진광남 교수

OBJECTIVES: We aim ed to evaluate a commercial artificial intelligence (AI) solution on a multicenter cohort of chest radiographs and to compare physicians' ability to detect and localize referable thoracic abnormalities with and without AI assistance. METHODS: In this retrospective diagnostic cohort study, we investigated 6,006 consecutive patients who underwent both chest radiography and CT. We evaluated a commercially available AI solution intended to facilitate the detection of three chest a

OBJECTIVES: The aim of this study was to evaluate a prototype dual-energy computed tomography calcium subtraction algorithm and its impact on luminal visualization in patients with heavily calcified coronary arteries. MATERIALS AND METHODS: Twenty-nine patients (62% male; mean age, 64 ± 7 years) who had undergone dual-energy coronary computed tomography angiography were retrospectively included in this institutional review board-approved, Health Insurance Portability and Accountability Act-compl

Thin-section axial and multiplanar reformation images are helpful in the diagnosis of BPF. Multi-detector row CT can be an initial diagnostic modality of BPF.

Computed tomographic angiography is a reliable preoperative imaging technique for the selection of appropriate legs as candidates for fibular free transfer.

PURPOSE: Lunit INSIGHT CXR (Lunit) is a commercially available deep-learning algorithm-based decision support system for chest radiography (CXR). This retrospective study aimed to evaluate the concordance rate of radiologists and Lunit for thoracic abnormalities in a multicenter health screening cohort. METHODS AND MATERIALS: We retrospectively evaluated the radiology reports and Lunit results for CXR at several health screening centers in August 2020. Lunit was adopted as a clinical decision su

PURPOSE: This study evaluated the performance of a commercially available deep-learning algorithm (DLA) (Insight CXR, Lunit, Seoul, South Korea) for referable thoracic abnormalities on chest X-ray (CXR) using a consecutively collected multicenter health screening cohort. METHODS AND MATERIALS: A consecutive health screening cohort of participants who underwent both CXR and chest computed tomography (CT) within 1 month was retrospectively collected from three institutions' health care clinics (n

Preoperative localization is necessary prior to video assisted thoracoscopic surgery for the detection of small or deeply located lung nodules. We compared the localization ability of a mixture of lipiodol and methylene blue (MLM) (0.6 mL, 1:5) to methylene blue (0.5 mL) in rabbit lungs. CT-guided percutaneous injections were performed in 21 subjects with MLM and methylene blue. We measured the extent of staining on freshly excised lung and evaluated the subjective localization ability with 4 po

The aim of this study was to investigate the association between image characteristics on preoperative chest CT and severe pleural adhesion during surgery in lung cancer patients. We included consecutive 124 patients who underwent lung cancer surgeries. Preoperative chest CT was retrospectively reviewed to assess pleural thickening or calcification, pulmonary calcified nodules, active pulmonary inflammation, extent of emphysema, interstitial pneumonitis, and bronchiectasis in the operated thorax

ABSTRACT: Along with recent developments in deep learning techniques, computer-aided diagnosis (CAD) has been growing rapidly in the medical imaging field. In this work, we evaluate the deep learning-based CAD algorithm (DCAD) for detecting and localizing 3 major thoracic abnormalities visible on chest radiographs (CR) and to compare the performance of physicians with and without the assistance of the algorithm. A subset of 244 subjects (60% abnormal CRs) was evaluated. Abnormal findings include