• Asian Pacific Journal of Cancer Prevention
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• 제13권4호
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• pp.1583-1588
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• 2012

Fine needle aspiration (FNA) cytology is well accepted as a safe, reliable, minimal invasive and cost-effective method for diagnosis of salivary gland lesions. This study evaluated the accuracy and diagnostic performance of FNA cytology in Thailand. A consecutive series of 290 samples from 246 patients during January 2001-December 2009 were evaluated from the archive of the Anatomical Pathology Department of our institution and 133 specimens were verified by histopathologic diagnoses, obtained with material from surgical excision or biopsy. Cytologic diagnoses classified as unsatisfactory, benign, suspicious for malignancy and malignant were compared with the histopathological findings. Among the 133 satisfactory specimens, the anatomic sites were 70 (52.6%) parotid glands and 63 (47.4 %) submandibular glands. FNA cytological diagnoses showed benign lesions in 119 cases (89.5 %), suspicious for malignancy in 3 cases (2.2 %) and malignant in 11 cases (8.3%). From the subsequent histopathologic diagnoses, 3/133 cases of benign cytology turned out to be malignant lesions, the false negative rate being 2.2 % and 1/133 case of malignant cytology turned out to be a benign lesion, giving a false positive rate was 0.8%. The overall accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 97.0% (95% CI, 70.6%-99.4%), 81.3% (95% CI, 54.4%-96.0%), 99.1% (95% CI, 95.4%-100%), 92.9% (95% CI, 66.1%-99.8), 97.5% (95% CI, 92.8%-99.5%), respectively. This study indicated that FNA cytology of salivary gland is a reliable and highly accurate diagnostic method for diagnosis of salivary gland lesions. It not only provides preoperative diagnosis for therapeutic management but also can prevent unnecessary surgery.

• Asian Pacific Journal of Cancer Prevention
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• 제15권13호
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• pp.5245-5247
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• 2014

Background: Helicobacter pylori (H.pylori) is one of the most important causes of dyspepsia and gastric cancer and diagnosis can be made by invasive or non-invasive methods. The Atlas Helicobacter pylori antigen test is a new rapid non-invasive method which is simple to conduct. The aim of this study was to determine its sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. Materials and Methods: This prospective study was conducted between July 2012 and December 2013. Stool samples of 59 dyspeptic patients who underwent upper endoscopy were evaluated for H. pylori stool antigen. Results: From the 59 patients who participated in this study, there were 36 (61%) males and 23 (39%) females. H. pylori was diagnosed in 24 (40.7%) gastric biopsies, 22 (91.7 %) of these being positive for the Atlas H. pylori antigen test. The sensitivity, specificity, PPV, NPV and accuracy were 91.7%, 100%, 100%, 94.6% and 96.6% respectively. Conclusions: The Atlas H. pylori antigen test is a new non-invasive method which is simple to perform and avails reliable results in a few minutes. Thus it can be the best option for the diagnosis of H. pylori infection due to its high sensitivity and specificity.

• Journal of Korean Neurosurgical Society
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• 제52권6호
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• pp.528-533
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• 2012

Objective : In the pediatric population the skull has not yet undergone ossification and it is assumed that the diagnostic rate of skull fractures by simple X-rays are lower than that of adults. It has been recently proposed that the diagnostic rates of skull fractures by 3-dimensional computer tomography (3D-CT) are higher than simple X-rays. The authors therefore attempted to compare the diagnostic rates of pediatric skull fractures by simple X-rays and 3D-CTs with respect to the type of fracture. Methods : One-hundred patients aged less than 12 years who visited the Emergency Center for cranial injury were subject to simple X-rays and 3D-CTs. The type and location of the fractures were compared and Kappa statistical analysis and the t-test were conducted. Results : Among the 100 pediatric patients, 65 were male and 35 were female. The mean age was $50{\pm}45$ months. 63 patients had simple skull fractures and 22 had complex fractures, and the types of fractures were linear fractures in 74, diastatic fractures 15, depressed fractures in 10, penetrating fracture in 1, and greenstick fractures in 3 patients. Statistical difference was observed for the predictive value of simple skull fractures' diagnostic rate depending on the method for diagnosis. A significant difference of the Kappa value was noted in the diagnosis of depressed skull fractures and diastatic skull fractures. Conclusion : In the majority of pediatric skull fractures, 3D-CT showed superior diagnosis rates compared to simple skull X-rays and therefore 3D-CT is recommended whenever skull fractures are suspected. This is especially true for depressed skull fractures and diastatic skull fractures.

• 한국엔터테인먼트산업학회논문지
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• 제15권1호
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• pp.181-189
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• 2021

본 연구는 알츠하이머 치매 환자의 MMSE-K, SGDS, NPI-Q, BADL IADL CDR 평가도구 결과를 분석하여 치매평가 도구들 간의 상관관계와 치매중등도 판정에 미치는 영향정도를 확인하고자 하였다. 본 연구는 일개 병원의 알츠하이머대상자의 치매검사자료인 2차 자료를 분석한 서술적 조사 연구이다. 본 연구는 2015년 1월부터 2017년 4월까지 일개 치매검진병원에서 등록 관리되고 있는 알츠하이머 치매 환자 총 617명의 자료를 분석하였으며, 수집된 자료는 SPSS 25.0 통계 프로그램을 이용하여 분석하였다. 치매평가도구 중 치매중등도 판정에 영향력이 큰 유의한 도구로는 CDR, ADL, MMSE-K, SGDS 도구이며, 유의한 영향을 미치지 못하는 도구로는 NPI-Q, IADL로 나타났다.

• Korean Journal of Radiology
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• 제22권4호
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• 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.

• Korean Journal of Radiology
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• 제20권1호
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• pp.158-165
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• 2019

Objective: Recent studies demonstrated that core needle biopsy (CNB) can effectively reduce the possibility of inconclusive results and prevent unnecessary diagnostic surgery. However, the effectiveness of CNB in patients with suspicious thyroid nodules has not been fully evaluated. This prospective study aimed to determine the potential of CNB to assess thyroid nodules with suspicious ultrasound (US) features. Materials and Methods: Patients undergoing CNB for thyroid nodules with suspicious features on US were enrolled between May and August 2016. Diagnostic performance and the incidence of non-diagnostic results, inconclusive results, conclusive results, malignancy, unnecessary surgery, and complications were analyzed. Subgroup analysis according to nodule size was performed. The risk factors associated with inconclusive results were evaluated using multivariate logistic regression analysis. Results: A total of 93 patients (102 thyroid nodules) were evaluated. All samples obtained from CNB were adequate for diagnosis. Inconclusive results were seen in 12.7% of cases. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for diagnosis of malignancy were 93.8%, 100%, 100%, 78.9%, and 95%, respectively. None of the patients underwent unnecessary surgery. The diagnostic performance was not significantly different according to nodule size. On multivariate logistic regression analysis, larger nodule size and shorter needle length were independent risk factors associated with inconclusive results. Conclusion: Samples obtained by CNB were sufficient for diagnosis in all cases and resulted in high diagnostic values and conclusive results in the evaluation of suspicious thyroid nodules. These findings indicated that CNB is a promising diagnostic tool for suspicious thyroid nodules.

• 한국융합학회논문지
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• 제11권11호
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• pp.47-53
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• 2020

최근 모니터링 및 예측 시스템을 이용하여 사전에 결함을 발견하고 이를 경고하는 시스템이 활발히 연구되고 있다. 차량 안전 관리에 있어서도 예측 결함 분석 기술을 적용하여 자동차 휠 베어링의 고장 유무 및 고장 유형을 조기에 경고하는 시스템이 필요하다. 본 논문에서는 휠 베어링과 결합 된 센서 모듈과 각 센서 모듈에서 차량 가속 정보 및 진동 정보를 수집, 저장 및 분석하는 진단 시스템을 제시하였다. 제안된 센서 모듈은 저비용으로 차량의 휠 베어링 상태를 모니터링하며, 이렇게 수집된 데이터를 활용하여 진단 및 고장 예측 기능을 수행하는 방안을 연구하였다. 개발된 센서 모듈과 예측 분석 시스템은 가진 테스트 장비 및 실제 차량을 이용하여 테스트하고 그 유효성을 평가하였다.

• Asia pacific journal of information systems
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• 제25권4호
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• pp.626-641
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• 2015

Conventional case-based reasoning (CBR) does not perform efficiently for high-volume datasets because of case retrieval time. To overcome this problem, previous research suggested clustering a case base into several small groups and retrieving neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performance than the conventional CBR. This paper proposes a new case-based reasoning method called the clustering-merging CBR (CM-CBR). The CM-CBR method dynamically indexes a search pool to retrieve neighbors considering the distance between a target case and the centroid of a corresponding cluster. This method is applied to three real-life medical datasets. Results show that the proposed CM-CBR method produces similar or better predictive performance than the conventional CBR and clustering-CBR methods in numerous cases with significantly less computational cost.

• 제어로봇시스템학회논문지
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• 제6권4호
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• pp.254-260
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• 2000

This paper deals with the development of fault tolerant control for a nonlinear boiler system with noise and disturbance. The MCMBPC(Multivariable Constrained Model Based Predictive Control) is adopted for the control of the specific boiler turbin model. The fault detection and diagnosis are accomplished with the Kalman filter and two bias estimators. Once a fault is detected, two Bias estimators are driven to estimate the fault and to discriminate Process fault and sensor fault. In this paper, a fault tolerant control scheme combining MCMBPC with a fault compensation method based on the bias estimator is proposed. The proposed scheme has been applied to the nonlinear boiler system and shown a satisfactory performance through some simulations.