• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Analytical Science and Technology
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• v.25 no.4
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• pp.257-264
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• 2012

Penicillins belong to the ${\beta}$-lactam class of antibiotics, and are frequently used in human and veterinary medicine. Despite the positive effects of these drugs, improper use of penicillins poses a potential health risk to consumers. This study has been undertaken to determinate multi-residues of penicillins, including amoxicillin, ampicillin, oxacillin, bezylpenicillin, cloxacillin, dicloxacillin, and nafcillin, using liquid chromatographic tandem mass spectrometer (LC-MS/MS). The developed method was validated for specificity, precision, recovery, and linearity in livestock and marine products. The analytes were extracted with 80% acetonitrile and clean-up by a single reversed-phase solid-phase extraction step. Six penicillins presented recoveries higher than 76% with the exception of Amoxicillin. Relative standard deviations (RSDs) were not more than 10%. The method was applied to 225 real samples. Benzylpenicillin was detected in 12 livestock products and 7 marine products. Amoxicillin, ampicillin, cloxacilllin, dicloxacillin, nafcillin and oxacillin were not detected. The detected levels were 0.001~0.009 mg/kg in livestock products excluding eggs and milk. In marine products, the detected levels were under 0.03 mg/kg. They were under the MRL levels. As monitoring results, it is identified to be safe but it is considered that safety management of antibiotics should continue by monitoring.

• Korean Journal of Clinical Laboratory Science
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• v.36 no.1
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• pp.13-18
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• 2004

This study was designed to establish working range for reoportable range in own laboratory in order to cover the upper and lower limits of the range in test method. We experimented ten times during 10 days for setting of reportable range with between run for method evaluation. It is generally assumed that the analytical method produces a linear response and that the test results between those upper and lower limits are then reportable. CLIA recommends that laboratories verify the reportable range of all moderate and high complexity tests. The Clinical Laboratory Improvement Amendments(CLIA) and Laboratory Accreditation Program of the Korean Society for Laboratory Medicine states reportable range is only required for "modified" moderately complex tests. Linearity requirements have been eliminated from the CLIA regulations and from others accreditation agencies, many inspectors continue to feel that linearity studies are a part of good lab practice and should be encouraged. It is important to assess the useful reportable range of a laboratory method, i.e., the lowest and highest test results that are reliable and can be reported. Manufacturers make claims for the reportable range of their methods by stating the upper and lower limits of the range. Instrument manufacturers state an operating range and a reportable range. The commercial linearity material can be used to verify this range, if it adequately covers the stated linear interval. CLIA requirements for quality control, must demonstrate that, prior to reporting patient test results, it can obtain the performance specifications for accuracy, precision, and reportable range of patient test results, comparable to those established by the manufacturer. If applicable, the laboratory must also verify the reportable range of patient test results. The reportable range of patient test results is the range of test result values over which the laboratory can establish or verify the accuracy of the instrument, kit or test system measurement response. We need to define the usable reportable range of the method so that the experiments can be properly planned and valid data can be collected. The reportable range is usually defined as the range where the analytical response of the method is linear with respect to the concentration of the analyte being measured. In conclusion, experimental results on reportable range using concentrated control sample and zero calibrators covering from highest to lowest range were salicylate $8.8{\mu}g/dL$, phenytoin $0.67{\mu}g/dL$, phenobarbital $1.53{\mu}g/dL$, primidone $0.16{\mu}g/dL$, theophylline $0.2{\mu}g/dL$, vancomycine $1.3{\mu}g/dL$, valproic acid $3.2{\mu}g/dL$, digitoxin 0.17ng/dL, carbamazepine $0.36{\mu}g/dL$ and acetaminophen $0.7{\mu}g/dL$ at minimum level and salicylate $969.9{\mu}g/dL$, phenytoin $38.1{\mu}g/dL$, phenobarbital $60.4{\mu}g/dL$, primidone $24.57{\mu}g/dL$, theophylline $39.2{\mu}g/dL$, vancomycine $83.65{\mu}g/dL$, valproic acid $147.96{\mu}g/dL$, digitoxin 5.04ng/dL, carbamazepine $19.76{\mu}g/dL$, acetaminophen $300.92{\mu}g/dL$ at maximum level.

• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.158-169
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• 2019

Ergot alkaloids are mycotoxin produced by fungi of the Claviceps genus, mainly by Claviceps purpurea in EU. Recently obtained informations indicates necessity for control the ergot in imported grains. Recent occurrence data of ergot alkaloids from EU countries indicate the necessities of management and control these toxins from the imported grains like rye, wheat, oat etc. The aim of this study is to optimize the liquid chromatography-tandem mass spectrometry method for determination of ergot alkaloids (ergometrine, ergosine, ergotamine, ergocornine, ergocryptine, ergocristine and their epimers (-inines) from grain and grain-based food. The test method was optimized by extracting the sample with acetonitrile containing 2 mM ammonium carbonate, purification with Mycosep cartridge, and instrumental analysis by LC-MS/MS using Syncronis C18 column. The standard calibration curves showed linearity with correlation coefficents; $R^2$ >0.99. Mean recoveries ranged from 72.0 to 111.3% at three different fortified levels (20, 50, and $100{\mu}g/kg$). The correlation coefficient expressed as precision was within the range of 1.9-12.9%. The limit or quantifications (LOQ) ranged from 0.012 to $0.058{\mu}g/kg$. The developed analytical method met the criteria of AOAC Int. and CAC validation parameters like accuracy and sensitivity. As a result, it was confirmed that the test method developed in this study is suitable for the simultaneous analysis of six species of ergot alkaloid from grains and grain products.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.