• Title/Summary/Keyword: Medical Information Standard Technology

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Rapid Detection of Streptococcus mutans Using an Integrated Microfluidic System with Loop-Mediated Isothermal Amplification

  • Jingfu Wang;Jingyi Wang;Xin Chang;Jin Shang;Yuehui Wang;Qin Ma;Liangliang Shen
    • Journal of Microbiology and Biotechnology
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    • v.33 no.8
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    • pp.1101-1110
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    • 2023
  • Streptococcus mutans is the primary causative agent of caries, which is one of the most common human diseases. Thus, rapid and early detection of cariogenic bacteria is critical for its prevention. This study investigated the combination of loop-mediated isothermal amplification (LAMP) and microfluid technology to quantitatively detect S. mutans. A low-cost, rapid microfluidic chip using LAMP technology was developed to amplify and detect bacteria at 2.2-2.2 × 106 colony-forming units (CFU)/ml and its detection limits were compared to those of standard polymerase chain reaction. A visualization system was established to quantitatively determine the experimental results, and a functional relationship between the bacterial concentration and quantitative results was established. The detection limit of S. mutans using this microfluidic chip was 2.2 CFU/ml, which was lower than that of the standard approach. After quantification, the experimental results showed a good linear relationship with the concentration of S. mutans, thereby confirming the effectiveness and accuracy of the custom-made integrated LAMP microfluidic system for the detection of S. mutans. The microfluidic system described herein may represent a promising simple detection method for the specific and rapid testing of individuals at risk of caries.

A Three-Dimensional Deep Convolutional Neural Network for Automatic Segmentation and Diameter Measurement of Type B Aortic Dissection

  • Yitong Yu;Yang Gao;Jianyong Wei;Fangzhou Liao;Qianjiang Xiao;Jie Zhang;Weihua Yin;Bin Lu
    • Korean Journal of Radiology
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    • v.22 no.2
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    • pp.168-178
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    • 2021
  • Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). Materials and Methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). Conclusion: The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

Reversal of Multidrug Resistance and Computational Studies of Pistagremic Acid Isolated from Pistacia integerrima

  • Rauf, Abdur;Uddin, Ghias;Raza, Muslim;Ahmad, Aftab;Jehan, Noor;Ahmad, Bashir;Nisar, Muhammad;Molnar, Joseph;Csonka, Akos;Szabo, Diana;Khan, Ajmal;Farooq, Umar;Noor, Mah
    • Asian Pacific Journal of Cancer Prevention
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    • v.17 no.4
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    • pp.2311-2314
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    • 2016
  • Pistagremic acid (PA) is a bioactive triterpenoid isolated from various parts of Pistacia integerrima plants. The aim of this research was to investigate PA for reversion of multidrug resistant (MDR) mediated by P-glycoprotein using rhodamine-123 exclusion study on a multidrug resistant human ABCB1 (ATP-binding cassette, sub-family B, member 1) gene-transfected mouse T-lymphoma cell line in vitro. Results were similar to those with verapamil as a positive control. Docking studies of PA and standard Rhodamine123 were carried out against a P-gp crystal structure which showed satisfactory results. Actually, PA cannot bind exactly where co-crystallized ligand of P-gp is already present. However, the docking study predicted that if a compound gives a lesser score then it may have some potency. The docking scores of PA and Rhodamine were similar. Therefore, we can conclude that there are certain important chemical features of PA which are responsible for the inhibiting potency of P-gp.

Implementation of ISO/IEEE 11073-10404 Monitoring System Based on U-Health Service (유헬스 서비스 기반의 ISO/IEEE 11073-10404 모니터링 시스템 구현)

  • Kim, Kyoung-Mok
    • Journal of Advanced Navigation Technology
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    • v.18 no.6
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    • pp.625-632
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    • 2014
  • The u-health service is using portable device such as smart device and it consists of small computing device. The u-health service carry out same performance with desktop computer. We designed message structure based on Bluetooth HDP. This message structure is used to transmit patient's biometric data on the smart device of medical team, patient and family over the mobile network environment. ISO/IEEE 11073 PHD standard was defined based on the method of communication between the agent and the manager. And We are confirmed the reliable transmission of biometric data at the smart device by implementing the android OS based patient information monitoring application to check the status of patient for medical team, patient and family.

Paramedic Student Perception and Attitude on Child Abuse

  • Jung, Ji-Yeon;Sin, Sang-Yeol;Lee, Jae-Min
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.12
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    • pp.219-226
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    • 2020
  • This study conducted an online survey of university students based in Jeolla-do province from September 1 to 10, 2020 using Naver survey form to find out the perception and attitude of paramedic students about child abuse. A total of 293 students were studied, and the collected data was analyzed using SPSS WIN 20.0. The analysis of general characteristics, recognition of child abuse reporting obligations and activation plans, and awareness of child abuse education were calculated with frequency and percentage, and the degree of child abuse awareness was calculated by the average and standard deviation. Differences in perception of child abuse behavior based on the general characteristics of respondents were used T-test and ANOVA.

A Study on the Photographic Characteristics of Laser Scanner Film (Laser Scanner 필름의 사진특성에 관한 연구)

  • Kim, Yeoung-Chan
    • The Journal of Information Technology
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    • v.8 no.2
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    • pp.53-58
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    • 2005
  • In this study, we made experiments the preparation of silver halide microcrystals, physical ripening, chemical ripening, spectral sensitivity, additives and coating in order to develop medical laser scanner film which has photographic characteristic suitable for exposure to He-Ne and Ar laser. In the practice of sensitometry, the photographic material is exposed to a known quantity and quality of radiant energy, developed under standard conditions, and the densities resulting from the various exposures are then measured. The results are usually expressed in graphic form as curves, and from these curves numerical values are derived which are used to specify the characteristics of the material.

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The Design of mBodyCloud System for Sensor Information Monitoring in the Mobile Cloud Environment

  • Park, Sungbin;Moon, Seok-Jae;Lee, Jong-Yong;Jung, Kye-Dong
    • International journal of advanced smart convergence
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    • v.5 no.1
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    • pp.1-7
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    • 2016
  • Recently, introduced a cloud computing technology to the IT industry, smart phones, it has become possible connection between mobility terminal such as a tablet PC. For dissemination and popularization of movable wireless terminal, the same operation have focused on a viable mobile cloud in various terminal. Also, it evolved Wireless Sensor Network(WSN) technology, utilizing a Body Sensor Network(BSN), which research is underway to build large Ubiquitous Sensor Network(USN). BSN is based on large-scale sensor networks, it integrates the state information of the patient's body, it has been the need to build a managed system. Also, by transferring the acquired sensor information to HIS(Hospital Information System), there is a need to frequently monitor the condition of the patient. Therefore, In this paper, possible sensor information exchange between terminals in a mobile cloud environment, by integrating the data obtained by the body sensor HIS and interoperable data DBaaS (DataBase as a Service) it will provide a base of mBodyCloud System. Therefore, to provide an integrated protocol to include the sensor data to a standard HL7(Health Level7) medical information data.

An embedded vision system based on an analog VLSI Optical Flow vision sensor

  • Becanovic, Vlatako;Matsuo, Takayuki;Stocker, Alan A.
    • Proceedings of the Korea Society of Information Technology Applications Conference
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    • 2005.11a
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    • pp.285-288
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    • 2005
  • We propose a novel programmable miniature vision module based on a custom designed analog VLSI (aVLSI) chip. The vision module consists of the optical flow vision sensor embedded with commercial off-the-shelves digital hardware; in our case is the Intel XScale PXA270 processor enforced with a programmable gate array device. The aVLSI sensor provides gray-scale imager data as well as smooth optical flow estimates, thus each pixel gives a triplet of information that can be continuously read out as three independent images. The particular computational architecture of the custom designed sensor, which is fully parallel and also analog, allows for efficient real-time estimations of the smooth optical flow. The Intel XScale PXA270 controls the sensor read-out and furthermore allows, together with the programmable gate array, for additional higher level processing of the intensity image and optical flow data. It also provides the necessary standard interface such that the module can be easily programmed and integrated into different vision systems, or even form a complete stand-alone vision system itself. The low power consumption, small size and flexible interface of the proposed vision module suggests that it could be particularly well suited as a vision system in an autonomous robotics platform and especially well suited for educational projects in the robotic sciences.

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Improvement Segmentation Method of Medical Images using Volume Data (의료영상에서 볼륨 데이터를 이용한 분할개선 기법)

  • Chae, Seung-Hoon;Pan, Sung Bum
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.8
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    • pp.225-231
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    • 2013
  • Medical image segmentation is an image processing technology prior to performing various medical image processing. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Accurate judgment of segmentation region is needed to segment the interest region in which patient requested in medical image that various organs exist. However, an case that scanned a part of organs is small occurs. In this case, information to determine the segmentation region is lack. consequently, a removal of segmentation region occurs during the segmentation process. In this paper, we improved segmentation results in a small region using volume data and linear equation. In order to verify the performance of the proposed method, we segmented the lung region of chest CT images. As a result of experiments, we confirmed that image segmentation accuracy rose from 0.978 to 0.981 and standard deviation also improved from 0.281 to 0.187.

Construction of Artificial Intelligence Training Platform for Multi-Center Clinical Research (다기관 임상연구를 위한 인공지능 학습 플랫폼 구축)

  • Lee, Chung-Sub;Kim, Ji-Eon;No, Si-Hyeong;Kim, Tae-Hoon;Yoon, Kwon-Ha;Jeong, Chang-Won
    • KIPS Transactions on Computer and Communication Systems
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    • v.9 no.10
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    • pp.239-246
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    • 2020
  • In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.