Woo Young Kang
Korea University · Medicine
About the Lab
Professor Woo Young Kang's research lab specializes in medical imaging and geriatric health, focusing on the application of artificial intelligence—particularly deep learning—in diagnostic imaging for aging populations and musculoskeletal disorders. The lab conducts population-based studies on frailty and geriatric syndromes in rural communities, while also advancing automated, AI-driven quantitative imaging techniques such as deep learning-based bone mineral density (BMD) measurement from routine CT scans. Their work bridges clinical geriatrics with cutting-edge medical image analysis, aiming to improve early detection of osteoporosis and spinal conditions through non-invasive, opportunistic screening methods. The lab also explores smart materials and structural actuation using piezoelectric polymers, demonstrating interdisciplinary innovation in biomedical engineering.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Frailty has been previously studied in Western countries and the urban Korean population; however, the burden of frailty and geriatric conditions in the aging populations of rural Korean communities had not yet been determined. Thus, we established a population-based prospective study of adults aged ≥ 65 years residing in rural communities of Korea between October 2014 and December 2014. All participants underwent comprehensive geriatric assessment that encompassed the assessment of cognitive an
To evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 112 routine clinical CT scans from 84 patients who underwent either chest (N:39), lumbar spine (N:34), or abdominal CT (N:39) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-Q
This retrospective study examined the diagnostic efficacy of automated deep learning-based bone mineral density (DL-BMD) measurements for osteoporosis screening using 422 CT datasets from four vendors in two medical centers, encompassing 159 chest, 156 abdominal, and 107 lumbar spine datasets. DL-BMD values on L1 and L2 vertebral bodies were compared with manual BMD (m-BMD) measurements using Pearson's correlation and intraclass correlation coefficients. Strong agreement was found between m-BMD
Approximately 4.8% of the patients who underwent ESI with dexamethasone experienced minor and transient systemic effects. These effects were more common in patients who had undergone a previous spine surgery or received a cervical ESI.
Using the fact that polyvinylidene fluoride polymer (PVDF) has the anisotropic property, the experimental and numerical investigations were performed to study the static behavior of lasted composite plate when the PVDF was used as a continuous actuator. In this way, it was confirmed that not only the directionality of the composite material, but also the directionality of the PVDF could influence the deformation of the structure. With combination of the different layer angles of both composite a
Background Both multidetector computed tomography (MDCT) and magnetic resonance imaging (MRI) are used for assessment of lumbar foraminal stenosis (LFS). Therefore, it is relevant to assess agreement between these imaging modalities. Purpose To determine intermodality, inter-, and intra-observer agreement for assessment of LFS on MDCT and MRI. Material and Methods A total of 120 foramina in 20 patients who visited our institution in January and February 2014 were evaluated by six radiologists wi
ABSTRACT: In the pubertal period, bone age advances rapidly in conjunction with growth spurts. Precise bone-age assessments in this period are important, but results from the hand and elbow can be different. We aimed to compare the bone age between the hand and elbow around puberty onset and to elucidate the chronological age confirming puberty onset according to elbow-based bone age.A total of 211 peripubertal subjects (127 boys and 84 girls) who underwent hand and elbow radiographs within 2 mo
Opportunistic osteoporosis screening using deep learning (DL) analysis of low-dose chest CT (LDCT) scans is a potentially promising approach for the early diagnosis of this condition. We explored bone mineral density (BMD) profiles across all adult ages and prevalence of osteoporosis using LDCT with DL in a Korean population. This retrospective study included 1915 participants from two hospitals who underwent LDCT during general health checkups between 2018 and 2021. Trabecular volumetric BMD of
OBJECTIVES: This study aims to assess the limitations of the height loss ratio (HLR) method and introduce a new approach that integrates a deep learning (DL) model to enhance vertebral compression fracture (VCF) detection performance. METHODS: We conducted a retrospective study on 589 patients with chronic VCFs. We compared four different methods: HLR-only, DL-only, a combination of HLR and DL for positive VCF, and a combination of HLR and DL for negative VCF. The models were evaluated using dic
Research Areas
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