of developed AI applications is presented in the article.Введение. Индустриальная революция 4.0 произошла во многом благодаря внедрению методов искусственного интеллекта.Цель исследования. Показать качественные перемены, которые произошли в последние 3 года в реализации методов искусственного интеллекта в здравоохранении путем исследования трендов по публикациям в базе данных PubMed.Материалы. Все резюме статей с ключевым словом “artificial intelligence” были загружены в текстовые файлы из базы данных https://www.ncbi.nlm.nih.gov/pubmed/. Путем обобщения ключевых слов мы классифицировали современные применения искусственного интеллекта в медицине. 78 420 резюме были извлечены из базы данных PubMed, в том числе 5558 обзоров, 304 рандомизированных исследования, 247 многоцентровых исследований. Затем были классифицированы типичные сферы применения.Результаты. Интерес к теме искусственного интеллекта в индексированных в PubMed публикациях растет согласно закону развития инноваций. Количество неанглоязычных публикаций увеличивалось до 2008 года и было представлено на китайском, немецком, французском и русском языках. После 2008 года количество неанглоязычных публикаций снизилось в пользу англоязычных.Выводы. В последние два-три года искусственный интеллект в практике принятия решений в медицине стал иметь реальное практическое применение. Кроме того, инструменты для создания систем принятия решений на базе методик искусственного интеллекта стали уже не диковинными и доступны не только математикам. Американское управление пищевыми продуктами и лекарственными препаратами (FDA) одобрило ряд приложений к клинической практике. Это еще одна перемена, которая затронула не только ученых, но и практиков. Большинство таких приложений используется для анализа медицинских изображений, в том числе и в онкологии, и демонстрирует сравнимую точность с человеком специалистом. В статье представлена разработанная классификация применения технологий искусственного интеллекта

Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

PID control of depth of hypnosis in anesthesia for propofol and remifentanil coadministration

Tese de mestrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasThe purpose of general anesthesia is to deeply sedate a person so that they lose consciousness, sensitivity, and body reflexes, and so that surgeries can be safely performed without the patient feeling pain or discomfort during the procedure. General anesthesia is a combination of the effect of three components, namely hypnosis, analgesia, and neuromuscular blockade. Each component is regulated through the action of a specific drug, or through the combined effect of two or more drugs. In recent years there have been many advances in the field of automatic control systems for drug delivery during anesthesia, which can be implemented using a wide variety of controllers and process variables. The reason behind these advances is that an automatic control system can provide several benefits, such as a reduction in the anesthesiologist's workload, a reduction in the amount of medication used (which implies a faster and better recovery time for the patient in the postoperative phase), and, in fact, a more robust performance with fewer episodes of over- or under-dosing of the drug. A proportional-integral-derivative controller (PID) continuously calculates the error value that is the difference between the desired value and the measured process variable and applies a correction that is based on proportional, integral and derivative terms. In this dissertation, a specific PID control system for propofol and remifentanil is proposed to regulate the hypnosis component during anesthesia using the bispectral index (BIS) as the process variable. Infusion rates of both drugs are also controlled. The adjustment of the PID parameters, so that the BIS was closer to what was expected, was done using a genetic algorithm. The implementation of the control system was done in Simulink in order to simulate a surgery. The simulation scheme includes the patient models for both drugs, a disturbance profile, and two different PID controllers for the two phases of anesthesia - induction and maintenance. Aspects such as noise in the BIS signal and artifacts were taken into account in the system and a suitable noise filter was applied in the control algorithm. In addition, a ratio between the infusion rates of propofol and remifentanil has been introduced to allow the anesthesiologist to choose the appropriate opioid-hypnotic balance In the end, a performance analysis of the control system was made based on seven performance indices (namely the integrated absolute error, the settling time, the median performance error, the median absolute performance error, the wobble, and the above and below recommended BIS values). Although there are many types of control systems for the automatic control of hypnosis depth described in the literature, these are not usually used in clinical practice. Therefore, it is important to continue research to produce robust and user-friendly systems that integrate clinicians' clinical knowledge and meet their actual needs

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

ObjectivesWe develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FISs), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of the risk.Methods and materialsThree FISs, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to their strategic choices. Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base are agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and later validated the model.ResultsIn an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008–2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret our results. We have also run regressions to better understand the implications of our results.ConclusionsThe main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of our system, which also may be easily applied to other settings

Automation of the anesthetic process: New computer-based solutions to deal with the current frontiers in the assessment, modeling and control of anesthesia

The current trend in automating the anesthetic process focuses on developing a system for fully controlling the different variables involved in anesthesia. To this end, several challenges need to be addressed first. The main objective of this thesis is to propose new solutions that provide answers to the current problems in the field of assessing, modeling and controlling the anesthetic process. Undoubtedly, the main handicap to the development of a comprehensive proposal lies in the absence of a reliable measure of analgesia. This thesis proposes a novel fuzzy-logic-based scheme to evaluate the impact of including a new variable in a decision-making process. This scheme is validated by way of a preliminary analysis of the Analgesia Nociception Index (ANI) monitor on analgesic drug titration. Furthermore, the capacity of the ANI monitor to provide information to replicate the decisions of the experts in different clinical situations is studied. To this end, different artificial intelligence-based algorithms are used: specifically, the suitability of this index is evaluated against other variables commonly used in clinical practice. Regarding the modeling of anesthesia, this thesis presents an adaptive model that allows characterizing the pharmacological interaction effects between the hypnotic and analgesic drug on the depth of hypnosis. In addition, the proposed model takes into account both inter- and intra-patient variabilities observed in the response of the subjects. Finally, this work presents the synthesis of a robust optimal PID controller for regulating the depth of hypnosis by considering the effect of the uncertainties derived from the patient's pharmacological response. Moreover, a study is conducted on the limitations introduced when using a PID controller versus the development of higher order solutions under the same clinical and technical considerations

Sistema inteligente de ayuda a la decisión para la gestión de operaciones de producción en cadenas de suministro de lazo cerrado

Las cadenas de suministro convencionales se están reconvirtiendo en sistemas más complejos, dejando atrás el flujo lineal de materias primas a productos, por una circulación cerrada que atiende al ciclo de vida de cada componente. La gestión de operaciones ha jugado un papel clave en la transición hacia la economía circular, utilizando para ello entidades de mayor alcance denominadas Cadenas de Suministro de Lazo Cerrado (CSLC). En esta tesis se presenta el trabajo de investigación desarrollado para proponer una metodología que resuelva los problemas de toma de decisiones en las operaciones de gestión en las CSLC, centrándose especialmente en la gestión de los inventarios de los procesos. Los criterios operacionales de las cadenas de suministro circulares se caracterizan por tener más incertidumbre que las cadenas de suministro lineal. Es por ello, que la propuesta de estudio se inició planteando una representación de la estructura de un CSLC genérica, ajustada a las necesidades de la gestión de operaciones, que permitió extraer los elementos clave en la toma de decisiones al sinterizar el proceso en aquellas tareas que tenían incidencia significativa sobre las variables a monitorizar. Los enfoques clásicos resuelven los problemas de toma de decisiones operativas sobre la producción utilizando métodos analíticos y simulaciones precisas. Pero en las CSLC este tipo de planteamientos presentan mayor dificultad de aplicación debido a la naturaleza estocástica de su sistema de producción. Por ello, no se ha buscado la precisión de una solución óptima, sino una estrategia de satisfacción basada en el conocimiento y la experiencia de un tomador de decisiones. El sistema propuesto en este estudio combina métodos de lógica difusa y de aprendizaje automático, y es capaz de inferir conocimiento de datos reales, proporcionados por gestores expertos, para calcular un sistema de toma de decisiones automático. El sistema de ayuda a la decisión general se ha validado en una CSLC de ropa lavada hospitalaria, pues se ajusta a las necesidades como caso de prueba, al disponer de una mecánica de funcionamiento muy establecida. Tras los resultados obtenidos, este estudio aparece como una herramienta eficiente para iniciar el camino hacia la integración total de elementos y decisiones en un sistema inteligente en el contexto del paradigma de la Industria 4.0