• 한국인공지능학회지
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• 제12권1호
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• pp.25-29
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• 2024

In this study, research was conducted to predict the probability of cervical cancer occurrence associated with the use of hormonal contraceptives. Cervical cancer is influenced by various environmental factors; however, the human papillomavirus (HPV) is detected in 99% of cases, making it the primary attributed cause. Additionally, although cervical cancer ranks 10th in overall female cancer incidence, it is nearly 100% preventable among known cancers. Early-stage cervical cancer typically presents no symptoms but can be detected early through regular screening. Therefore, routine tests, including cytology, should be conducted annually, as early detection significantly improves the chances of successful treatment. Thus, we employed artificial intelligence technology to forecast the likelihood of developing cervical cancer. We utilized the logistic regression algorithm, a predictive model, through Microsoft Azure. The classification model yielded an accuracy of 80.8%, a precision of 80.2%, a recall rate of 99.0%, and an F1 score of 88.6%. These results indicate that the use of hormonal contraceptives is associated with an increased risk of cervical cancer. Further development of the artificial intelligence program, as studied here, holds promise for reducing mortality rates attributable to cervical cancer.

• 한국산학기술학회논문지
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• 제21권1호
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• pp.477-486
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• 2020

본 연구는 알츠하이머 치매노인을 대상으로 Apolipoprotein E (ApoE) ɛ4의 유무와 치매수준에 따른 우울과 기억력의 상관관계를 분석하고자 하였다. 임상치매척도(clinical dementia rating; CDR)가 0.5점에서 2점 사이인 65세 이상 노인 50명을 대상으로 임상치매척도, 노인용 서울언어학습검사, 레이 복합도형검사, 축약형 노인우울검사를 실시하였고 ApoE 유전자형은 구강상피세포를 채취하여 실시한 유전자 검사를 통해 확인하였다. 자료분석은 맨 휘트니 U 검정, 상관관계 분석을 실시하였다. ApoE ɛ4가 없는 경우에 CDR 1점과 2점에서 우울과 언어적 즉시회상 기억력이 유의한 음의 상관관계가 있었고(p<.05), ApoE ɛ4를 보유한 경우에는 CDR 1점에서 우울과 언어적 즉시회상, 언어적 지연회상 기억력에서 유의한 음의 상관관계가 있었다(p<.05). 우울과 언어적 즉시회상 기억력은 유의한 상관관계가 있었고 ApoE ɛ4를 보유한 경우에는 우울과 언어적 지연회상 기억력에도 유의한 상관관계가 있었다. 따라서 알츠하이머 치매의 예방 및 중재로 시각적인 훈련보다 언어적 기억력훈련이 유용할 것이며 우울치료가 상호보완적으로 도움을 줄 수 있을 것으로 기대한다.

• 수면정신생리
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• 제2권1호
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• pp.13-22
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• 1995

To provide the physician with adequate information to diagnose and treat sleepwalking and sleep terrors, the author reviewed clinical features, epidemiology, causative and precipitating factors, polysomnography, diagnosis, differential diagnosis, and treatment for these disorders. Sleepwalking and sleep terrors have been defined as disorders of arousal that occur early in the night and have their onset during stage 3 or 4 sleep. In both disorders, patients are difficult to arouse, and complete amnesia or minimal recall of the episode is frequent. Genetic, developmental, and psychological factors have been identified as causes of both sleepwalking and sleep terrors. Sleepwalking and sleep terrors typically begin in childhood or early adolescence and are usually outgrown by the end of adolescence. When sleepwalking or sleep terrors have a post-pubertal onset or continue to adulthood, psychopathology is a more significant causative factors. The behavior that occur from deep slow-wave sleep can be painful or dangerous to the individual and/or disturbing to those close to that individual. The assessment of patients suspected of having these conditions requires a thorough medical and sleep history. The most important consideration in managing patients with sleepwalking or sleep terrors episodes is protection from injury.

• 한국전자통신학회논문지
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• 제18권2호
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• pp.373-378
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• 2023

희귀질환은 증상이 전형적이지 않고 진단정보가 부족하여 전문의들조차 증상을 기반으로 질환을 의심하거나 질환명을 떠올리는 데에 어려움을 겪는다. 따라서 증상이 시작한 시점에서부터 정확한 진단을 받기까지 많은 시간 및 비용이 발생하며, 이는 환자의 신체적, 정신적, 경제적 부담을 심각하게 초래한다. 환자의 증상정보를 통해 의심되는 희귀질환을 제시하여 의사의 진단에 활용할 수 있도록, 본 논문에서는 웹 크롤링 및 텍스트마이닝을 활용한 희귀질환 조기 발견 보조시스템을 제안하고 이를 구현한다.

• Korean Journal of Radiology
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• 제23권6호
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• pp.598-614
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• 2022

While ultrasound (US) is considered an important tool for hepatocellular carcinoma (HCC) surveillance, it has limited sensitivity for detecting early-stage HCC. Abbreviated MRI (AMRI) has recently gained popularity owing to better sensitivity in its detection of early-stage HCC than US, while also minimizing the time and cost in comparison to complete contrast-enhanced MRI, as AMRI includes only a few essential sequences tailored for detecting HCC. Currently, three AMRI protocols exist, namely gadoxetic acid-enhanced hepatobiliary-phase AMRI, dynamic contrast-enhanced AMRI, and non-enhanced AMRI. In this study, we discussed the rationale and technical details of AMRI techniques for achieving optimal surveillance performance. The strengths, weaknesses, and current issues of each AMRI protocol were also elucidated. Moreover, we scrutinized previously performed AMRI studies regarding clinical and technical factors. Reporting and recall strategies were discussed while considering the differences in AMRI protocols. A risk-stratified approach for the target population should be taken to maximize the benefits of AMRI and the cost-effectiveness should be considered. In the era of multiple HCC surveillance tools, patients need to be fully informed about their choices for better adherence to a surveillance program.

• 여성건강간호학회지
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• 제30권1호
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• pp.67-78
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• 2024

Purpose: The present study investigated experiences of traumatic perinatal events, the provision of related education, and educational needs of nurses working in the labor and delivery room (LDR). Methods: Nurses working in the LDRs of six institutions and two nurse portal sites were invited to participate in the survey, delivered on paper or online. The data were collected from October 1 to November 25, 2022. Data from 129 nurses were analyzed using frequency, the chi-square test, the Fisher exact test, the t-test, and analysis of variance. Results: Virtually all participants (98.6%) reported having experienced at least one traumatic perinatal event (dystocia, postpartum hemorrhage, neonatal congenital anomalies, severe maternal or neonatal injury, stillbirth, and maternal or neonatal death) while working in the LDR. The most shocking traumatic perinatal event experienced was the maternal or neonatal death (40.3%), but 24.8% of participants did not recall ever receiving education on the topic. About 63% of participants experienced traumatic perinatal events within a year of working in the LDR. The average score for education needs regarding traumatic perinatal events was 3.67±0.37 out of 4, and participants preferred simulation education as the most effective educational method. Conclusion: Since most of the participants had experienced various traumatic perinatal events in the early stages of working in the LDR and expressed a high level of need for education on traumatic perinatal events, it is necessary to provide more effective stimulation education programs in the early period of work in the LDR.

• Radiation Oncology Journal
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• 제38권2호
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• pp.84-92
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• 2020

Patients undergoing radiation therapy for head and neck cancer (HNC) experience significant early and long-term side effects. The likelihood and severity of complications depends on a number of factors, including the total dose of radiation delivered, over what time it was delivered and what parts of the head and neck received radiation. Late side effects include: permanent loss of saliva; osteoradionecrosis; radiation recall myositis, pharyngoesophageal stenosis; dental caries; oral cavity necrosis; fibrosis; impaired wound healing; skin changes and skin cancer; lymphedema; hypothyroidism, hyperparathyroidism, lightheadedness, dizziness and headaches; secondary cancer; and eye, ear, neurological and neck structures damage. Patients who undergo radiotherapy for nasopharyngeal carcinoma tend to suffer from chronic sinusitis. These side effects present difficult challenges to the patients and their caregivers and require life-long strategies to alleviate their deleterious effect on basic life functions and on the quality of life. This review presents these side effects and their management.

• 한국정보통신학회논문지
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• 제26권10호
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• pp.1490-1499
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• 2022

부정맥이란 심장이 불규칙한 리듬이나 비정상적인 심박동수를 갖는 것을 말하며, 뇌졸중, 심정지 등을 유발하거나 사망에도 이를 수 있는 만큼, 조기 진단과 관리가 무엇보다 중요하다. 본 연구에서는 심전도 신호의 QRS 특징 추출에 적합한 CNN과 기존 LSTM의 직전 패턴의 수렴 한계를 해결할 수 있는 BLSTM을 연결한 CNN-BLSTM 결합 모델을 이용한 부정맥 분류 방법을 제안한다. 이를 위해 먼저 전처리 과정을 통해 잡음을 제거한 심전도 신호에서 QRS 특징점을 검출하고 단일 비트 세그먼트를 추출하였다. 이때 데이터의 불균형 문제를 해결하기 위해 GAN 오버샘플링 기법을 적용하였다. 이 후 합성곱 계층을 통해 부정맥 신호의 패턴을 정밀하게 추출하도록 구성하고 이를 BLSTM의 입력으로 사용한 후 매개변수를 학습시키고 검증 데이터로 학습 모델을 평가한 후 부정맥 분류의 정확도를 확인하였다. 제안한 방법의 우수성을 입증하기 위해 MIT-BIH 부정맥 데이터베이스를 이용하여 분류의 정확도, 정밀도, 재현율, F1-score를 비교하였다. 성능평가 결과 각각 99.30%, 98.70%, 97.50%, 98.06%로 우수한 분류율을 나타내는 것을 확인할 수 있었다.

• 대한의용생체공학회:의공학회지
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• 제44권2호
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• pp.139-146
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• 2023

Osteoporosis is a disease in which the risk of bone fractures increases due to a decrease in bone density caused by aging. Osteoporosis is diagnosed by measuring bone density in the total hip, femoral neck, and lumbar spine. To accurately measure bone density in the lumbar spine, the vertebral region must be segmented from the lumbar X-ray image. Deep learning-based automatic spinal segmentation methods can provide fast and precise information about the vertebral region. In this study, we used 695 lumbar spine images as training and test datasets for a deep learning segmentation model. We proposed a lumbar automatic segmentation model, CM-Net, which combines the center point of the spine and the modified U-Net network. As a result, the average Dice Similarity Coefficient(DSC) was 0.974, precision was 0.916, recall was 0.906, accuracy was 0.998, and Area under the Precision-Recall Curve (AUPRC) was 0.912. This study demonstrates a high-performance automatic segmentation model for lumbar X-ray images, which overcomes noise such as spinal fractures and implants. Furthermore, we can perform accurate measurement of bone density on lumbar X-ray images using an automatic segmentation methodology for the spine, which can prevent the risk of compression fractures at an early stage and improve the accuracy and efficiency of osteoporosis diagnosis.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.