ABSTRACT
The discovery of active constituents of traditional Chinese medicine(TCM) faces multiple challenges, such as limited approaches to evaluate poly-pharmacological effects, and the lack of systematic methods to identify active constituents. Aimed at these bottleneck problems in the field, the present study intensively discussed the key scientific problems in the identification of active constituents of TCM, based on scientific methodologies including systematology, information theory, and synergetics. A comprehensive strategy is herein proposed to investigate the correlations between the chemical composition and biological activities of TCM, from macro-, meso-, and micro-scales. Moreover, in this study, we systematically proposed the methodology of the multimodal identification of TCM active constituents, and thoroughly constructed its core technologies. Its technical framework is suggested to be assessed by multimodal information acquisition, centered on multisource information fusion, and focused on interaction evaluation. Furthermore, the core technologies for the multimodal identification of active constituents of TCM were developed in this study, which is according to the characteristics of the exchanges of between TCM and biological organisms, in the aspects of material, energy and information. Finally, two examples of the application of the proposed method were briefly introduced. The proposed methodology provides a novel way to solve the bottlenecks in the study of active constituents of TCM, and lays the foundation for the multimodal study of TCM.
Subject(s)
Chemistry, Pharmaceutical/methods , Drugs, Chinese Herbal/chemistry , Medicine, Chinese Traditional , Research DesignABSTRACT
Objective To determine the consistency of urogenital hiatus ( U H ) data between the semi‐automatic measurement and manual measurement using transperineal pelvic floor ultrasonography . Methods Total of 286 three‐dimensional images of minimal U H dimension were obtained . And they were divided into study group ( 100 images) and test group ( 186 images) randomly . T hree experts traced and created the w hole profile of the U H of those images in the study group by M AT LAB . T hen the semi‐automatic software was obtained through machine learning algorithms . In the test group , 6 parameters of U H ( including anterioposterior diameter , transverse diameter ,circumference , area ,left and right levator urethral gap distance) were measured by two experts ( D 1 and D2 ) both manually and semi‐automatically . T he time experts spent on measuring was also recorded and compared . Results T he time used for semi‐automatic measurement was significantly shorter than that for manual measurement [ ( 7 .49 ± 1 .51 ) s vs ( 42 .42 ± 11 .08) s ,( 7 .52 ± 1 .37) s vs ( 43 .45 ± 9 .09) s for D1 and D2 , t ＝ －12 .09 ,－13 .64 ,all P＝0 .00] . T he Pearson correlation coefficients between semi‐automatic and manual measurements of 6 parameters were 0 .857 －0 .985 ( P ＜ 0 .01) ,0 .853 －0 .979 ( P ＜ 0 .01 ) in D1 and D2 ,respectively . T he interclass correlation coefficients ( ICC) of six parameters were ranged from 0 .846 －0 .985 for D1 and 0 .843～0 .979 for D2 ( all P ＜ 0 .01 ) . T he Bland Altman plot also showed good agreement between two methods . Conclusions Intellectual recognition and semi‐automatic measurement has simplified the process for U H measurement ,and it is proved to be a reliable and timesaving method that is practical for clinical use .
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Objective To investigate the feasibility of the automatic cystocele severity grading software for quantitative evaluation of prolapse of bladder posterior wall by transperineal ultrasound . Methods One hundred and seventy transperineal ultrasound video clips were recorded when the female patients performing the Valsalva maneuver and those clips were divided into training group ( 85 cases) and test group ( 85 cases) randomly ,then the ralated structures of the images from the training group offline were marked . Through machine learning algorithm ,the computer had learned and was able to analyzed the marking information ,then the automatic cystocele severity grading software was obtained . And later the software was ran to mark the structures and get the cystocele severity grading in the images from the test group . Meanwhile , the same structures of the same images manually were marked and after an interval of more than two weeks the process were repeated by 3 doctors . Finally the grading results obtained from the software and the measurers of the 3 doctors were compared . Results The intelligent identification and automatic measurement software obtained from the machine learning algorithm was able to identify the related structures . The grading results of each measurer were of good consistency ( κ :0 .72 －0 .78 ;ICC :0 .980－0 .990) . The grading results between different measurers were of good consistency ( κ :0 .65－0 .75 ;ICC :0 .985－0 .992) . The grading results between automatic software and three different measurers were of good consistency ( κ :0 .63－0 .67 ;ICC :0 .967－0 .969 ; r ＝0 .936 ,0 .943 ,0 .936 ,all P ＜0 .01) . Conclusions The automatic cystocele severity grading software is able to identify the related structures in the images and reliable to apply the software in pelvic floor ultrasound .
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Objective To develop an intelligent system which is able to offer an optimized emergency treatment recommendation for fast triage automatically.Methods An algorithm and intelligent platform for grading injury were developed based on physiological signal collecting technology, intelligent grading algorithm and integration technology.A comparison between this system and traditional methods was made.Results This intelligent system was able to increase accuracy by 21%and took only 48%of the time taken by traditional methods.There was significant difference between the two groups(P=0.038<0.05).Conclusion The accuracy of the triage is improved by this intelligent system that is less time-consuming.With this device, the injury statement can be identified quickly and the targeted medical treatment can be performed accurately.The efficiency of emergency treatment in case of disaster will thus be dramatically increased.
ABSTRACT
This article was aimed to propose and develop traditional Chinese medicine (TCM) health engineering, using modern science and technology, in order to carry out the detection research which reflected TCM features of human functional state. It was an important way to achieve TCM diagnosis and the objective evaluation on therapeutic effects of TCM. The combination of TCM health engineering with information technology, and the formation of TCM functional state intelligent identification system, as well as the extension into the Health Internet of Things (HIT) could provide more accurate and convenient services for human healthcare in the age of big health. And identification on TCM syndromes was the core to achieve this goal. Using characterization data on TCM syndromes could realize the data processing between TCM syndrome and prescription through intelligent computing, in order to form the TCM syndrome differentiation and treatment intelligent system. It had great significance in the promotion of TCM health project.