Seoul National University · Medicine
Professor Sung Hye Kong's research lab specializes in osteoporosis and musculoskeletal health, with a strong focus on improving fracture risk prediction through advanced data-driven methodologies. The lab integrates clinical data, medical imaging, and machine learning to develop personalized prediction models, particularly using tools like FRAX, TBS (trabecular bone score), and deep learning algorithms such as DeepSurv. Research also explores the role of vitamin D in fracture and fall prevention, emphasizing optimal dosing strategies. The lab is at the forefront of applying artificial intelligence to enhance diagnostic accuracy and clinical decision-making in osteoporosis.
Figures are computed from collected data and may differ slightly.
<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.
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.
We identified key proteins differentially expressed between PC and PA using proteomic analyses of parathyroid neoplasms. These findings may help to diagnose PC accurately and elucidate potential therapeutic targets.
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