• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.14 no.1
• /
• pp.70-78
• /
• 2010

Objectives : To qualify or enhance the diagnostic ability of students in Oriental Medicine, so called standardized patients are ideal modality, but because it's a man-based method, more convenient tools are required. Computer-based diagnostic ability test program gives effective way for the very purpose. So we made a pilot software evaluating Pattern Identification ability in Oriental Medicine. Methods and Materials : The pilot software was coded with Microsoft's EXCEL VBA. 87 names of Zheng (Symptom Pattern) and 674 names of symptom (including some signs) are adopted from the former standardization works conducted by Korean Institute of Oriental Medicine (KIOM) in 1996. Results : Compared with some manned modalities to test Pattern Identification ability, the test by this software shows superiority in convenience and objectivity. Conclusion : This software is world's first program to perform computer-based evaluation of Pattern Identification in Oriental Medicine, and it gives effective way to complement both written test and manned clinical performance test (CPX).

• Journal of Korea Multimedia Society
• /
• v.23 no.12
• /
• pp.1486-1495
• /
• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.1
• /
• pp.507-515
• /
• 2015

This paper suggests 6 cases of TFA parameters algorithm(Mean, VA, RS, SKEW, UN, EN) to search for the detection of recognition rates regarding breast disease using CAD on ultrasound images. Of the patients who visited a university hospital in Busan city from August 2013 to January 2014, 90 cases of breast ultrasound images based on the findings in breast US and pathology were selected. $50{\times}50$ pixel size ROI was selected from the breast US images. After pre-processing histogram equalization of the acquired test images(negative, benign, malignancy), we calculated results of TFA algorithm using MATLAB. As a result, in the TFA parameters suggested, the disease recognition rates for negative and malignancy was as high as 100%, and negative and benign was approximately 83~96% for the Mean, SKEW, UN, and EN. Therefore, there is the possibility of auto diagnosis as a pre-processing step for a screening test on breast disease. A additional study of the suggested algorithm and the responsibility and reproducibility for various clinical cases will determine the practical CAD and it might be possible to apply this technique to range of ultrasound images.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.10
• /
• pp.887-895
• /
• 2013

Metastatic bone cancer, the cancer which occurred in the various organs and progressively spread to bone, is one of the complications in cancer patients. This cancer is divided into the osteoblast and osteolytic metastasis. Although Computer Tomography(CT) could be an useful tool in diagnosis of bone metastasis, lesions are often missed by the visual inspection and it makes clinicians difficult to detect metastasis earlier. Therefore, in this study, we construct a three-dimensional(3D) volume rendering data from tomography images of the chest CT, and apply a 3D based image processing algorithm to them for detection bone metastasis lesions. Then we perform a three-dimensional visualization of the detected lesions.From our test using 10 clinical cases, we confirmed 94.1% of average sensitivity for osteoblast, and 90.0% of average sensitivity, respectively. Consequently, our findings showed a promising possibility and potential usefulness in diagnosis of metastastic bone cancer.

• Journal of Periodontal and Implant Science
• /
• v.48 no.2
• /
• pp.114-123
• /
• 2018

Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

• The Journal of Korean Academy of Prosthodontics
• /
• v.55 no.4
• /
• pp.403-409
• /
• 2017

In the case of an extraction in the maxillary anterior region, immediate placement of implant-supported fixed prosthesis can be considered as a treatment option. Fewer surgical operations, reduced treatment time, and optimal availability of existing bone are obvious advantages of the method; however, when applied in the continuous maxillary anterior region, inter-implant distance must be carefully considered, as well as accurate diagnosis and treatment planning for predictable outcome. In this case report, immediate placement of two implants in the continuous maxillary anterior along with bone graft following the extraction of root rests, and the restoration of provisional and implant-supported fixed prosthesis on a 63-year-old patient had resulted in both esthetically and functionally satisfactory clinical outcomes.

• The Journal of Korean Academy of Prosthodontics
• /
• v.56 no.2
• /
• pp.166-172
• /
• 2018

In case of the treatment of maxillary anterior teeth, it should be taken into account the proper morphology, arrangement and color satisfying patient's esthetic demands. For this purpose, facial composition, dentofacial composition, dental composition and dentogingival composition should be considered making diagnosis and treatment plan in an esthetic point of view. In adjustable temporary crown state, careful evaluation and correction of the esthetic and functional aspect were performed, and the definite restoration was reproduced using double scan.

• The Journal of Korean Academy of Prosthodontics
• /
• v.55 no.1
• /
• pp.79-87
• /
• 2017

The open bite malocclusion is a common clinical entity and has multifactorial causes. Development of effective treatment plan and management is dependent on proper diagnosis. The skeletal open bite patient requires a coordinated orthodontic and orthognathic surgical approach to achieve stable occlusion, acceptable esthetics, and improved function. But in case of open bite with severely decayed dentition, restoration in the entire dentition is necessary. Using the facial analysis and diagnostic wax-up, the most effective treatment was prosthetic rehabilitation. The provisional restorations were fabricated to satisfy esthetic and functional requirements, which result in the uniformly distributed occlusal force, anterior and canine guidance. The inter-arch relationship, labio-dental harmony, and the soft tissue aspect, which is important to estimate the longevity were evaluated. Definitive restorations of monolithic zirconia were made by replicating provisional restorations by using the latest CAD/CAM technology. They were delivered to the patient and clinical follow-up observation was satisfactory.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.05a
• /
• pp.703-706
• /
• 2004

나선형 CT 혈관촬영에서 획득한 영상의 분석를 통해서 폐색전증이 의심되는 부위를 자동으로 검출하는 방법으로, 연구 대상은 20명의 환자를 대상으로 분석하였으며 CT 검사 후 방사선과 의사가 정상소견을 받은 환자 5명과 폐색전증이 있는 판독소견을 가진 15명을 대상으로 비교 분석하였다. CT 검사하는 동안에 조영제를 투입하면, 폐색전증이 발생한 부위는 조영제 양과 분포가 불균등하여 명암값이 낮게 검출된다. 검출방법으로는 전처리 작업으로 폐영역만을 분할하고, 분할된 폐영역에서 혈관을 찾기 위해 모폴로지기법를 적용하여 세선화(thinning) 작업을 진행한다. 다음 공정으로는 경계선을 찾아 local watershed를 적용하여 혈관을 검출하고, 검출된 혈관내에서 원형모델을 적용하여 모폴로지(morphology)을 통해 국소 부위의 미세한 농도변화를 인지하여 색전이 발생한 영역을 자동검출하였다. 본 논문의 자동검출시스템에서는 색전증이 있는 경우에 true positive의 발생빈도는 case 당 4.5개가 검출되었다. 정상인의 경우에도 혈류의 흐름, 혈류의 분기점, 노이즈로 인한 false positive의 빈도는 case 당 2.6개가 발생하여 전체적으로 false positive는 5.2개가 검출되었다. 본 논문은 false positive의 비율이 높게 검출되었지만 폐영역 CT 검사의 컴퓨터지원진단시스템(computer aided diagnosis)의 향후 연구과제에 방향을 제시할 수 있을 것이라 사료된다.

• The Journal of the Korea Contents Association
• /
• v.15 no.3
• /
• pp.184-191
• /
• 2015

Prostate ultrasound is used to diagnose prostate cancer, BPH, prostatitis and biopsy of prostate cancer to determine the size of prostate. BPH is one of the common disease in elderly men. Prostate is divided into 4 blocks, peripheral zone, central zone, transition zone, anterior fibromuscular stroma. BPH is histologically transition zone urethra accompanying excessive nodular hyperplasia causes a lower urinary tract symptoms(LUTS) caused by urethral closure as causing the hyperplastic nodule characterized finding progressive ambient. Therefore, in this study normal transition zone image for hyperplasia prostate and normal transition zone image is analyzed quantitatively using a computer algorithm. We applied texture features of GLCM to set normal tissue 60 cases and BPH tissue 60cases setting analysis area $50{\times}50pixels$ which was analyzed by comparing the six parameters for each partial image. Consequently, Disease recognition detection efficiency of Autocorrelation, Cluster prominence, entropy, Sum average, parameter were high as 92~98%.This could be confirmed by quantitative image analysis to nodular hyperplasia change transition zone of the prostate. This is expected secondary means to diagnose BPH and the data base will be considered in various prostate examination.