• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5127-5131
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• 2014

Background: Cervical cancer is the ninth most common cancer among females in Turkey. Cervical smear is a routine screening test used for the detection of cervical abnormalities and also it detects certain infections of the cervix. Objective: To analyze cervical smear results of our clinic in order to determine most frequent pathology of the women in North Eastern Anatolia Region of Turkey. Materials and Methods: In a retrospective study design, 8,495 cervical cytology cases diagnosed at the Pathology Department of the Regional Education and Research Hospital in Erzurum over the last one and half years extending from August 2012 to December 2013 were investigated. Results: The most common diagnosis was found to be inflammation, 65.5 % (5,566 out of 8,495), and the least was squamous epithelial abnormalities 0.2% (13 out of 8,495). There was some variation among the three pathologists regarding diagnosis but findings for the latter. Conclusions: Regular cervical smear tests are one of the most important strategies in early diagnosis of cervical cancer but there are conflicting data regarding the prevalence of epithelial cell abnormalities in Turkey, and the reasons o f this should be investigated.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.100-102
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• 1992

Currently, detection of sacroilitis is necessary in detect ion of ankylosing spondylitis. So early detection of sacroilitis is needed for early detection of ankylosing spondylitis. But it is difficult to detect sacroiliac abnormalities in early stage by conventional plain X-ray. Therefore, it is performed 3-dimensional volume rendering from the CT image of sacroiliac. Then early detection of sacroilitis is made by analyzing the reconstructed 3-dimensional image.

• The Journal of Korean Orthopaedic Ultrasound Society
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• v.7 no.2
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• pp.89-97
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• 2014

Purpose: To evaluate the ultrasonographic findings of the elbows on group screening of middle school baseball players. Materials and Methods: Ninety-three players (age: 12-15, mean 13.5 years) of four middle school baseball team were evaluated with bilateral elbow ultrasonographies in the field regardless of elbow pain. Medial and anterolateral ultrasound examination of the both elbow were performed in the field to detect any abnormalities including medial epicondylar separation or fragmentation and capitellar osteochondritis dissecans respectively. We analyzed the relationship among elbow pain, physical findings and sonographic abnormalities and the differences of sonographic abnormalities between pitchers and fielders. Results: Thirty-six of 93 (39%) players had sonographic abnormalities of elbow in dominant arm, 30 with medial epicondylar apophyseal separation or fragmentation, 2 with osteochondritis dissecans, 4 with both lesions. Twenty-nine of 37 (78%) players with elbow pain had sonographic abnormalities. On physical examination, players with medial epicondylar abnormalities had medial epicondylar tenderness (59%) and pain on valgus stress test (52%), and 5 of 6 (83%) players with osteochondritis dissecans showed flexion contracture more than $5^{\circ}$. The incidence of medial epicondylar abnormalities between pitchers and fielders was statistically not significant but osteochondritis dissecans was more prevalent in pitchers (p<0.05). Conclusion: Elbow sonography is a simple and useful screening tool in the field and also effective for early detection of medial epicondylar abnormalities or osteochondritis dissecans that could be the main causes of elbow pain in adolescent baseball players.

• Clinical and Experimental Pediatrics
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• v.58 no.9
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• pp.317-324
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• 2015

Kabuki syndrome (KS) is a rare syndrome characterized by multiple congenital anomalies and mental retardation. Other characteristics include a peculiar facial gestalt, short stature, skeletal and visceral abnormalities, cardiac anomalies, and immunological defects. Whole exome sequencing has uncovered the genetic basis of KS. Prior to 2013, there was no molecular genetic information about KS in Korean patients. More recently, direct Sanger sequencing and exome sequencing revealed KMT2D variants in 11 Korean patients and a KDM6A variant in one Korean patient. The high detection rate of KMT2D and KDM6A mutations (92.3%) is expected owing to the strict criteria used to establish a clinical diagnosis. Increased awareness and understanding of KS among clinicians is important for diagnosis and management of KS and for primary care of KS patients. Because mutation detection rates rely on the accuracy of the clinical diagnosis and the inclusion or exclusion of atypical cases, recognition of KS will facilitate the identification of novel mutations. A brief review of KS is provided, highlighting the clinical and genetic characteristics of patients with KS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.168-175
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• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.89-92
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• 2021

In a manufacturing fields, an abnormality or breakdown of equipment is a factor that causes product defects. Recently, with the spread of smart factory services, a lot of research to predict and prevent machine's failures is actively ongoing. However, there is a big difficulty in developing a classification model because the number of abnormal or failure data of the machine is severely smaller than normal data. In this paper, we present an algorithm for detecting abnormalities in an MCT at manufacturing work site depending on the differences between inputs and outputs of Autoencoder model and analyze its performance. The algorithm detects abnormalities using only features of normal data from manufacturing data of the MCT in which abnormal data does not exist.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.199-206
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• 2022

A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.583-589
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• 2001

For several decades many researchers have studied various algorithms, known as non-destructive testing, to identify abnormalities within a structure. Damage detection technique using vibration signal is a kind of these methods. Many researchers have published lots of papers dealing vibration signal to identify structural damage. All the methods for damage detection using vibration signal can be divided into two big categories. The first category is the method that requires some reference model such as finite element model, and the second is the method that does not require any reference model but needs only experimental data. This paper will be devoted to classify damage detection methods that utilize vibration signal.

• Proceedings of the KSMRM Conference
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• 2003.10a
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• pp.100-100
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• 2003

In brain MR imaging, contrast-enhanced study is important in the detection and characterization of lesions. As a postcontrast brain MR imaging, conventional T1 weighted imaging has been usually used. Magnetization transfer imaging has been used to increase conspicuity of enhancing lesions. In addition, fat-suppression imaging can be used as in other parts of the body. Recently, FLAIR sequence has been reported to be useful in detecting subarachnoid, meningeal, and subdural abnormalities. In this exhibit, we demonstrate basic principles and typical appearances of various pulse sequences that can be used as a postcontrast brain MR imaging in children. Furthermore, we discuss imaging strategies to increase clinical usefulness of postcontrast brain MR imaging for specific abnormalities. The advantages and disadvantages of each pulse sequence are also discussed.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5741-5747
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• 2015

Purpose: To understand the epidemiology of different upper gastrointestinal (UGI) tract related abnormalities through endoscopic data analysis. Materials and Methods: A retrospective study of three years from January 2009 to December 2011 was conducted with data from endoscopic surveillance of upper GI tract problems, collected from the Gastroenterology Unit, Osmania General Hospital, Hyderabad. MS excel and Medcalc software (comparison of proportions) were used for data analysis. Results: A total of 10,029 (6,468 in males and 3,561 in females) endoscopies were performed during this three-year period. The male to female ratio was 1.8:1. Overall, ~30% of endoscopies evaluated showed patients with acid peptic disorders, 13.6% with vascular-related abnormalities, 10.6% showed structural abnormalities, followed by 6.3% with malignancies. Burden of malignancies was mostly observed in the older age group (60-69 years). Esophageal cancer cases decreased (p=0.0001) whereas stomach cancers increased over this period (p=0.0345). We also observed an increased incidence of acid peptic disease (APD) (p=0.0036) and gastroesophageal reflux disease (GERD) (p=0.0002) cases during this period. Conclusions: Endoscopic diagnosis is useful for early detection of UGI anomalies and helpful for physicians to manage and treat varied kinds of UGI disorders. Analysis of data revealed changing trends in the incidence of various pathologies of the UGI tract. Functional dyspepsia and GERD definitely reduce the quality of life of the individual. The role of our diverse dietary habits and lifestyle associated with these problems have not yet been established, though there have been reports on the effect of coffee, spicy food, wheat-based diet, screening of UGI pathologies along with collection of complete personal and medical history details, can h elp in correlating the patients' condition with various aspects of lifestyle and diet.