Ju Gang Nam
서울대학교 의과대학 · 의학
Ju Gang Nam 교수의 연구실은 흉부 단층촬영 및 흉부 X선 영상에 기반한 인공지능 기반 진단 알고리즘 개발에 주력하고 있습니다. 특히 악성 폐결절 탐지, 흉부 레이저 영상에서의 다중 이상소견 진단, 만성 폐질환 환자의 생존 예측 모델링 등 임상적 실용성을 고려한 AI 기반 영상진단 기술을 연구하고 있습니다. 최근에는 실생활 건강검진 환자를 대상으로 한 프랙티컬 임상 시험을 통해 AI 도구의 임상적 유용성을 입증하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Purpose To develop and validate a deep learning-based automatic detection algorithm (DLAD) for malignant pulmonary nodules on chest radiographs and to compare its performance with physicians including thoracic radiologists. Materials and Methods For this retrospective study, DLAD was developed by using 43 292 chest radiographs (normal radiograph-to-nodule radiograph ratio, 34 067:9225) in 34 676 patients (healthy-to-nodule ratio, 30 784:3892; 19 230 men [mean age, 52.8 years; age range, 18-99 ye
Background The impact of artificial intelligence (AI)-based computer-aided detection (CAD) software has not been prospectively explored in real-world populations. Purpose To investigate whether commercial AI-based CAD software could improve the detection rate of actionable lung nodules on chest radiographs in participants undergoing health checkups. Materials and Methods In this single-center, pragmatic, open-label randomized controlled trial, participants who underwent chest radiography between
We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs, and to evaluate its impact in diagnostic accuracy, timeliness of reporting and workflow efficacy.DLAD-10 was trained with 146 717 radiographs from 108 053 patients using a ResNet34-based neural network with lesion-specific channels for 10 common radiological abnormalities (pneumothorax, mediastinal widening, pneumoperitoneum, nodule/mass, consolidation, pleural effusion, linear atelect
The CS-SENSE mDixon-GRE HBP sequence provided comparable overall image quality and non-inferior solid FFL detectability compared with the standard mDixon-GRE sequence, with reduced acquisition time.
Preoperative MDCT features of IMCCs can help differentiate the SD and LD types and predict patient prognosis.
Background Preexisting indexes for predicting the prognosis of chronic obstructive pulmonary disease (COPD) do not use radiologic information and are impractical because they involve complex history assessments or exercise tests. Purpose To develop and to validate a deep learning-based survival prediction model in patients with COPD (DLSP) using chest radiographs, in addition to other clinical factors. Materials and Methods In this retrospective study, data from patients with COPD who underwent
A deep learning-based nodule detection algorithm showed excellent detection performance of lung cancers that were not reported on chest radiographs during routine practice and significantly reduced reading errors when used as a second reader.<i>Supplemental material is available for this article.</i>© RSNA, 2020See also commentary by White in this issue.
Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analy
The dynamic contrast-enhanced parameter of rate transfer constant from the fixed T1 acted as a preferable marker to differentiate true progression from pseudoprogression.
