• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• Korean Journal of Radiology
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• 제25권4호
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• pp.343-350
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• 2024

Objective: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. Materials and Methods: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. Results: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). Conclusion: PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.

• 대한방사선기술학회지:방사선기술과학
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• 제45권4호
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• pp.321-330
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• 2022

Echocardiography and cardiac enzymes test are the tests to assess ischemic heart disease. The purpose of this study was to verify the accuracy by comparing and analyzing two tests for the diagnosis of ischemic heart disease. A retrospective study was conducted on 393 study subjects who underwent echocardiography and cardiac enzymes test. As a result of the study, regional wall motion abnormality (RWMA) increased as the age of the study subjects increased. As a result of ROC analysis, RWMA showed a larger area under the curve (AUC) than cardiac enzymes. RWMA showed the highest accuracy with 81.1% of all cardiac enzymes. Among cardiac enzymes, cTnI showed the highest accuracy. Thus, It was confirmed that RWMA of echocardiography is more accurate than cardiac enzyme is in diagnosing ischemic heart disease.

• Journal of Multimedia Information System
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• 제3권2호
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• pp.35-42
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• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• 대한소아치과학회지
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• 제50권4호
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• pp.421-433
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• 2023

This study was to examine the developmental dental abnormalities in childhood cancer survivors. Risk factors were assessed for 125 children with radiographic data through a retrospective analysis of medical records and panoramic images. 68.0% of childhood cancer survivors exhibited at least one dental abnormality. The types of abnormalities varied depending on the age at cancer diagnosis and treatment intensity, ranging from microdontia (43.2%), to abnormal root development (39.2%) and tooth agenesis (33.6%). Logistic regression analysis demonstrated that a young age at diagnosis (under 3 years), the use of heavy metal agents, a history of hematopoietic stem cell transplantation (HSCT), and combination treatment of chemotherapy, radiation therapy, and HSCT were associated with a significantly higher risk for overall dental abnormalities. The increased risk ratios were 6.00, 3.06, 3.22, and 7.87, respectively (p < 0.05). The results of this study will predict dental abnormality in permanent dentition according to the diagnosis age and treatment method of childhood cancer.

• 대한영상의학회지
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• 제82권4호
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• pp.770-790
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• 2021

최신 국제 임상진료지침을 기반으로 한 특발폐섬유증의 진단은 부합하는 임상 소견과 함께 고해상 CT에서 전형적인 상용간질폐렴 소견을 보일 때 조직학적 폐 생검 없이 진단 가능하다. 영상 검사는 특발폐섬유증의 평가 및 진단에 중추적인 역할을 하며, 정확한 진단을 위하여 임상적, 영상의학적 및 병리학적 소견에 대한 다학제 검토의 중요성이 강조된다. 간질성폐이상(interstitial lung abnormality)은 우연히 발견된 영상의학적 이상 소견을 지칭하며, 간질성폐이상과 임상적으로 의미 있는 간질폐질환에 대한 구분은 적절한 임상 평가를 기반으로 이루어져야 한다. 저자들은 이번 종설을 통하여 특발폐섬유증 진단의 최신 지견 및 간질성폐이상에 대한 이해를 도움으로써 미만성 간질폐섬유증 환자의 정확한 진단과 치료 및 예후 증진에 도움이 되고자 한다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.801-804
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• 2013

미각의 종류별 구획에 따른 설진 모델을 제안한다. 시스템의 전체 구성은 혀 영상획득, 혀 영역 검출, 혀 영역 분할, 분할 영역의 색상분포 검출, 이상 유무 판별로 구성된다. 혀의 DB는 정상 및 비정상 혀로 분류되었으며 실제 한방병원에 내원하는 환자들의 혀 사진으로 구축하였다. 혀 영역으로부터 짠맛, 신맛, 단맛, 쓴맛의 네 가지 영역으로 나누어 분할하고, HSI 컬러모델을 이용하여 색상분석을 시행하였다. 이때, 주변 조도의 영향을 최소화하기 위하여 I(Intensity)값을 제외한 H(Hue)와 S(Saturation) 성분의 히스토그램을 이용하여 색상을 분석하였다. 제안하는 색상분석 진단모델과 한의학 전문의의 진단 결과를 비교하여 미각별 영역의 이상 유무를 판단하였다. 제안하는 설진 알고리즘으로 판단한 결과 87.5%가 전문의의 분류의 결과 일치함을 확인하였다.

• 대한예방한의학회지
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• 제4권2호
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• pp.72-92
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• 2000

This research has conducted studies on an Oriental medicine-based method of diagnosing of occupational musculoskeletal system diseases. This researcher has searched through existing relevant medical literature. Also, this researcher has worked on a moire topography using moire topography. In this course, this researcher has reached the following conclusion in relation to the possibility of using a moire topography as a diagnosing device of musculoskeletal system diseases under Oriental medicine . 1 The Western medicine outlines its criteria of screening occupational musculoskeletal system diseases as follows A. The occupational musculoskeletal diseases must clearly include one or more of the subjective symptoms characterized by pain, hypoesthesia dysaesthesia, anaesthesia. etc . B, There should be clinically admitted objective observations and diagnosis outlining that the disease concerned shows symptoms such as tenderness, induration. and edema that can appear with occupational musculoskeletal system diseases. dyscinesia should be admitted with the disease concerned, or there should be observations and diagnosis outlining that abnormality exists in electric muscular or nervous diagnosis and examination . C. It should be admitted that prior to the occurrence of symptoms or observations and diagnosis on musculoskeletal system-related diseases, a patient has been engaged in works with conditions requiring improper work posture or work movement. That is, this is an approach whereby they see abnormality in the musculoskeletal system come from material and structural defect, and adjust and control abnormality in the musculoskeletal system and secreta . 2. The Oriental medicines sees that a patient develops the pain of occupational musculoskeletal diseases as he cannot properly activate the flow of his life force and blood thus not only causing formation of lumps in the body and blocking the flow of life force and blood in some parts of the body. Hence, The Oriental medicine focuses on resolving the cause of weakening the flow of life force and blood, instead of taking material approach of correcting structural abnormality Furthermore , Oriental medicine sees that when muscle tension builds up, this presses blood vessels and nerves passing by, triggering circulation dyscrasia and neurological reaction and thus leading to lesion. Thus, instead of taking skeletal or neurophysiological approach. it seeks to fundamentally resolve the cause of the flow of the life force and blood in muscles not being activated. As a result Oriental medicine attributes the main cause of musculoskeletal system diseases to muscle tension and its build-up that stem from an individual's long formed chronicle habit and work environment. This approach considers not only the social structure aspect including companies owners and work environment that the existing methods have looked at, but also individual workers' responsibility and their environmental factors. Hence, this is a step forward method. 3 The diagnosis of musculoskeletal diseases under Oriental medicine is characterized by the fact that an Oriental medicine doctor uses not only photos taken by himself, but also various detection devices to gather information and pass comprehensive judgment on it. Thus, it is the core of diagnosis under Oriental medicine to develop diagnosing devices matching the characteristics of information to be induced and to interpret information so induced from the views of Oriental medicine. Diagnosis using diagnosing devices values the whole state of a patient and formal abnormality alike, and the whole balance and muscular state of a patient serves as the basis of diagnosis. Hence, this method, instead of depending on the information gathered from devices under Western medicine, requires devices that provide information on the whole state of a patient in addition to the local abnormality information that X-ray. CT, etc., can offer. This method sees muscle as the central part of the abnormality in the musculoskeletal system and thus requires diagnosing devices enabling the muscular state. 4. The diagnosing device using moire topography under Oriental medicine has advantages below and can be used for diagnosing musculoskeletal system diseases with industrial workers . First, the device can Provide information on the body in an unbalanced state. and thus identify the imbalance and difference of height in the left and right stature that a patient can not notice at normal times. Second, the device shows the twisting of muscles or induration regions in a contour map. This is not possible with existing shooting machines such as X-ray, CT, etc., thus differentiating itself from existing machines. Third, this device makes it possible for Oriental medicine to take its unique approach to the abnormality in the musculoskeletal system. Oriental medicine sees the state and imbalance state in muscles as major factors in determining the lesion of musculoskeletal system, and the device makes it possible to shoot the state of muscles in detail. In this respect, the device is significant. Fourth, the device has an advantage as non-aggression diagnosing device.

• Nuclear Engineering and Technology
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• 제52권9호
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• pp.2009-2016
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• 2020

A nuclear power plant is a large complex system with tens of thousands of components. To ensure plant safety, the early and accurate diagnosis of abnormal situations is an important factor. To prevent misdiagnosis, operating procedures provide the anticipated symptoms of abnormal situations. While the more severe emergency situations total less than ten cases and can be diagnosed by dozens of key plant parameters, abnormal situations on the other hand include hundreds of cases and a multitude of parameters that should be considered for diagnosis. The tasks required of operators to select the appropriate operating procedure by monitoring large amounts of information within a limited amount of time can burden operators. This paper aims to develop a system that can, in a short time and with high accuracy, select the appropriate operating procedure and sub-procedure in an abnormal situation. Correspondingly, the proposed model has two levels of prediction to determine the procedure level and the detailed cause of an event. Simulations were conducted to evaluate the developed model, with results demonstrating high levels of performance. The model is expected to reduce the workload of operators in abnormal situations by providing the appropriate procedure to ultimately improve plant safety.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.