Data-driven based four examinations in TCM: a survey / 数字中医药（英文）

@#Traditional Chinese medicine (TCM) diagnosis is a unique disease diagnosis method with thousands of years of TCM theory and effective experience. Its thinking mode in the process is different from that of modern medicine, which includes the essence of TCM theory. From the perspective of clinical application, the four diagnostic methods of TCM, including inspection, auscultation and olfaction, inquiry, and palpation, have been widely accepted by TCM practitioners worldwide. With the rise of artificial intelligence (AI) over the past decades, AI based TCM diagnosis has also grown rapidly, marked by the emerging of a large number of data-driven deep learning models. In this paper, our aim is to simply but systematically review the development of the data-driven technologies applied to the four diagnostic approaches, i.e. the four examinations, in TCM, including data sets, digital signal acquisition devices, and learning based computational algorithms, to better analyze the development of AI-based TCM diagnosis, and provide references for new research and its applications in TCM settings in the future.

Computational intelligence in tropical medicine

The application of computational technology for medical purpose is a very interesting topic. Knowledge content development and new technology search using computational technology becomes the newest approach in medicine. With advanced computational technology, several omics sciences are available for clarification and prediction in medicine. The computational intelligence is an important application that should be mentioned. Here, the author details and discusses on computational intelligence in tropical medicine.

Sistema inteligente para auxílio ao diagnóstico no monitoramento fetal eletrônico por análise de cardiotocografias / A computer aided diagnostic intelligent system for electronic fetal monitoring based on cardiotocographies

A análise acurada da frequência cardíaca fetal (FCF) correlacionada com as contrações uterinas permite diagnosticar, e consequentemente antecipar, diversos problemas relativos ao bem estar fetal e à preservação de sua vida. O presente trabalho apresenta os resultados de um sistema híbrido, baseado em regras determinísticas e em um módulo de inferência nebuloso do tipo Mamdani, para análise de sinais coletados através de exames denominados cardiotocografias (CTG). As variáveis analisadas são: o valor basal da FCF, suas variabilidades de curto e de longo prazo, acelerações transitórias e desacelerações, sendo estas classificadas por seu tipo e número de ocorrências. São utilizados dois modelos de classificação. A saída do sistema, em qualquer dos modelos, é um diagnóstico de primeiro nível baseado nestas variáveis de entrada. O sistema inteligente para auxílio ao diagnóstico no monitoramento fetal eletrônico por análise de cardiotocografias (SISCTG) foi desenvolvido na linguagem de scripts do programa MATLAB® v.7. O projeto conta também com uma parceria multi-institucional entre o Brasil e a Alemanha, envolvendo o Departamento de Engenharia de Teleinformática (DETI) da Universidade Federal do Ceará (UFC), a Maternidade-Escola Assis Chateaubriand (MEAC), a Technische Universitãt München e a empresa alemã Trium GmbH, que fornece a base de dados utilizada neste trabalho. Os resultados apresentados pelo SISCTG mostram-se promissores, com um índice de acertos (comparando-se os dois modelos utilizados) variando de 83% a 100%, de acordo com o tipo de diagnóstico. Isto permite projetar o aprimoramento deste sistema com novas variáveis de entrada (como a entropia aproximada da FCF e da sua variabilidade). A validação do sistema contou com especialistas brasileiros e alemães na área obstétrica.

The accurate analysis of the fetal heart rate (FHR) and its correlation with uterine contractions (UC) allow the diagnostic and the anticipation of many problems related to fetal distress and the preservation of its life. This paper presents the results of a hybrid system based on a set of deterministic rules and fuzzy inference system developed to analyze FHR and UC signals collected by cardiotocography (CTG) exams. The studied variables are basal FHR, short and long-term FHR variability, transitory accelerations and decelerations, these lasts classified by their type and number of occurrences. Two classification models are used. For both models, the system output is a first level diagnostic based on those input variables. The system is developed using the MATLAB® v.7 script language. The project is also supported by a multi-institutional agreement between Brazil and Germany, among the DETI (Departamento de Engenharia de Teleinformática of the Universidade Federal do Ceará), the MEAC (Maternidade-Escola Assis Chateaubriand), the TUM (Technische Universitãt München), and the Trium GmbH, a German company who supplied the database used in this project. The results are very promising with a diagnostic accuracy (considering the two models used) varying from 83% to 100%, according to the type of diagnostic. These results allow the projection of refinements of the proposed system, inserting new input variables (such as the approximate entropy of the FHR and its variability). The system validation methodology was based on the knowledge of Brazilian and German obstetricians.