Journal of the Korea Institute of Information and Communication Engineering
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v.23
no.4
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pp.381-387
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2019
The limitations of applying a variety of artificial intelligence to the medical community are, first, subjective views, extensive interpreters and physical fatigue in interpreting the image of an interpreter's illness. And there are questions about how long it takes to collect annotated data sets for each illness and whether to get sufficient training data without compromising the performance of the developed deep learning algorithm. In this paper, when collecting basic images based on acne data sets, the selection criteria and collection procedures are described, and a model is proposed to classify data into small loss rates (5.46%) and high accuracy (96.26%) in the sequential structure. The performance of the proposed model is compared and verified through a comparative experiment with the model provided by Keras. Similar phenomena are expected to be applied to the field of medical and skin care by applying them to the acne classification model proposed in this paper in the future.
KIPS Transactions on Computer and Communication Systems
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v.10
no.11
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pp.305-310
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2021
Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changing to web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CT images and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fully-convolutional network-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imaging field. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI web application service. We expect that our proposed AI web application service will be utilized for screening test.
To know which parameters were acceptable for achieving lowest radiation exposure to the patients and highest image quality at the diagnostic X-ray radiography, we measured the patient radiation dose and image quality in transmitted PACS (Picture Archiving and Communication System) at variable combinations of the added filters. As a result, the Dose Area Product (DAP: $mGy{\cdot}cm^2$) and Entrance Surface Doses (ESDs: $mGy$) was lowest at 1 mmAl + 0.2 mmCu and highest at 0 mmAl. The histogram of the image quality by transmitted PACS was not significantly different at variable combinations of exposure parameters on the MATLAB. In conclusion, this study can be helpful for expecting radiation dose-exposure and control exposure parameters for the diagnostic X-ray radiography.
Contemper nursing literature place much importance on human- centered and individualized care. Nursing research has related stress during hospitalization of adolescent patients to adaptation to a new environment, isolation from friends, limitation due to illness, over protection of parents and communication with member of the medical team. The investigator conducted this study in the hope that an understanding of adolescents responses to hospitalization, their perceptions, the kinds and levels of stress, and the relationships between stressors and individual characteristics would contribute to the improvement of adolescent patient care. The objective of the study was to obtain informations related to the adolescents psychological stress experience during hospitalization, specifically stress from interpersonal relationships and communication, isolation from the family, social or economic problems, illness and from the treatment environment and nursing care. An interview schedule adopted from Holmes and Rahe's Social Readjustment Rating Scale and selected items from Voicer's instrument on stress-producing events was used with 120 adolescent inpatients aged 13 to 18 years three general hospitals in Seoul during Aug. 10, to Sep. 30, 1975. 1. The sample consisted of 66 male and 54 female patients. Sixty-six percent were late adolescents, aged 16 to 18 years: 4% were early adolescents, aged 13 to 15 years. The primary cause for hospitalization was for orthopedic problems (35.8%). More than half of these (54.4%) were due to injury or accident. 2. Stress eclated to illness revealed the highest score (4.97), followed by stress related to treatment environment and nursing care (4.34) , isolation from family and social or economic problems (4.01) and interpersonal relationships and communication (3.96). 3. The perceived indifference of doctors and nurses was a serious cause of stress (mean=4.83). Fellow patients and visitors caused least stress (mean=2.06). 4. Discontinuation of education or unemployment were major stressful events (mean=4.71). Least stressful was isolation from the family (mean=3.47). 5. More than 94% of the respondents expressed fears related to body image (mean=4.97) 6. Within the category of treatment environment and nursing care, items related to restrictions because of treatment, discomfort because of treatment, inadequate explanation from nurses about procedures were rated as severe stress events (mean=4.6). Items related to the ward environment and to having a relative stay with them were seen by the group as less serious events (mean=3.7). 7. Stress related to interpersonal relationships and communication was correlated positively with female patients and those preferring passive activities. (P〈0.05) 8. Stress related to family problems was positively related to female and early adolescent patients (P< 0.05). Stress related to social problems was positively , elated to students and those preferring active pursuits (P< 0.05). 9. There were no correlation between the high stress related to disease and any of the characteristic items. (P> 0.05) 10. Stress related to treatment environment and nursing care was positively related of early adolescent and female and student patients. (P< 0.05) This group of hospitalized adolescents reported high level of stress related to treatment environment and nursing care, due to lack of consideration of normal growth and development and individual characteristics. The findings have important implications for the planning of effective, individualized, comprehensive nursing care of adolescents during hospitalization.
Today each hospital is trend that change rapidly by up to date, digitization and introducing newest medical treatment equipment. So, we introduce new CR system and supplement film system's shortcoming and PACS, EMR, RTP system's network that is using in hospital harmoniously and accomplish quality improvement of medical treatment and service elevation about business efficiency enlargement and patient Accordingly, we wish to introduce our case that integrate reflex that happen with radiation oncology here upon to PACS using CR system and estimate the availability. We measured that is Gantry, Collimator Star Shot, Light vs. Radiation, HDR QA(Dwell position accuracy) with Medical LINAC(MEVATRON-MX) Then, PACS was implemented on the digital images on the monitor that can be confirmed through the QA. Also, for cooperation with OCS system that is using from present source and impose code that need in treatment in each treatment, did so that Order that connect to network, input to CR may appear, did so that can solve support data mistake (active Pinacle's case supports DICOM3 file from present source but PACS does not support DICOM3 files.) of Pinacle and PACS that is Planning System and look at Planning premier in PACS. All image and data constructed integration to PACS as can refer and conduct premier in Hospital anywhere using CR system. Use Dosimetry IP in Filmless environment and QA's trial such as Light/Radition field size correspondence, gantry rotation axis' accuracy, collimator rotation axis' accuracy, brachy therapy's Dwell position check is available. Business efficiency by decrease and so on of unnecessary human strength consumption was augmented accordingly with session shortening as that integrate premier that is neted with radiation oncology using CR system to PACS. and for the future patient information security is essential.
KIPS Transactions on Computer and Communication Systems
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v.10
no.10
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pp.285-290
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2021
In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.
Seongwon Chae;Jae-Hyun Jo;Ye-Eun Park;Jin-Hyoung, Jeong;Sung Jin Kim;Ahnryul Choi
The Journal of Korea Institute of Information, Electronics, and Communication Technology
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v.16
no.5
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pp.331-337
/
2023
In this paper, we propose a deep learning model to detect lumbar 3 (L3) CT images to determine the occurrence and degree of sarcopenia. In addition, we would like to propose an optimization technique that uses oversampling ratio and class weight as design parameters to address the problem of performance degradation due to data imbalance between L3 level and non-L3 level portions of CT data. In order to train and test the model, a total of 150 whole-body CT images of 104 prostate cancer patients and 46 bladder cancer patients who visited Gangneung Asan Medical Center were used. The deep learning model used ResNet50, and the design parameters of the optimization technique were selected as six types of model hyperparameters, data augmentation ratio, and class weight. It was confirmed that the proposed optimization-based L3 level extraction model reduced the median L3 error by about 1.0 slices compared to the control model (a model that optimized only 5 types of hyperparameters). Through the results of this study, accurate L3 slice detection was possible, and additionally, we were able to present the possibility of effectively solving the data imbalance problem through oversampling through data augmentation and class weight adjustment.
Journal of the Institute of Electronics and Information Engineers
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v.50
no.3
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pp.102-112
/
2013
In this paper, two step processing method of contour extraction and complement which contain hexagonal shape for low contrast and noisy images is proposed. This method is based on the combination of Laplacian-Gaussian filter and an idea of filters which are dependent on the shape. At the first step, an algorithm which has six masks as its extractors to extract the hexagonal edges especially in the corners is used. Here, two tricorn filters are used to detect the tricorn joints of hexagons and other four masks are used to enhance the line segments of hexagonal edges. As a natural image, a corneal endothelial cell image which usually has regular hexagonal form is selected. The edge extraction of hexagonal shapes in corneal endothelial cell is important for clinical diagnosis. The proposed algorithm and other conventional methods are applied to noisy hexagonal images to evaluate each efficiency. As a result, this proposed algorithm shows a robustness against noises and better detection ability in the aspects of the output signal to noise ratio, the edge coincidence ratio and the extraction accuracy factor as compared with other conventional methods. At the second step, the lacking part of the thinned image by an energy minimum algorithm is complemented, and then the area and distribution of cells which give necessary information for medical diagnosis are computed.
The aesthetics of current dermatology clinics are the main space of dermatology, which is gradually becoming an area of specialty. Paths requiring spatial transition due to the expansion of treatment field in dermatology clinics are currently emerging, meaning that the duty to provide medical and aesthetic environment with high spatial connection must be accompanied. Contrary to past clinics where only functional aspects were emphasized, current clinics require a differentiated environment that considers both aspects of function and aesthetics, centered on the patient. The purpose of this research in this perspective is to study the efficient connection between dermatology and aesthetics, while also analyzing identity factors to indicate design factors differentiated from other functional spaces, to apply them as preliminary data for the planning of dermatology clinics. Based on the above, six dermatology clinics located in Seoul were designated for case studies through field studies. Overall, Aesthetics connected to dermatology clinics legally must have separate business registrations and have alternative entrance ways. Currently however, there were many cases where entrance, waiting, reception and receipt were not separated. there were efforts to partially display identities when analyzing design factors that formed the identity of dermatology clinics. However, there were insufficient cases where visual communication factors such as a clinic's spatial identity, logo, signing system, and applied products were integrated into a coherent theme. At this point when dermatology clinics are becoming brands, all fields must merge for integrated identity effects that go beyond the boundaries of contemporary H.I, to clearly display their identity with the clinics' professional image and consistent concept.
This paper suggests techniques to enhance coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, we choose new approximation coefficients using a non-linear approximation of luminance term. This boosts the fidelity. Our experiment employing the above methods shows enhancement in the coding time more than two times over traditional coding methods and shows improvement in PSNR value by about 1-3dB at the same compression rate.
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