Seoul National University · 医学
Professor Jinwook Choi's research lab specializes in medical image analysis, mobile health systems, and multimodal data integration for clinical decision support. The lab focuses on leveraging deep learning and artificial intelligence to extract meaningful diagnostic information from medical imaging—particularly dental panoramic radiographs—for early detection of conditions like osteoporosis. It also develops innovative mobile clinical information systems that integrate fragmented patient data across heterogeneous sources, enhancing care coordination and accessibility. Additionally, the lab explores advanced video processing techniques, such as depth video up-conversion using hybrid sensor fusion, to improve real-time 3D applications in healthcare and beyond.
Figures are computed from collected data and may differ slightly.
<b>:</b> Dental panoramic radiographs (DPRs) provide information required to potentially evaluate bone density changes through a textural and morphological feature analysis on a mandible. This study aims to evaluate the discriminating performance of deep convolutional neural networks (CNNs), employed with various transfer learning strategies, on the classification of specific features of osteoporosis in DPRs. For objective labeling, we collected a dataset containing 680 images from different pat
Patient clinical data are distributed and often fragmented in heterogeneous systems, and therefore the need for information integration is a key to reliable patient care. Once the patient data are orderly integrated and readily available, the problems in accessing the distributed patient clinical data, the well-known difficulties of adopting a mobile health information system, are resolved. This paper proposes a mobile clinical information system (MobileMed), which integrates the distributed and
Our study demonstrated that the performance of the XGB model using initial information at ED triage for predicting patients in need of critical care outperformed the conventional model with KTAS.
Our proposed approach for biomarker data extraction addresses key limitations regarding data representation and can handle reports prepared in the clinical setting, which often contain incomplete sentences, typographical errors, and inconsistent formatting.
We propose a novel framework for upconversion of depth video resolution in both spatial and time domains considering spatial and temporal coherences. Although the Time-of-Flight (TOF) sensor which is widely used in computer vision fields provides depth video in realtime, it also provides a low resolution and a low frame-rate depth video. We propose a cheaper solution that enhances depth video obtained from a TOF sensor by combining it with a Charge-coupled Device (CCD) camera in 3D contents whic
This paper proposes a novel framework for up-conversion of depth video resolution both in spatial and in time domain. Time-of-flight (TOF) sensors are widely used in computer vision fields. Although TOF sensors provide depth video in real time, there are some problems in a sense that it provides a low resolution and a low frame-rate depth video. We propose a cheaper solution that enhances depth video obtained by TOF sensor by combining it with CCD camera. The proposed method provides high qualit
Seoul National University Hospital strives to move its hospital information system to a whole new level, which enables customized healthcare service and fulfills individual requirements. The current information strategy is being formulated as an initial step of development, promoting the establishment of next-generation hospital information system.
Recent large-scale genome-wide association studies have identified common genetic variations that may contribute to the risk of amyotrophic lateral sclerosis (ALS). However, pinpointing the risk variants in noncoding regions and underlying biological mechanisms remains a major challenge. Here, we constructed a convolutional neural network model with a large-scale GWAS meta-analysis dataset to unravel functional noncoding variants associated with ALS based on their epigenetic features. After filt
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