Query: dongjun choi

title: A security study for Control Network: Security Threat Using Control Protocol
abstract: Unlike a general IT environment, an industrial control system is an environment where stability and continuity are more important than security. In the event of a security accident in the industrial control system, physical motion can be controlled, so physical damage can occur and physical damage can even result in personal injury. Cyber attacks on industrial control systems are not simply cyber damage, but terrorism. However, the security of industrial control systems has not been strengthened yet, and many vulnerabilities are actually occurring. This paper shows that the PLC can be remotely controlled by analyzing the connection process and packets for the PLC protocol used in the industrial control system and bypassing the security mechanism existing in the protocol. Through this, we intend to raise the security awareness of the industrial control system. kiweodeu:saneobjeeosiseutem, boan, peurotokol, jaejeonsonggonggyeog Industrial Control System, Security, Protocol, ReplayAttack http:/www.jsebs.org ISSN: 2288 3908 The Journal of Society for e Business Studies Vol.25, No.2, May 2020, pp.99 108 https:/doi.org/10.7838/jsebs.2020.25.2.099

title: Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph
abstract: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear.  The algorithm was developed using 6793 shoulder radiograph series performed between January 2015 and June 2018, which were labeled based on ultrasound or MRI conducted within 90 days, and clinical information (age, sex, dominant side, history of trauma, degree of pain) The output was the probability of significant rotator cuff tear (supraspinatus/infraspinatus complex tear with 50% of tendon thickness) An operating point corresponding to sensitivity of 98% was set to achieve high negative predictive value (NPV) and low negative likelihood ratio (LR The performance of the algorithm was tested with 1095 radiograph series performed between July and December 2018. Subgroup analysis using Fisher's exact test was performed to identify factors (clinical information, radiography vendor, advanced imaging modality) associated with negative test results and NPV.  Sensitivity, NPV, and LR were 97.3% 96.6% and 0.06, respectively. The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients suspected of rotator cuff tear. The subgroup analysis showed that age 60 years p 0.001) non dominant side p 0.001) absence of trauma history p 0.001) and ultrasound examination p 0.001) were associated with negative test results. NPVs were higher in patients with age 60 years p 0.024) and examined with ultrasound p 0.001)  The deep learning algorithm could accurately rule out significant rotator cuff tear based on shoulder radiographs.  The deep learning algorithm can rule out significant rotator cuff tear with a negative likelihood ratio of 0.06 and a negative predictive value of 96.6% The deep learning algorithm can guide patients with significant rotator cuff tear to additional shoulder ultrasound or MRI with a sensitivity of 97.3% The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients with clinically suspected rotator cuff tear.

title: Can Additional Patient Information Improve the Diagnostic Performance of Deep Learning for the Interpretation of Knee Osteoarthritis Severity
abstract: The study compares the diagnostic performance of deep learning (DL) with that of the former radiologist reading of the Kellgren Lawrence (KL) grade and evaluates whether additional patient data can improve the diagnostic performance of DL. From March 2003 to February 2017, 3000 patients with 4366 knee AP radiographs were randomly selected. DL was trained using knee images and clinical information in two stages. In the first stage, DL was trained only with images and then in the second stage, it was trained with image data and clinical information. In the test set of image data, the areas under the receiver operating characteristic curve (AUC)s of the DL algorithm in diagnosing KL 0 to KL 4 were 0.91 (95% confidence interval (CI) 0.88 0.95) 0.80 (95% CI, 0.76 0.84) 0.69 (95% CI, 0.64 0.73) 0.86 (95% CI, 0.83 0.89) and 0.96 (95% CI, 0.94 0.98) respectively. In the test set with image data and additional patient information, the AUCs of the DL algorithm in diagnosing KL 0 to KL 4 were 0.97 (95% confidence interval (CI) 0.71 0.74) 0.85 (95% CI, 0.80 0.86) 0.75 (95% CI, 0.66 0.73) 0.86 (95% CI, 0.79 0.85) and 0.95 (95% CI, 0.91 0.97) respectively. The diagnostic performance of image data with additional patient information showed a statistically significantly higher AUC than image data alone in diagnosing KL 0, 1, and 2 (p values were 0.008, 0.020, and 0.027, respectively).The diagnostic performance of DL was comparable to that of the former radiologist reading of the knee osteoarthritis KL grade. Additional patient information improved DL diagnosis in interpreting early knee osteoarthritis.

title: Can Deep Learning Using Weight Bearing Knee Anterio Posterior Radiograph Alone Replace a Whole Leg Radiograph in the Interpretation of Weight Bearing Line Ratio?
abstract: Weight bearing whole leg radiograph (WLR) is essential to assess lower limb alignment such as weight bearing line (WBL) ratio. The purpose of this study was to develop a deep learning (DL) model that predicts the WBL ratio using knee standing AP alone. Total of 3997 knee AP WLRs were used. WBL ratio was used for labeling and analysis of prediction accuracy. The WBL ratio was divided into seven categories (0, 0.1, 0.2, 0.3, 0.4, 0.5, and 0.6) After training, performance of the DL model was evaluated. Final performance was evaluated using 386 subjects as a test set. Cumulative score (CS) within error range 0.1 was set with showing maximum CS in the validation set (95% CI, 0.924 0.970) In the test set, mean absolute error was 0.054 (95% CI, 0.048 0.061) and CS was 0.951 (95% CI, 0.924 0.970) Developed DL algorithm could predict the WBL ratio on knee standing AP alone with comparable accuracy as the degree primary physician can assess the alignment. It can be the basis for developing an automated lower limb alignment assessment tool that can be used easily and cost effectively in primary clinics.

title: Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning
abstract: To develop a deep learning algorithm capable of evaluating subscapularis tendon (SSC) tears based on axillary lateral shoulder radiography. A total of 2,779 axillary lateral shoulder radiographs (performed between February 2010 and December 2018) and the patients' corresponding clinical information (age, sex, dominant side, history of trauma, and degree of pain) were used to develop the deep learning algorithm. The radiographs were labeled based on arthroscopic findings, with the output being the probability of an SSC tear exceeding 50% of the tendon's thickness. The algorithm's performance was evaluated by determining the area under the receiver operating characteristic curve (AUC) sensitivity, specificity, negative predictive value (NPV) and negative likelihood ratio (LR at a predefined high sensitivity cutoff point. Two different test sets were used, with radiographs obtained between January and December 2019; Test Set 1 used arthroscopic findings as the reference standard (n 340) whereas Test Set 2 used MRI findings as the reference standard (n 627) The AUCs were 0.83 (95% confidence interval, 0.79 0.88) and 0.82 (95% confidence interval, 0.79 0.86) for Test Sets 1 and 2, respectively. At the high sensitivity cutoff point, the sensitivity, NPV, and LR were 91.4% 90.4% and 0.21 in Test Set 1, and 90.2% 89.5% and 0.21 in Test Set 2, respectively. Gradient weighted Class Activation Mapping identified the subscapularis insertion site at the lesser tuberosity as the most sensitive region. Our deep learning algorithm is capable of assessing SSC tears based on changes at the lesser tuberosity on axillary lateral radiographs with moderate accuracy. We have developed a deep learning algorithm capable of assessing SSC tears based on changes at the lesser tuberosity on axillary lateral radiographs and previous clinical data with moderate accuracy. Our deep learning algorithm could be used as an objective method to initially assess SSC integrity and to identify those who would and would not benefit from further investigation or treatment.

title: Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi View Radiographs
abstract: Accurate image interpretation of Waters' and Caldwell view radiographs used for sinusitis screening is challenging. Therefore, we developed a deep learning algorithm for diagnosing frontal, ethmoid, and maxillary sinusitis on both Waters' and Caldwell views. The datasets were selected for the training and validation set (n 1403, sinusitis% 34.3% and the test set (n 132, sinusitis% 29.5% by temporal separation. The algorithm can simultaneously detect and classify each paranasal sinus using both Waters' and Caldwell views without manual cropping. Single and multi view models were compared. Our proposed algorithm satisfactorily diagnosed frontal, ethmoid, and maxillary sinusitis on both Waters' and Caldwell views (area under the curve (AUC) 0.71 (95% confidence interval, 0.62 0.80) 0.78 (0.72 0.85) and 0.88 (0.84 0.92) respectively) The one sided DeLong's test was used to compare the AUCs, and the Obuchowski Rockette model was used to pool the AUCs of the radiologists. The algorithm yielded a higher AUC than radiologists for ethmoid and maxillary sinusitis (p 0.012 and 0.013, respectively) The multi view model also exhibited a higher AUC than the single Waters' view model for maxillary sinusitis (p 0.038) Therefore, our algorithm showed diagnostic performances comparable to radiologists and enhanced the value of radiography as a first line imaging modality in assessing multiple sinusitis.

title: Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography
abstract: The aim of this study was to compare the diagnostic performance of a deep learning algorithm with that of radiologists in diagnosing maxillary sinusitis on Waters' view radiographs.  Among 80,475 Waters' view radiographs, examined between May 2003 and February 2017, 9000 randomly selected cases were classified as normal or maxillary sinusitis based on radiographic findings and divided into training (n 8000) and validation (n 1000) sets to develop a deep learning algorithm. Two test sets composed of Waters' view radiographs with concurrent paranasal sinus computed tomography were labeled based on computed tomography findings: one with temporal separation (n 140) and the other with geographic separation (n 200) from the training set. Area under the receiver operating characteristics curve (AUC) sensitivity, and specificity of the algorithm and 5 radiologists were assessed. Interobserver agreement between the algorithm and majority decision of the radiologists was measured. The correlation coefficient between the predicted probability of the algorithm and average confidence level of the radiologists was determined.  The AUCs of the deep learning algorithm were 0.93 and 0.88 for the temporal and geographic external test sets, respectively. The AUCs of the radiologists were 0.83 to 0.89 for the temporal and 0.75 to 0.84 for the geographic external test sets. The deep learning algorithm showed statistically significantly higher AUC than radiologist in both test sets. In terms of sensitivity and specificity, the deep learning algorithm was comparable to the radiologists. A strong interobserver agreement was noted between the algorithm and radiologists (Cohen &kgr; coefficient, 0.82) The correlation coefficient between the predicted probability of the algorithm and confidence level of radiologists was 0.89 and 0.84 for the 2 test sets, respectively.  The deep learning algorithm could diagnose maxillary sinusitis on Waters' view radiograph with superior AUC and comparable sensitivity and specificity to those of radiologists.

title: Performance of deep learning to detect mastoiditis using multiple conventional radiographs of mastoid
abstract: This study aimed to compare the diagnostic performance of deep learning algorithm trained by single view (anterior posterior (AP) or lateral view) with that trained by multiple views (both views together) in diagnosis of mastoiditis on mastoid series and compare the diagnostic performance between the algorithm and radiologists.  Total 9,988 mastoid series (AP and lateral views) were classified as normal or abnormal (mastoiditis) based on radiographic findings. Among them 792 image sets with temporal bone CT were classified as the gold standard test set and remaining sets were randomly divided into training (n 8,276) and validation (n 920) sets by 9:1 for developing a deep learning algorithm. Temporal (n 294) and geographic (n 308) external test sets were also collected. Diagnostic performance of deep learning algorithm trained by single view was compared with that trained by multiple views. Diagnostic performance of the algorithm and two radiologists was assessed. Inter observer agreement between the algorithm and radiologists and between two radiologists was calculated.  Area under the receiver operating characteristic curves of algorithm using multiple views (0.971, 0.978, and 0.965 for gold standard, temporal, and geographic external test sets, respectively) showed higher values than those using single view (0.964/0.953, 0.952/0.961, and 0.961/0.942 for AP view/lateral view of gold standard, temporal external, and geographic external test sets, respectively) in all test sets. The algorithm showed statistically significant higher specificity compared with radiologists (p 0.018 and 0.012) There was substantial agreement between the algorithm and two radiologists and between two radiologists (k 0.79, 0.8, and 0.76)  The deep learning algorithm trained by multiple views showed better performance than that trained by single view. The diagnostic performance of the algorithm for detecting mastoiditis on mastoid series was similar to or higher than that of radiologists.

title: Pre existing and machine learning based models for cardiovascular risk prediction
abstract: Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre existing cardiovascular risk prediction models and developed machine learning based prediction algorithms. This study included 222,998 Korean adults aged 40 79 years, naive to lipid lowering therapy, had no history of cardiovascular disease. Pre existing models showed moderate to good discrimination in predicting future cardiovascular events (C statistics 0.70 0.80) Pooled cohort equation (PCE) specifically showed C statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C statistic (0.751) which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer Lemeshow kh2 86.1, P 0.001) than PCE for whites did (Hosmer Lemeshow kh2 171.1, P 0.001) Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin naive healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.

title: A Study on feasibility for runway development IIAC 4th runway construction case study