공성혜 교수
Sung Hye Kong
서울대학교 · 의학
연구실 소개
공성혜 교수의 연구실은 갑상선암 및 골다공로시스를 중심으로 한 임상 연구를 수행하며, 특히 저위험도 피부성 갑상선 미소암에서 수술과 활성적 관찰 간의 삶의 질 비교, 골다공로시스의 정밀 예측 모델 개발, 그리고 골다공로시스와 인지 기능 저하 간의 연관성에 대한 장기적 관찰 연구를 진행하고 있습니다. 특히 머신러닝 기반의 골절 위험 예측 알고리즘과 TBS(트라베큘라 뼈 점수)의 임상적 활용 최적화에 초점을 맞추고 있습니다. 이는 환자의 개인화된 치료 결정과 예방 전략 수립에 기여하고자 하는 연구 목표를 가지고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15<b><i>Background:</i></b> In this ongoing multicenter prospective cohort study on active surveillance (AS) in low-risk papillary thyroid microcarcinoma (PTMC), we aimed to compare the quality of life (QoL) of participants based on their choice of treatment, that is, AS or immediate surgery (OP). <b><i>Methods:</i></b> QoL of 203 participants who chose AS and 192 participants who underwent OP was evaluated using a thyroid-specific QoL questionnaire at diagnosis and during follow-up (median 8 mont
The prediction of fracture risk in osteoporotic patients has been a topic of interest for decades, and models have been developed for the accurate prediction of fracture, including the fracture risk assessment tool (FRAX). As machine-learning methodologies have recently emerged as a potential model for medical prediction tools, we aimed to develop a novel fracture prediction model using machine-learning methods in a prospective community-based cohort. In this study, 2227 participants (1257 femal
Daily vitamin D dose of 800 to 1,000 IU was the most probable way to reduce the fracture and fall risk. Further studies designed with various regimens and targeted vitamin D levels are required to elucidate the benefits of vitamin D supplements.
DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.
We evaluated whether metabolic factors were associated with cognitive decline, compared to baseline cognitive function, among geriatric population. The present study evaluated data from an ongoing prospective community-based Korean cohort study. Among 1,387 participants who were >65 years old, 422 participants were evaluated using the Korean mini-mental status examination (K-MMSE) at the baseline and follow-up examinations. The mean age at the baseline was 69.3 ± 2.9 years, and 222 participants
The trabecular bone score (TBS) was introduced as an indirect index of trabecular microarchitecture, complementary to bone mineral density (BMD), and is derived using the same dual energy X-ray absorptiometry images. Recently, it has been approved for clinical use in Korea. Therefore, we conducted a comprehensive review to optimize the use of TBS in clinical practice. The TBS is an independent predictor of osteoporotic fractures in postmenopausal women and men aged >50 years. The TBS is potentia
In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar to that in other research fields. This review aimed to provide an overview of the latest studies using machine learning to address issues, mainly focusing on osteoporosis and fractures. Machine learning models for diagnosing and classifying osteoporo
Dapagliflozin treatment did not affect systemic endothelial function or renal injury markers except <i>N</i>-acetyl-beta-D-glucosaminidase.
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