신현주 교수
Hyun-Ju Shin
연세대학교 영상의학과 · 의학
연구실 소개
신현주 교수의 연구실은 의료 영상 진단 분야에서 인공지능 기반 영상 분석과 소아 영상 진료의 정밀도 향상을 핵심 목표로 삼고 있습니다. 특히 체질량조절 기반 AI 소프트웨어가 흉부 단층촬영의 진단 속도와 정확도에 미치는 영향을 실증적으로 분석하며, 소아 환자의 영상에서의 병변 탐지 성능 향상에 기여하고 있습니다. 또한 뇌혈류관계 및 산화 스트레스와 관련된 분자 기전(예: LCN2)이 신경세포 사멸에 미치는 영향에 대한 기초 연구를 병행하여, 신경영상 진단의 기초를 다지고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15Whether the utilization of artificial intelligence (AI) during the interpretation of chest radiographs (CXRs) would affect the radiologists' workload is of particular interest. Therefore, this prospective observational study aimed to observe how AI affected the reading times of radiologists in the daily interpretation of CXRs. Radiologists who agreed to have the reading times of their CXR interpretations collected from September to December 2021 were recruited. Reading time was defined as the du
The hippocampal cell death that follows kainic acid (KA)-induced seizures is associated with blood-brain barrier (BBB) leakage and oxidative stress. Lipocalin-2 (LCN2) is an iron-trafficking protein which contributes to both oxidative stress and inflammation. However, LCN2's role in KA-induced hippocampal cell death is not clear. Here, we examine the effect of blocking LCN2 genetically on neuroinflammation and oxidative stress in KA-induced neuronal death. LCN2 deficiency reduced neuronal cell d
Artificial intelligence (AI) applied to pediatric chest radiographs are yet scarce. This study evaluated whether AI-based software developed for adult chest radiographs can be used for pediatric chest radiographs. Pediatric patients (≤ 18 years old) who underwent chest radiographs from March to May 2021 were included retrospectively. An AI-based lesion detection software assessed the presence of nodules, consolidation, fibrosis, atelectasis, cardiomegaly, pleural effusion, pneumothorax, and pneu
Combining CARE kV, reduction of mAs from 240 to 170 mAs and noise reduction by applying SAFIRE strength 3 or 4 reduced the radiation dose by 41.3% without increasing image noise compared with the standard-dose FBP images.
Three acquisitions can be enough for hepatic SWVs in children more than 6 years old regardless of breathing status or hepatic pathology. More acquisitions are recommended for children under the age of 5 years during FB.
Small cyst size (<1 cm) on prenatal US, triangular cord thickening (≥4 mm) and gallbladder mucosal irregularity on postnatal US, and small cyst size (≤2.2 cm) and an invisible distal common bile duct on MRI can discriminate cBA from CC type Ia/b in infancy.
In liver IVIM DWI with multiple <i>b</i>-values in children, there was a positive correlation between hepatic fat and blood volume, and a negative correlation between hepatic stiffness and endovascular blood flow velocity, while diffusion-related parameters were not affected.
The purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (≤5 years old) who underwent abdominal radiograph and ultrasonography (US) for suspicion of intussusception from March 2005 to December 2017 were retrospectively included and divided into control and intussusception groups according to the US results. A YOLOv3-based algorithm was deve
PURPOSE: Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer. MATERIALS AND METHODS: Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because c
Serum TgAbs may interfere with FNA-Tg studies and caution is advised while analyzing FNA-Tg for detection of LNM in patients with PTC.
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